Self Study Report
Department of Mathematics and Statistics
Approved by the Faculty of the Department of Mathematics and Statistics:
March 6, 2014
Self Study Report ...................................................................................................................................... 1
Department of Mathematics and Statistics................................................................................................ 1
1
Where We Are Now ....................................................................................................................... 5
.1.a
Quality of Undergraduate Students......................................................................................... 5
.1.a.1
Scholarship Support ........................................................................................................ 6
.1.a.2
Student Success and Satisfaction .................................................................................... 6
.1.a.2.1 Learning Outcomes ...................................................................................................... 6
.1.a.2.2 Recruitment Rates ........................................................................................................ 7
.1.a.2.3 Retention Rates and Graduation Rates ........................................................................ 7
.1.a.2.4 Placement and Acceptance into Advanced Degree Programs ..................................... 7
.1.a.2.5 Ethnic and Gender Diversity ....................................................................................... 8
.1.a.2.6 Level of Financial Need............................................................................................... 8
.1.a.2.7 Student Surveys ........................................................................................................... 8
.1.a.2.8 Curriculum Quality ...................................................................................................... 8
.1.a.2.8.a Syllabi, Degree Requirements, and Advisement ................................................... 9
.1.a.2.8.b List of Courses ...................................................................................................... 9
.1.a.2.9 Contribution to the Core Curriculum and General Education Outcomes .................... 9
.1.a.3
Signature Experiences ................................................................................................... 10
.1.a.3.1 Research Practica ....................................................................................................... 10
.1.a.3.2 Urban Service Learning Programs............................................................................. 10
.1.a.3.3 Internships.................................................................................................................. 10
.1.a.3.4 Study Abroad ............................................................................................................. 10
.1.a.4
Honors College .............................................................................................................. 10
.1.a.4.1 Honors Courses and Honors Add-ons........................................................................ 10
.1.a.4.2 Honors Faculty Fellows ............................................................................................. 10
.1.a.4.3 Honors Students in the Major .....................................................................................11
.1.a.4.4 Students Participating in the GSU Research Undergraduate Conference ..................11
.1.a.5
Undergraduate Programs within the GSU Context ........................................................11
.1.b
Quality of Graduate Students Attracted to the Program ......................................................11
.1.b.1
Expanding Support for Graduate Programs ...................................................................11
.1.b.1.1 Total Numbers of Graduate Students by degree program, and concentration. ...........11
.1.b.1.2 Percentage of Graduate Students Compared to Total Number of Students in the
Department .............................................................................................................................. 12
.1.b.1.3 Graduate Student Financial Support .......................................................................... 12
.1.b.1.4 Ratio of Graduate Students to Faculty....................................................................... 12
.1.b.2
Data on Professional Degree Programs ......................................................................... 12
.1.b.3
Student Success and Satisfaction .................................................................................. 12
.1.b.3.1 Learning Outcomes ................................................................................................... 12
.1.b.3.2 Recruitment Rates, Admission Requirements and procedures, and advisement. ...... 13
.1.b.3.3 Retention Rates, Graduation Rates, and Output Quality Metrics .............................. 14
.1.b.3.4 Placement Rates......................................................................................................... 14
.1.b.3.5 Ethnic and Gender Diversity ..................................................................................... 14
.1.b.3.6 Level of Financial need ............................................................................................. 15
.1.b.3.7 Student Surveys ......................................................................................................... 15
.1.b.3.8 Student Publications and Presentations ..................................................................... 15
.1.b.3.9 Student Outcomes After Graduation ......................................................................... 15
.1.c
Research in the Department ................................................................................................ 15
.1.c.1
Success of the Department's Research Culture ............................................................. 15
.1.c.1.1 2CI Hires .................................................................................................................... 15
.1.c.1.2 Levels of External and Internal Funding ................................................................... 15
.1.c.1.3 National/International Rankings of the Unit .............................................................. 15
.1.c.1.4 Research Productivity that Furthers the Strategic Goals of the University ............... 16
.1.c.1.4.a Citations ............................................................................................................... 16
.1.c.1.5 Success in Recruitment and Retention of Top Faculty in the Field ........................... 16
.1.c.1.6 Faculty Development ................................................................................................. 16
.1.c.2
Faculty Partnerships and Professional Service .............................................................. 17
.1.c.2.1 Participation in Research Centers and Clusters ......................................................... 17
.1.c.2.2 Collaborations ............................................................................................................ 17
.1.c.2.3 Evidence of Interdisciplinary Research ..................................................................... 17
.1.c.2.4 Significant Professional Service ................................................................................ 17
.1.c.3
Recognition of Scholarly Excellence ............................................................................ 17
.1.c.4
Unit Infrastructure for Supporting Research ................................................................. 18
.1.d
Contribution to Cities .......................................................................................................... 18
.1.e
Globalizing the University .................................................................................................. 18
.1.f Overall Assessment ................................................................................................................. 18
.2
Resources .................................................................................................................................... 19
.2.a
Faculty Resources ............................................................................................................... 19
.2.a.1
Student/Faculty Ratio .................................................................................................... 19
.2.a.2
Credit Hour Generation ................................................................................................. 19
.2.b
Administrative Resources ................................................................................................... 20
.2.b.1
Staff Support per FTE Faculty Member ........................................................................ 20
.2.c
Technological Resources ..................................................................................................... 20
.2.d
Space Resources .................................................................................................................. 20
.2.e
Laboratory Resources ......................................................................................................... 20
.3
Where We Want to Go................................................................................................................. 21
.4
What to Do to Get There ............................................................................................................. 22
.4.a
Student Success in Core Courses ........................................................................................ 22
.4.b
Development of Honors Courses ........................................................................................ 23
.4.c
Growth and Focus of the Major .......................................................................................... 23
.4.d
Growth of Graduate Programs ............................................................................................ 24
.4.e
Enhancing the Department's Research Profile .................................................................... 25
Appendices .............................................................................................................................................. 26
.5
Data on Freshman Math Majors.................................................................................................. 26
.6
Assessment Reports .................................................................................................................... 26
.6.a
General Learning Outcomes ............................................................................................... 26
.6.b
Pre and Post QL Data .......................................................................................................... 37
.6.c
Math 2211 Calculus I Assessment ...................................................................................... 41
.6.d
Math 1113 Precalculus Assessment .................................................................................... 49
.6.e
Math 1111 College Algebra Assessment ............................................................................. 60
.7
Retention and Graduation ........................................................................................................... 67
.8
Ethnic and Gender Diversity of Undergraduates ........................................................................ 67
.9
Student Survey Data.................................................................................................................... 68
.9.a
Undergraduate Alumni Surveys .......................................................................................... 68
.9.b
Undergraduate Student Surveys .......................................................................................... 72
.9.c
Graduate Alumni Surveys ................................................................................................... 75
.9.d
Graduate Student Surveys ................................................................................................... 79
.10 Syllabi ......................................................................................................................................... 83
.10.a
Math 1070 Elementary Statistics ........................................................................................ 83
.10.b Math 1101, Introduction to Mathematical Modeling .......................................................... 84
.10.c
Math 1111 College Algebra................................................................................................. 85
.10.d Math 1113 Precalculus ........................................................................................................ 86
.10.e
Math 1220 Survey of Calculus............................................................................................ 87
.10.f
Math 2211 Calculus of One Variable I ................................................................................ 88
.10.g Math 2212 Calculus of One Variable II .............................................................................. 89
.10.h Math 2420 Discrete Mathematics ....................................................................................... 90
.10.i
Math 3000 Bridge to Higher Mathematics ......................................................................... 92
.10.j
Math 3435 Introductory Linear Algebra ............................................................................. 92
.10.k Math 4441 Modern Algebra I.............................................................................................. 94
.10.l
Math 4661 Introduction to Analysis I ................................................................................. 94
.10.m Math 4662 Introduction to Analysis II ................................................................................ 95
.10.n Math 4751 Mathematical Statistics I .................................................................................. 95
.11 Degree Requirements B.S. in Mathematics ................................................................................ 96
.11.a
No Concentration ................................................................................................................ 96
.11.b
Concentration in Actuarial Science ..................................................................................... 96
.11.c
Concentration in Computer Information Systems .............................................................. 97
.11.d
Concentration in Computer Science ................................................................................... 98
.11.e
Concentration in Managerial Sciences ................................................................................ 99
.11.f
Concentration in Statistics .................................................................................................. 99
.12 Course Descriptions .................................................................................................................. 100
.13 List of Courses ...........................................................................................................................110
.14 Major Counts............................................................................................................................. 133
.15 Degrees Conferred .................................................................................................................... 134
.16 Math Majors Enrolled in RIMMES .......................................................................................... 135
.17 Math Majors Enrolled in Honors .............................................................................................. 135
.18 Graduate Student Publications and Presentations ..................................................................... 136
.19 Student outcomes after graduation ............................................................................................ 147
.20 Citations by Tenured and Tenure Track Faculty ....................................................................... 149
.21 External Research Grants .......................................................................................................... 151
.22 Composition of Faculty............................................................................................................. 153
.23 Interdisciplinary Publications ................................................................................................... 157
.24 Editorial Work by Departmental Faculty .................................................................................. 159
.25 International Conferences and Talks ......................................................................................... 160
.25.a
International Conferences ................................................................................................. 160
.25.b Hosting of Visiting Scholars ............................................................................................. 161
.25.c
International Talks ............................................................................................................. 161
.26 Study Abroad Programs ............................................................................................................ 166
.27 Student Faculty Ratios .............................................................................................................. 167
.28 Credit Hour Generation ............................................................................................................. 167
.29 Assessment Reports for the Mathematics Major ...................................................................... 170
1 Where We Are Now
The Department of Mathematics and Statistics offers undergraduate and graduate degrees with several
concentrations. At the undergraduate level the degrees are a B.S. in Mathematics with no concentration
and with concentrations in actuarial science, managerial science, computer science, statistics, and
computer information systems. In collaboration with the Robinson College of Business, the
department also offers dual B.S./M.S. programs in actuarial science, mathematical risk management,
and computer information systems. At the graduate level, the department offers an M.S. degree with
concentrations in bioinformatics, biostatistics, discrete mathematics, scientific computing, statistics,
and statistics and allied field. A Ph.D. in Mathematics and Statistics is offered with concentrations in
Biostatistics, Bioinformatics, and Mathematics. In 2012 the department conferred 27 B.S. Degrees, 15
M.S. degrees, and 5 Ph.D. degrees. As of Spring 2014, the department has roughly 240 undergraduate
math majors. In 2012, the department had 68 M.S. students, and 47 Ph.D. students.
The department has a very strong involvement with the universities core curriculum, offering 11 core
courses used in area A2 and area D of undergraduate student's degree requirements. In Fall 2013, the
department taught more students than any other department at GSU except for Biology and
Communications. Several of the undergraduate courses (Math 1070, 1111, and 1113) are taught using a
hybrid model with a combination of lecture and self-directed work in a computer lab (the MILE). This
model has been used to successfully address previously high failure rates in core courses. The
department has also been focusing on lowering high failure rates in calculus courses through a variety
of measures.
The department's Ph.D. program is relatively new, accepting its first students in 2008, and has grown
quickly since then. In the same period, the focus of research in the department has shifted in several
respects. Over the last few years, much of the departmental hiring has focused on bioinformatics and
biostatistics. Currently about half the Ph.D. students in the department are in bioinformatics or
biostatistics. Departmental faculty have been actively involved in GSU initiatives in these areas, such
as the Brains and Behavior program (B&B), the Molecular Basis of Disease program (MBD), and the
Neuroscience Institute. Graduate programs are divided between mathematics and statistics with
separate graduate directors for each discipline. The bioinformatics and biostatistics concentrations
currently fall under the area of statistics, although prior to Fall 2013, the Ph.D. concentration in
bioinformatics was a mathematics concentration.
The math major has grown steadily over the last few years and a number of changes have had an
impact on the experience of our undergraduates. We have a capstone course in which students develop
a variety of skills relevant to research and work in the mathematical sciences. We also have a very
active undergraduate research program (RIMMES, Research Initiation in Mathematics, Mathematics
Education, and Statistics).
.1.a Quality of Undergraduate Students
Data on freshman math majors is included in Appendix 1. The data give averages for high school GPA,
Freshman index, SAT, and ACT. By these criteria, the quality of entering math majors has not changed
dramatically over the past five years.
.1.a.1 Scholarship Support
The department has not tracked data relevant to student scholarships.
.1.a.2 Student Success and Satisfaction
.1.a.2.1 Learning Outcomes
Assessment of the general education outcomes can be found in Appendix 2. Over the past five years,
MATH 1111 student success rates have been improved to 75% from 60%. Math 1113 shares the same
pattern. Math 1070 and Math 2211 have similarly improved student learning outcomes. Math 1111, 1113,
and 1070 are now taught in a hybrid MILE (Mathematics Interactive Learning Environment) model in
which students attend 50 minutes of lecture per week and spend the rest of their time in the lab studying
in a self-directed way. This hybrid model has had a large impact on the success rates of students in these
courses. Along with the change in the teaching and learning model, assessments and assessment
techniques used in the program also have a great impact in identifying the weaknesses and strengths of
the program. The assessments we have used allowed us to judge and monitor the students' progress
through observations, experiments, written assignments, and research projects. These research projects
include implementation of review sessions, different software usage, and the material used in the classes.
Also those assessments provide pedagogical templates that help professors to develop effective
instructional techniques and provide comprehensive information about student progress, including
students' strengths and weaknesses.
We believe assessment is needed for accountability. The assessment process has been changed over the
years to redefine our educational goals aligned with the university's strategic plan, articulated multiple
measurable objectives for each goal, designed appropriate approaches and measures to assess how well
students are meeting the articulated objectives. These changes give us an opportunity to re-examine
objectives, methods, and measures as feedback to help students to improve their learning.
In an on-going process, the content of Math 1111 and Math 1113 has been revised. Also depending on
the assessment results, the course material has been revised with more related real life examples and
collaboration has been made with other departments to respond to the needs of the industry and higher
education. Also, assessments have had a significant impact on instruction. Students are more motivated
to learn and are more engaged in learning.
Mathematics courses are sequenced. If the courses are effective, then success in an earlier course should
predict success in a later course. In assessing outcomes, how students perform after a course can be as
important a consideration as success in the course itself. The following table gives information on the
progression of students through precalculus and calculus. The first two columns classify students by
grade earned in Math 1113 in Spring 2013 and give the ABC rates for students earning that grade when
taking Math 2211 in Fall 2013. The second set of two columns give information for Math 2211 Spring
2013 and Math 2212 in Fall 2013 and the last two give Math 2212 Spring 2013 and Math 2215 Fall 2013.
Only first attempts are given in the ABC rates.
1113
Grade
2211
ABC
2211
Grade
2212
ABC
2212
Grade
2215
ABC
A
87.5%
A
76.8%
A
71.4%
B
64.5%
B
72.7%
B
50%
C
50.0%
C
64.3%
C
25%
Assessment reports for the mathematics major are given in Appendix 25. The overall assessment of the
major occurs during the capstone course Math 4991 Senior Seminar. The assessment is based on
demonstrating proficiency in the following areas:
1. Problem solving.
2. Knowledge of the discipline.
3. Use of technology.
4. Ability to use and consult specialized mathematical literature.
5. Ability to understand and formulate mathematical proofs.
Proficiency in these areas represent the broad goals of the major and is contributed to by the specific
learning outcomes of each of the courses required by the major. In Math 4991, these targets are assessed
through a range of project, problems, research assignments, paper reviews, and presentations. The
overall conclusion of the assessment has been that these targets are at least partially met in the sense that
a majority of the students did well enough on the specific assignments to demonstrate the desired
proficiency. Full details are given in the assessment reports.
.1.a.2.2 Recruitment Rates
The math major has grown steadily in the last 5 years. The number of majors increased from 171 in
Fall 2008 to 227 in Fall 2012. The data in Appendix 1 show a slight decrease in the SAT scores of
Freshman math majors over this period, while high school GPA did not vary significantly. It is not
clear how significant the variation in SAT scores might be. But it appears that, on the whole, the
students recruited into the program are comparable to those previously in the major.
.1.a.2.3 Retention Rates and Graduation Rates
Appendix 3 gives tables referencing retention and graduation rates for mathematics majors by cohort.
The tables cover cohorts starting with Fall 2006 and ending with Fall 2009. The data is somewhat
difficult to interpret, in that there is considerable variability in the rates from cohort to cohort. With a
majority of the cohorts, a significant drop in enrollment occurs within two years. This is a critical
juncture for math majors, in that success in the calculus sequence often determines whether a student
will continue with a math major. Note that in the latest cohort shown, the retention is significantly
better, with 59.3% retained over a 4 year period. A variety of things potentially contribute to this. The
department has put an enormous amount of effort into decreasing DWF rates in the calculus sequence.
We have also attempted to engage our majors more in the department through departmental advisement
and mentoring. The latter helps with retention early in a student's pursuit of the major. The latter is in
part intended to encourage students into appropriate courses, but also to tie students to the department
so that they build a sense of community.
The overall graduation rates for the cohorts vary quite widely, with the Fall 2007 cohort having the
worst rates of graduation of any of the four. We are not aware of any obvious changes within the
department that might account for this. There was a slight improvement in Fall 2008, and a more
substantial improvement in Fall 2009.
.1.a.2.4 Placement and Acceptance into Advanced Degree Programs
The department has not tracked data on placement of students into graduate programs. Nevertheless,
the student surveys are informative. Of the responding graduates, 18.2% are currently pursuing a Ph.D.
degree and 90.9% are currently employed. (See Appendix 5.a). Students indicated that they are
attending North Carolina State University and Georgia State University. Anecdotally, the department
has had undergraduates continue their studies at a number of other schools, including Georgia Tech, the
University of Tennessee, and Florida State.
.1.a.2.5 Ethnic and Gender Diversity
Information on the ethnic and gender composition of the department is given in Appendix 4. The split
between males and females has been steady at roughly 60% male and 40% female among
undergraduate math majors. In contrast, there has been what appears to be a significant change in the
racial composition of undergraduate math majors over the past five years. During this period, the
percentage of black students increased from 35% to 43% of math majors, now comprising the single
largest group. Overall, the population of math majors is diverse and representative of the GSU student
population, with the exception of an apparent disproportion between male and female students. This
disproportion is unfortunately typical of math departments (and several other STEM departments)
nationally.
.1.a.2.6 Level of Financial Need
The department has not tracked students' level of financial need.
.1.a.2.7 Student Surveys
Undergraduate alumni and current student surveys are given in Appendices 5.a and 5.b respectively.
The alumni surveys show a generally high degree of satisfaction with the program, and a high rate of
placement for former students. A majority of alumni strongly agree that the program has made a
positive contribution to their lives and has been useful in their professional lives. The majority of the
students are pursuing graduate study or careers in which a mathematics degree is useful. (Note that the
department provides concentrations with emphasis on business and computation, so placement into
such areas can be considered relevant to the degree). One notable issue is apparent from the alumni
comments: many of the students indicate that they would have benefited from more emphasis on
computation, either in the form of programming or use of specialized mathematical software. This is
an area that is emphasized in several concentrations, but not in the plain B.S. in mathematics. Math
4991 Senior Seminar has in the past covered Matlab programming, but it is possible that some students
would benefit from a course in programming taken prior to the senior year.
The current student surveys are more mixed. A majority of the students seem to take a positive view of
the department's instruction and programs (i.e. ratings of 5 or 6 on the numerical items). But there is
significant variation. The department scores below 4 on issues pertaining to emphasis on writing,
speaking, ethical standards, and availability of undergraduate courses. It is worth noting that some of
these issues are addressed in Math 4991 Senior Seminar, which is one of the last courses taken by math
majors. The numbers might reflect the responses of students who had not yet taken this course. We
also note that the number of tenured or tenure-track faculty has shrunk in the last few years (due to in
part to retirements and unfavorable tenure decisions), which has had an impact on our ability to offer a
diverse selection of upper level courses.
.1.a.2.8 Curriculum Quality
For the most part, the department conforms to widely accepted standards for an undergraduate
mathematics degree. The B.S. degree without concentration includes a standard calculus sequence,
discrete math, a bridge course, two semesters of linear algebra, two semesters of algebra, two semesters
of analysis, a course in probability, four upper level math electives, and a capstone course. For a
simple comparison, Georgia Tech's applied math major requires a calculus sequence, including
introductory linear algebra, differential equations, a second linear algebra, combinatorics, probability,
one semester of algebra, two semesters of real analysis, one semester of complex analysis, numerical
analysis, and three upper level math electives. Most schools require a calculus sequence, core
sequences in analysis and algebra, along with variations and electives specific to the program.
The MAA Committee on the Undergraduate Program in Mathematics report indicates that students who
are preparing for graduate school in mathematics should have a foundational grounding in analysis and
algebra and that all majors should have a broad view of the mathematical sciences, including
complementary points of view such as continuous and discrete, algebraic and geometric, deterministic
and stochastic, and theoretical and applied. The B.S. with no concentration includes all of these
elements. The various concentrations supplement core math courses with core courses from the
discipline associated with the concentration as well as relevant applied math courses such as
optimization and numerical analysis.
.1.a.2.8.a
Syllabi, Degree Requirements, and Advisement
Course content standards for core courses and the courses required for the B.S. in mathematics can be
found in Appendix 6. Which of those courses are required for specific concentrations can be found in
Appendix 7. Course descriptions are in Appendix 8.
The department has used an advisement and mentoring committee, the purpose of which was to provide
regular advisement for majors and to connect them more closely with the department. The majors were
grouped and each group was assigned to a member of the committee. The members of the committee
sent the students e-mail actively encouraging them to come in and discuss their plans and their
schedules. Participation by students was low and, with large changes in advisement procedures at
GSU, the department is reassessing departmental advisement of undergraduates. A variety of more
formal procedures, such as transfer credits and graduation audits, are handled by the departmental
director of undergraduate studies.
.1.a.2.8.b
List of Courses
A complete list of courses offered in the fiscal years of 2011, 2012, and 2013 is given in Appendix 9.
The table includes number of sections, total number of students, and average number of students per
section. By far the largest enrollment is in Math 1070 Elementary Statistics and Math 1101
Introduction to Mathematical Modeling. Descriptions of all the courses are in Appendix 8.
.1.a.2.9 Contribution to the Core Curriculum and General Education Outcomes
The Department of Mathematics and Statistics makes a large contribution to the core curriculum
through area A2 and area D of the core curriculum. The learning outcomes associated with these areas
are
•
A2: Students understand and apply mathematical concepts and reasoning using verbal, numeric,
graphical and/or symbolic forms.
•
D: Students demonstrate understanding of the physical universe, the nature of science, and the
scientific method, and/or understand and apply mathematical concepts and reasoning using
verbal, numeric, graphical or symbolic forms.
The courses used in area A2 vary by major, but are typically one of: Math 1101, Math 1111, Math 1113,
Math 2211, or Math 2201. Math 1220, 2202, 2212, 2215, or 2420 can also be used in this area. Area D
for STEM majors typically requires Math 2211 or higher (or a course approved by the department).
For non-STEM majors, Area D includes the option of taking a range of math classes, including Math
1070, Math 1111, Math 1112, Math 1113, Math 1220, Math 2211, Math 2201, Math 2212, Math 2202,
or Math 2420. These courses contribute to the above quantitative learning outcomes in a fairly direct
way. Detailed assessment reports are included in Appendix 2.
.1.a.3 Signature Experiences
.1.a.3.1 Research Practica
Every year the Department hosts a research program for undergraduate students titled Research
Initiations in Mathematics, Mathematics Education and Statistics (RIMMES). The program is selective
and entrance is based upon an application process. It runs from October 1 until March 31 and it pairs
faculty mentors with student interested in research projects. The program concludes with a Conference
Day hosted by the Department in April where the students give 10 minute long presentations on their
work. The projects are also collected in Proceedings booklet available for consultation in the
department. The average number of students enrolled in the program per year is 10.
A table detailed enrollment by year is included in Appendix 12. During the period shown, the program
resulted in 37 student research talks at GSU and 23 talks outside of GSU.
.1.a.3.2 Urban Service Learning Programs
This is not something the department has been involved in.
.1.a.3.3 Internships
The department currently has no internship program and does not track student internships. A number
of students, particularly those in business related concentrations, such as Actuarial Science and
Managerial Science, have been successful in finding internships on their own.
.1.a.3.4 Study Abroad
The department has sponsored several study abroad programs. See Appendix 22.
.1.a.4 Honors College
.1.a.4.1 Honors Courses and Honors Add-ons
The following honors courses or add-ons were taught by departmental faculty:
•
•
Yao, add-on, Spring 2012
Enescu, Honors Seminar. Fall 2011
The department has also been teaching regular honors sections of Math 1070 Honors Elementary
Statistics. In the future, it is our intent to offer a number of courses as honors sections, including Math
1111 College Algebra, Math 1113 Precalculus, and Math 2211 Calculus of One Variable I. The hope is
that highly capable math majors, and other majors that would benefit from a strong foundation in math,
will particularly benefit from an enriched experience in these courses.
.1.a.4.2 Honors Faculty Fellows
Dr. Florian Enescu and Dr. Zhongshan Li are honors faculty fellows.
.1.a.4.3 Honors Students in the Major
The number of majors enrolled in the Honors program has significantly increased from 10 in Fall 2009
to 26 in Spring 2014. A table with the breakdown is attached in Appendix 13. This is a significant fact
in the context of a proposed departmental plan to encourage qualified math majors to participate in the
honors program and take honors sections of mathematics courses.
.1.a.4.4 Students Participating in the GSU Research Undergraduate Conference
The department has been a continuous presence at the GSURC through poster and oral presentations
with notable success. In 2011, Thomas Polstra won the second prize for this poster, and, in 2011, John
Hull has the third prize for his oral presentation (both advised by F. Enescu).
.1.a.5 Undergraduate Programs within the GSU Context
As with any math department, many of our lower level courses are an important part of the core
curriculum. Math 1101 Introduction to Math Modeling is terminal course taken by majors that have
limited need for further mathematics. Math 1111 College Algebra is required by business majors and is
a path to precalculus and calculus for other majors. Math 1113 Precalculus is the minimum area A
course for STEM majors and provides a path to calculus for majors that need calculus. Biology
requires Math 1113. Other STEM majors require varying levels of calculus: Computer Science and
Chemisty require two semesters of calculus, and physics requires three semesters of calculus. Math
1070 provides an elementary statistics course that is used by a large number of majors. See Appendix 8
for descriptions of these courses.
The department offers a number of service courses targeting specific majors. Math 2008, 3050, 3070,
and 3090 are taken by elementary education majors. Math 2008 and 3050 are also taken by Middle
Level Education majors. Math 3030 is taken by Computer Science majors. Math 2201 and Math 2202
form a calculus sequence that is focused on biology, chemistry, and neuroscience majors. Math 1070 is
required by business students. Courses in vector calculus and partial differential equations are
commonly taken by both physics and math majors.
The department has a number of collaborative curriculum efforts with other departments and colleges.
In particular, we have three new dual degree programs in which students earn a B.S. in math with a
M.S. in a business field. The options for the M.S. are Actuarial Science, Mathematical Risk
Management, and Computer Information systems. The programs are new, but student interest has been
high and we have strong hopes that these programs will provide students with paths towards successful
careers.
.1.b Quality of Graduate Students Attracted to the Program
.1.b.1 Expanding Support for Graduate Programs
The department has a Ph.D. program that first admitted students in Spring 2008. The Ph.D. program
had 10 students by Fall 2008. At the same time, the M.S. program had 73 students. The Ph.D. program
grew quickly while the M.S. program remained steady. In 2010, 2011, and 2012, department had 76,
75, and 68 M.S. students respectively, and 40, 45, and 47 Ph.D. students respectively.
.1.b.1.1 Total Numbers of Graduate Students by degree program, and
concentration.
M.S. program:
•
Mathematics Concentrations: 26 in 2010, 25 in 2011, 14 in 2012.
•
Bioinformatics/Biostatistics concentrations: 50 in 2010, 50 in 2011, 54 in 2012.
Ph.D. program:
•
Mathematics Concentrations: 13 in 2010, 18 in 2011, 22 in 2012.
•
Bioinformatics/Biostatistics concentrations: 27 in 2010, 27 in 2011, 25 in 2012.
.1.b.1.2 Percentage of Graduate Students Compared to Total Number of Students
in the Department
In 2010, 2011, and 2012. The percentages are 34%, 35%, 33%, respectively. This is close to a standard
ratio for research universities and in line with GSU targets.
.1.b.1.3 Graduate Student Financial Support
In 2010-2012, total number of students with GLA/GTA, and GRA support was:
•
GLA and GTA: 41 in 2010, 58 in 2011, and 56 in 2012.
•
GRA: 12 in 2010,13 in 2011, and 23 in 2012.
M.S. GLAs are supported at $7,500 per year; the Ph.D. GLAs are supported at $10,000 per year. M.S.
GTAs are supported at $9,000 per year; the Ph.D. GTAs are supported at $15,000 per year. The GRAs
are supported at various levels, up to $27,000.
.1.b.1.4 Ratio of Graduate Students to Faculty
In 2010- 2012, the ratios of graduate students to tenured and tenure-track faculty were 4.8, 5.0, and 5.7.
The ratios of Ph.D. students to graduate faculty were 1.5, 1.6, and 1.7.
.1.b.2 Data on Professional Degree Programs
Not applicable.
.1.b.3 Student Success and Satisfaction
.1.b.3.1 Learning Outcomes
A number of targets have been set for learning outcomes and progress of students in the M.S. and Ph.D.
programs. The targets focus on progression through the program by completion of courses, qualifying
exams, and completion of a thesis or dissertation.
•
Target 1: 85% of the Ph.D. students should complete at least eight graduate courses toward
their degree with a GPA of at least 3.0 by the end of their second year in the program. 70% of
the Ph.D. students are expected to pass Ph.D. Qualifying Exams in three areas by the end of
their second year in the program.
90% of the Ph.D. students completed at least eight graduate courses toward their degree with a GPA of
at least 3.0 by the end of their second year in the program. 80% of the Ph.D. students passed Ph.D.
Qualifying Exams in three areas by the end of their second year in the program.
Finding (2010-2013) - Target: Met
•
Target 2: 80% of Ph.D. students who have passed the Ph.D. Qualifying Exams are expected to
successfully complete and defend their dissertations.
100% of Ph.D. students who have passed the Ph.D. Qualifying Exams successfully completed and
defended their dissertations.
Finding (2010-2013) - Target: Met
•
Target 3: 85% of the M.S. thesis students should complete at least eight graduate courses
toward their degree with a GPA of at least 3.0 by the end of their second year in the program.
100% of the thesis students completed at least eight graduate courses toward their degree with a GPA of
at least 3.0 by the end of their second year in the program.
Finding (2010-2013) - Target: Met
•
Target 4: 85% of the non-thesis students should complete at least 10 graduate courses toward
their degree with a GPA of at least 3.0 by the end of their second year in the program.
Finding (2010-2013) - Target: Met
•
Target 5: 50% of the thesis proposals are expected to be approved for continuation on the thesis
track. The average time between entrance into the program and receipt of degree was 24
months.
100% of the non-thesis students completed at least ten graduate courses toward their degree with a GPA
of at least 3.0 by the end of their second year in the program.
Finding (2010-2013) - Target: Met
•
Target 6: 90% of the non-thesis students are expected to complete the research paper or project
report successfully in one semester.
100% of the non-thesis students have completed their research papers or project reports successfully in
one semester.
Finding (2010-2013) - Target: Met.
The surveys in Appendix 5.c and Appendix 5.d give some insight into graduate student satisfaction.
Most students seem to be satisfied or very satisfied with the programs, with some having concerns
about the scheduling of classes. Some of those scheduling issues are related to staffing issues caused
by a decrease in the number of statistics faculty.
.1.b.3.2 Recruitment Rates, Admission Requirements and procedures, and
advisement.
On the average, the acceptance ratios in 2010-2013 were: M.S. programs, 58% ; Ph.D. programs,
47% . The ratio in enrollment was: M.S. programs, 53% ; Ph.D. programs, 37% . The admissions
requirements were
•
A baccalaureate degree in mathematics or its equivalent.
•
Courses in mathematics equivalent to the following: Linear Algebra and two of the following
courses (depending on the concentrations): Modern Algebra I, Analysis I, Mathematical
Statistics I, Mathematical Statistics II, Graph Theory, Numerical Analysis, and Differential
Equations.
The admissions procedure followed by the department is that the departmental Graduate Committee
reviews the applications and makes admission/denial recommendations to the College of Arts and
Sciences.
With regard to graduate advisement, a new graduate student orientation is given before the classes start
in August. Each M.S. thesis student and Ph.D. student is assigned a faculty advisor. The graduate
directors provide general academic advisement to all graduate students; the faculty advisors are
primarily responsible for direction of thesis/dissertation research.
.1.b.3.3 Retention Rates, Graduation Rates, and Output Quality Metrics
The overall graduate retention rate is 90%. Of the M.S. students, 85% complete their degree programs
within three years. Of the Ph.D. students, 80% complete their degree program within five years. (Note
that the Ph.D. data is based on a limited number of students who were admitted early in the
development of the Ph.D. program.)
Output Quality Metrics:
•
Is the thesis/dissertation clearly written and well organized?
•
Most of the thesis/dissertations are clearly written and well organized. But some of them still
need improvement
•
What is the main contribution of the thesis/dissertation research?
•
25% of the theses proposed novel mathematical/statistical methods. But most of the theses were
based on simulation and data analysis results
•
Does the candidate have comprehensive knowledge on the thesis/dissertation research?
•
A majority (84%) of the students have comprehensive knowledge on the thesis/dissertation
research .
•
Is the thesis/dissertation technically sound and the main result justified?
•
A majority (75%) of the theses had sound results. But some of the theses results still need
simulation/theoretical justifications.
•
Are the results in the thesis/dissertation published or publishable in a research journal?
•
58% of the results in the thesis/dissertation are publishable in research journals after some
revisions.
.1.b.3.4 Placement Rates
Since Spring 2011, the M.S. program has graduated 59 MS students. Placement rate is 92%. The Ph.D.
program has graduated 16 Ph.D. students. Placement rate is 100%.
.1.b.3.5 Ethnic and Gender Diversity
The percentages for the years 2010, 2011, and 2012 were: Nonresident aliens, (35%, 35%, 42.5%) ;
Asian American, (9.8%, 11.7%, 11.5%) ; African American, (22%, 22.3%, 20.4%) ; White, (26.8%,
26.2%, 22%) ; Other, (6.4%, 4.8%, 3.6%) ; Male, (55%, 61%, 53%) ; Female: (45%, 39%, 47%) . The
composition of the graduate population has not changed dramatically over the last few years. Males
outnumber females, but not by a wide margin. Nonresident aliens are the largest group.
.1.b.3.6 Level of Financial need
Most of the graduate students have adequate financial resources to complete the degree programs. No
further accurate data is tracked by the department.
.1.b.3.7 Student Surveys
The graduate student alumni and student surveys are in Appendix 5.c and Appendix 5.d. The numerical
responses are overwhelmingly positive, with the exception of a common complaint about “Availability
of graduate courses in the department”. The written comments are mixed. Some written comments
praise the graduate program and faculty, while a few (included in the appendix) raise some concerns,
representing the opinions of a minority (about 10%) of the students as reflected in the numerical data.
.1.b.3.8 Student Publications and Presentations
In the last five years, the graduate students have published at least 66 papers and have given 65
presentations at conferences and workshops. Full details can be found in Appendix 14.
.1.b.3.9 Student Outcomes After Graduation
Since 2010, 13 Ph.D. graduates have found academic or industrial jobs. At least 58 M.S. graduates have
found academic or industrial jobs. More detail can be found in Appendix 15.
.1.c Research in the Department
.1.c.1 Success of the Department's Research Culture
.1.c.1.1 2CI Hires
The Department is currently pursuing a 2CI hire for big data disease modeling.
.1.c.1.2 Levels of External and Internal Funding
During the period from the 2009/2010 academic year through the 2013/2014 academic year, the
department grant funding was as follows:
•
External grants totaled $1,900,992, based on 30 separate grants.
•
Internal Research grants totaled $180,509, based on 13 separate grants.
•
Internal conference grants totaled $18,000, based on 5 separate grants.
To provide context for these numbers, note that the current overall departmental budget for the
department is $3,508,511.
.1.c.1.3 National/International Rankings of the Unit
The department is currently in the American Mathematical Society “public small” group of public
Ph.D. granting departments. The most recent NRC rankings for departments was based on data
compiled in 2005 and 2006, prior to the introduction of the Ph.D. program. Thus the department's
Ph.D. program is too new to have any established ranking.
.1.c.1.4 Research Productivity that Furthers the Strategic Goals of the University
Research activity has grown substantially in the department, which directly supports the strategic plan
for GSU to become a leading public research university addressing the most challenging issues of the
21st century. The topics of research have also shifted to some extent in the direction of challenging
new areas such as bioinformatics and biostatistics. This happened in combination with the
development of a Ph.D. concentration in bioinformatics and biostatistics.
The vast majority of departmental tenured and tenure track faculty have active research programs.
Since 2009, faculty in the department list 182 publications in refereed journals. During this period
faculty have received 21 research grants from federal funding agencies as PI or co-PI. Another 24
federal research grant proposals are under review. A list of federal grants obtained by departmental
faculty over the last several years can be found in Appendix 17.
.1.c.1.4.a
Citations
Citations for tenured and tenure track faculty in the department are given in Appendix 16. The table
gives two lines for each faculty member. The first is from the Thompson Reuters InCites database.
The second line includes two fields. The first field is total number of citations from MathSciNet, the
American Mathematical Society database, which is a standard source for mathematical citations. The
second field of the second line includes the citation database that individual faculty feel is most reliable
and complete for their respective areas. The reason for the multiple sets of numbers is that no one
database appeared to be reliable and complete across multiple fields. In some cases, the databases
appear to include only a small subset of the papers of a given author. This leads to an apparent small
number of citations, and in some cases a large number of average citations per paper, if the paper was
well cited. Bioinformatics and biostatistics faculty in particular seem to suffer from an artificially low
number of citations on MathSciNet, since they often publish in journals outside of mathematics and
statistics. Despite the difficulty of interpreting clearly incomplete data, it appears that most faculty in
the department have significant papers that have generated regular citations.
.1.c.1.5 Success in Recruitment and Retention of Top Faculty in the Field
A number of strong faculty have been recruited and hired since 2009, including Xin Qi, Remus Osan,
Yi Jiang, Hein van der Holst, and Xiaojing Ye. Inspection of the citation data in Appendix 16 shows
that several of these faculty already have substantial research records. Others are promising young
researchers with recent Ph.D.s. Several came with, or have since received, federal grants to support
their research.
.1.c.1.6 Faculty Development
Faculty composition is given in Appendix 18. Note that between Fall 2010 and Fall 2012, 3 faculty
were promoted to Associate Professor with Tenure. Several faculty were denied tenure or otherwise
left the department (one went to the neuroscience institute), resulting in a net decrease in assistant
professors from 13 to 6. The record of development of assistant professors to the point where they are
promoted to associate professors is mixed. It is also worth noting, that more recently, a number of
associate professors have been promoted to full professor. The most recent numbers for the department
are 7 full professors, 10 associate professors, and 4 assistant professors.
.1.c.2 Faculty Partnerships and Professional Service
.1.c.2.1 Participation in Research Centers and Clusters
Two former faculty joined the neuroscience institute and one joined the school of public health.
However the neuroscience faculty maintain contacts within the department, in fact teaching
mathematics courses, and have been collaborating with math department faculty.
.1.c.2.2 Collaborations
Most faculty have national collaborators and many maintain international collaborations.
.1.c.2.3 Evidence of Interdisciplinary Research
Many departmental faculty have interdisciplinary interests in their research. A particular focus has
been biological applications, through the fields of bioinformatics and biostatistics. A list of recent
interdisciplinary publications is given in Appendix 19.
.1.c.2.4 Significant Professional Service
Most departmental faculty referee papers regularly and many serve on editorial boards. See Appendix
20.
.1.c.3 Recognition of Scholarly Excellence
Departmental faculty have won variety of formal recognitions, including:
•
Belykh, Igor : Best Associate Editor Award, IEEE Circuits and Systems Society (December 10,
2013)
•
Bondarenko, Vladimir E. : Dean's Early Career Award (2011)
•
Enescu, Florian : Hambidge Fellow, Hambidge Center for Creative Arts and Sciences (March
2013) ; BIDEFENDER Invited Professor, S. Stoilow Institute of Mathematics of the Romanian ,
Academy of Sciences (May 2012) ; Nominated - Outstanding Faculty Award, Chair for the
College of Arts and Sciences (2011)
•
Li, Zhongshan : Shangxi Province 100 Talent Program Eminent Scholar, 2012- 2015, Shanxi
Province and North University of China (September 9, 2011) .
•
Osan, Remus M. : Early Career Award, Mathematical Biosciences Institute, Ohio State
University (September 27, 2013)
•
Smirnova, Alexandra B. : GSU 2012 Outstanding Faculty Award Nomination, GSU
•
Zhao, Yi : Young Investigator Award, National Security Agency (April 3, 2012) ; Young
Investigator Award, National Security Agency (December 1, 2009)
•
Hall, Frank: Fulbright Scholar grant to visit the Czech Republic and collaborate with Prof.
Miroslav Fiedler.
•
Yichuan Zhao, Young Investigator Award, National Security Agency (February 2012) .
•
Yichuan Zhao, Elected Member of the International Statistical Institute, 2009.
.1.c.4 Unit Infrastructure for Supporting Research
The departmental business manager provides support for administration of grants. The department
offers limited funding for faculty for summer research. Faculty are asked to write a proposal and the
proposals for summer research are reviewed by a committee. The funding is limited, but it has been
used to encourage submission of grant proposals; eligibility for the departmental support has been tied
to submission of external grant proposals.
.1.d Contribution to Cities
This is an area in which departmental contribution is currently minimal.
.1.e Globalizing the University
Departmental faculty have a number of strong international connections, including various visiting
professorships at foreign universities:
•
•
•
•
Dr. Florian Enescu: BITDEFENDER Invited Professor at the Institute of Mathematics of the
Romanian Academy for the month of May 2012.
Dr. Frank Hall: With a Fulbright Research Award, he was in Prague Czech Republic, Feb May 2010 doing research with Professor Miroslav (Mirek) Fiedler of the Czech Academy of
Sciences.
Dr. Zhongshan Li: Honorary Guest Professor of North University of China, 2006—present .
Dr. Igor Belykh: Invited Professor, Swiss Federal Institute of Technology, June-July 2009;
Associate member of the Centre for Chaos Control and Synchronization, City University of
Hong Kong, 2001-present; Associate member of the INPSC Multidisciplinary Institute for
Complex Systems, Normandy, France, 2009-present; Invited Professor, Polytechnico di
Milano, Milan, Italy, three months in 2006-2007 (grant from Cariplo foundation and Landau
network-Centro Volta Fellowship, Italy).
Faculty have also organized a number of international conferences and given many international talks.
Lists are given in Appendix 21.
Faculty have proposed various study abroad programs, shown in Appendix 22. Finally, the department
has been very successful in recruiting strong international students, as is apparent from the composition
of the graduate student population given in 1.b.3.5.
.1.f Overall Assessment
The department has had made several very notable accomplishments in the last few years. In particular
•
The department has a relatively new Ph.D. program that has grown dramatically. This has had a
big impact on the research output of the department and on the focus of faculty on directing
student research. It has also had a major impact on who is teaching courses; far more 1000
level courses are taught by GTAs. The Ph.D. program has not reduced the size of the M.S.
program, which has held approximately steady over the same period.
•
The department has fully embraced the current hybrid MILE hybrid model for several classes,
including Math 1070, 1111, and 1113. This has had a big impact on course assignments for
faculty, who now focus more on upper level courses while GTAs often cover MILE courses.
•
The current model has been successful. The department has made very strong progress in
improving student success rates in the very courses that are taught by the new GTAs. In many
courses, most recently Calculus, success rates for students have gone from below 50% to above
70%.
Over the past five years, in the same time that the department graduate programs have grown and there
has been a strong focus on improving undergraduate education, the department has actually shrunk in
terms of tenured and tenure track faculty. From Fall 2009 to Fall 2013, the department went from 43 to
36 full time faculty. Given the advances made by the department under conditions of limited resources,
we believe that it is critical that the department have the resources to continue the path of innovation of
the last few years, with strong support for the MILE and for the graduate programs.
.2 Resources
.2.a Faculty Resources
As the population of undergraduate and graduate mathematics majors was steadily growing over the three
year period (AY 2010 – AY2012), the number of tenured and tenure-track faculty members declined from
24 to 22. The total number of full time faculty members changed from 36 to 37 and then back to 36. The
shortage of instructors was compensated, to some extent, by the growing pool of GTAs (24 in 2010 vs.
30 in 2012). Additionally, the department had (on average) 2 part-time instructors each year. For our
graduate program to strengthen and develop, the number of tenure-track faculty members must be
increased. In order to meet the current teaching needs at the undergraduate level (in covering and
coordinating courses as well as supervising the graduate teaching assistants) and to accommodate further
increases in undergraduate and graduate enrollment, more full time lecturers are required. The number
of academic professionals (3) can be viewed as adequate since there are currently three hybrid emporium
model courses (Math 1070, 1111, and 1113) that need to be managed by them.
.2.a.1 Student/Faculty Ratio
Over the three year period (Fall 2010 – Fall 2012), the UG student/TT and the Grad student/TT Ratios
have grown from 9.4 to 10.8 and from 4.8 to 5.7, respectively. Ph.D. student/Grad Faculty Ratio has
changed from 1.5 to 1.7 with the number of graduate faculty members being 28. However, out of 28
graduate faculty members in 2012, only 22 were TT faculty (and can serve as chairs of doctoral
dissertation committees and direct M.S. theses). While there is a non-thesis option for the master’s
program, for the Ph.D. program to grow, the number of TT faculty members must be at least 30 so that
the pool of Ph.D. students could increase from 47 to 60 or even 70.
.2.a.2 Credit Hour Generation
The Department of Mathematics and Statistics is one of the major credit hour generators in the College
of Arts and Sciences, with only Biology and Communication Departments ahead of us. As far as fall
enrollment is concerned, from Fall 2010 to Fall 2012 the number of credit hours generated by our
department has grown from 19,594 to 20,632. One can observe a similar growth in the spring semesters:
from Spring 2011 to Spring 2013, the number of credit hours increased from 17,523 to 18,981. In the
fiscal year 2011, the number of credit hours was 43,473 and by the end of the fiscal year 2013 it increased
to 45,472. Moreover, in Spring 2014, the number of credit hours generated by our department has grown
even further (to 19,870), which is a 4.7% increase compared to Spring 2013. For comparison, in the
College of Arts and Sciences, Spring 2013-2014 increase is 0.4%. This 4.7% increase at our department
is the result of the growing undergraduate enrollment. In the near future, the department will need to
work on increasing its graduate enrollment, where the lack of funds for graduate support and insufficient
number of tenure-track faculty members is the main obstacle.
.2.b Administrative Resources
.2.b.1 Staff Support per FTE Faculty Member
Our administrative resources have remained essentially the same since before the Department started its
Ph.D. program in Fall 2007. Before the Ph.D. program, we had 3 full time staff members (Business
Manager, Office Manager, and an Administrative Assistant) and a part-time Staff Assistant. Recently, the
part-time Staff position got upgraded from ½ time to ¾ time, and that is the only change compared to the
pre-Ph.D period. However, the growing number of faculty external grants, as well as the growing number
of graduate assistants (from under 30 to over 70), requires a more substantial increase in our staff support.
.2.c Technological Resources
The Department of Mathematics and Statistics provides desktop computers for its graduate teaching
assistants as well as a separate computer lab for the general graduate student population. Faculty members
have desktop and/or laptop computers in their offices. To enhance the quality of instruction, the
Department has provided each faculty member with an Apple iPad. Several network-connected printers
and copy machines serve students and faculty members. A poster printer was bought in response to the
growing research demand. Some faculty members have computers and printers purchased from their
grant funds. To maintain and develop these technological resources, the Department of Mathematics and
Statistics needs a dedicated computer administrator. The lack of this position is of a major concern.
.2.d Space Resources
Most of our full time faculty members have individual offices. Three academic professionals share one
(small) office. There are only two offices for about 30 graduate teaching assistants and they are rather
crowded, especially when GTAs hold their office hours. There is only one (very small) colloquium room
that can no longer accommodate the entire seminar and colloquium audience. Many visitors mentioned
that the colloquium room is so packed that it is really hard to present there. Unsupported graduate students
have no space to study at the Department. They have to study in the library, which is far away from their
advisors (and that is inconvenient). The main concern is a shortage of individual offices and a complete
lack of seminar rooms, where faculty members and their students could hold regular research meetings.
.2.e Laboratory Resources
The first of the department's instructional computer laboratories (Urban Life MILE) was introduced (fullscale) in Fall 2005 to address the performance rates of College Algebra (Math 1111) and Precalculus
(Math 1113) courses. This state-of-the-art lab contained: 83 computers (including one dedicated
computer for the visually impaired), two printers for students’ use, an overhead projector, and software
for tracking student visits (AccuTrak). ” In Fall 2010 the Department implemented a redesign for its
Elementary Statistics course (Math 1070). Since Math 1070 is GSU’s highest enrolled course offered
through the Department of Mathematics and Statistics, a separate location was formed to accommodate
the large number of matriculating students. This location (Commons MILE) uses the same software
adopted to track student visits, AccuTrak, but houses 120 workstations and 2 projectors.
While instructional needs are met, the laboratory resources for research are virtually nonexistent. In the
future, we expect each TT faculty member to have a research laboratory space for them and their
undergraduate and graduate students.
.3 Where We Want to Go
The department has the following goals:
•
Strengthen and maintain recent improvements in outcomes in core courses. Thanks to efforts by
many in the department, student success has improved substantially in Math 1111, Math 1113,
and Math 2211. The decrease in the DWF rate for Math 1111 and Math 1113 largely
corresponds with the introduction of the current MILE model. Success in Math 2211 has been
more a matter of supplemental instruction and careful pruning of nonessential topics so that
more time can be spent on topics that are essential for success in later courses. However there
are still possibilities for improvement by adjusting the models for teaching all of these courses,
particularly as regards the issue of ensuring that the courses contribute to the success of students
in later sequenced math courses. It is our goal to continue to strengthen the MILE model to
both ensure strong success rates in the MILE courses while also ensuring that the MILE courses
prepare students for the calculus sequence.
•
Develop more honors courses and establish a higher percentage of math majors participating in
honors courses, particularly at the 1000 and 2000 level. This goal connects with the retention
and progress goal below, in that participation in honors course will give mathematics majors a
richer understanding that will benefit them later in the program.
•
Retention and progress of math majors: Increase the number of undergraduate math majors,
while also increasing the rate of graduation. The math major has grown steadily over the last
few years. We would like to ensure that we grow both the major and the number of math
students who graduate. Thus we propose to focus on retention and progress in the mathematics
major.
•
Develop appropriate concentrations and curriculum in the undergraduate programs. Some of
the concentrations are more active than others, with the Statistics and Actuarial Science
concentrations being the most popular, along with the “no concentration” option of a B.S. in
Mathematics. Our goal is to develop concentrations that will be both popular with students and
relevant to specific careers and further education in mathematics at the graduate level. They
should also provide reasonable paths for students with different skills and interests to make
progress toward graduation. It is important that we fill notable gaps in the curriculum, such as
topics relevant to pure math (e.g. geometry and topology) and topics relevant to applied math
(e.g. computer programming and general computational skills).
•
Continue to grow and strengthen the Ph.D. Program. The Ph.D. program has grown
significantly in the last five years. One goal is to support 40-60 Ph.D. students/per year.
The M.S. program has been successful in the last five years. The size of the current M.S.
program is reasonable. With very limited financial support for M.S. students and insufficient
faculty in statistics, our goal is to maintain the size of M.S. program at its current level.
Enhance the quality of graduate students. In addition to providing effective instruction and
research direction, we will improve the quality of the admitted graduate students by recruiting
graduates from strong undergraduate or graduate programs and by encouraging the applicants to
take the GRE subject test.
Continue to develop strengths in research in pure and applied mathematics and raise the
departmental research profile. This includes increasing external funding, increasing significant
publications, and recruiting strong researchers for faculty positions.
•
•
•
•
Continue to develop strong international connections, including study abroad programs,
recruitment of international students, and international research collaborations.
.4 What to Do to Get There
In what follows we give details on the departmental plans for achieving the goals outlined above.
.4.a Student Success in Core Courses
The department has made major advances in improving student success in core courses. It is critical
that we continue on this path. In particular, we need to make sure that the MILE courses have adequate
support and are adequately staffed, as well as ensure that appropriate resources are directed to
supplemental instruction for the calculus sequences. Given that all of these courses are sequenced, we
also need to ensure that the content of the courses and the method of instruction build skills that prepare
students for the next course in a sequence.
Regarding the resources required for these courses, there are on-going staffing and technology issues
associated with the MILE model and appropriate university support is critical. For expenses related to
instrucational inovation and pilots, we have also been active in seeking external support for various
initiatives related to improving education. The department has submitted several grant proposals of
this type, including:
•
Bondarenko, Vladimir E (Co-Principal), Grinshpon, Mark Samuilovich (Principal), Osan,
Remus Mihai (Co-Principal), Chahine, Iman Chafik (Co-Principal), Belykh, Igor (CoPrincipal), Clewley, Robert Harvey (Supporting), Rizzo, Rebecca L (Supporting), Stewart,
Michael A. (Supporting), "Enhancing Calculus Instruction Using Inquiry-Based Modules in
Neuroscience, Biomedical Sciences, and Physics to Improve Undergraduate STEM Education,"
Sponsored by National Science Foundation (NSF), Federal, $200,000.00.
•
Bondarenko, Vladimir E (Co-Principal), Grinshpon, Mark Samuilovich (Principal), Osan,
Remus Mihai (Co-Principal), Chahine, Iman Chafik (Co-Principal), Belykh, Igor (CoPrincipal), Rizzo, Rebecca L (Supporting), Stewart, Michael A. (Supporting), "Enhancing
Calculus Instruction Using Inquiry-Based Modules in Neuroscience, Biomedical Sciences, and
Physics to Improve Undergraduate STEM Education," Sponsored by National Science
Foundation (NSF), Federal, $300,000.00. (August 1, 2014 - July 31, 2016).
•
Smirnova, Alexandra B (Principal), Chahine, Iman Chafik (Co-Principal), Manzagol, Nilay
Sezin (Co-Principal), Sarkar, Sutandra (Co-Principal), Meadows, Leslie J (Co-Principal),
Grinshpon, Mark Samuilovich (Supporting), "Advancement of Student-Centered Education
through the Mathematics Interactive Learning Environment (MILE)," Sponsored by National
Science Foundation (NSF), Georgia State University, $500,001.00. (September 1, 2014 August 31, 2017).
We plan to continue to seek funding for instructional innovation in improvement of undergraduate
education. We also feel strongly that the department needs suitable resources to continue to expand the
success associated with the MILE, with supplemental instruction, and with the tutoring service offered
in the Math Assistance Complex (MAC). All of these have been and will continue to be critical to
undergraduate success in core courses.
Another issue is to provide an appropriate balance between lab and lecture time in the MILE courses
Math 1111 and Math 1113. Currently (Spring 2014) there is a pilot that increases the amount of lecture
time in these courses from the current 50 minutes per week to 105 minutes per week. Currently, both
students and faculty seem to have the perception that additional lecture time would be an improvement
in the model. To do this without increasing needs for faculty to staff the courses, the sizes of the
sections have been increased in the pilots. If the pilots suggest that it is worthwhile, we propose to
extend lecture time for the other sections of the MILE courses.
.4.b Development of Honors Courses
The department has proposed four new honors courses, variants of Math 1111 College Algebra, Math
1113 Precalculus, Math 2211 Calculus of One Variable I, and Math 2212 Calculus of One Variable II.
Our goals for the four new honors courses are: to offer strong students an enriched mathematical
experience in which they can interact closely with their instructor and each other; to provide students
with rigorous mathematics courses and more opportunities to foster independent and creative problem
solving; and to create friendships among the honors students taking mathematics courses and to
encourage the best Georgia State students to remain enrolled at GSU once they have completed their
basic coursework.
These courses are expected to benefit strong STEM students generally, but they have particular
potential in nurturing strong mathematics majors. This is expected to improve retention and progress
of such majors toward graduation by better preparing them for later course work. For context, it is
important to note that there are several difficult transitions in the undergraduate mathematics
curriculum. At these points, math majors encounter courses in which there is a significant jump in
what is expected of them in terms of mathematical sophistication. If all has gone well in previous
courses, the students are ready for this. But in many cases, these courses represent a potential barrier to
retention and progress toward graduation. (The transition to proof oriented courses is particularly
troubling, with many students struggling in Math 3000 Bridge to Higher Math and Math 4661
Introduction to Analysis I).
.4.c Growth and Focus of the Major
The goals of growing the major, increasing graduation rates for math majors, and developing
appropriate concentrations and courses are interrelated. The honors courses are one approach we
expect will provide better preparation for some math majors and give them a better basis for progress
toward graduation. However, this is not an approach that will benefit math majors not in the honors
program. Another measure that can both attract student and help move them toward graduation is to
have concentrations and courses that are relevant to the students goals. In particular we propose the
following:
•
A new concentration in applied mathematics. The department currently offers a straight math
degree with no concentration, in addition to concentrations in Statistics, Actuarial Science,
Managerial Science, Computer Science, and Computer Information Systems. There are also
three dual degree (B.S./M.S.) programs in cooperation with the Robinson College of Business:
Actuarial Science, Mathematical Risk Management, and Computer Information Systems. The
B.S. in Mathematics with no concentration is the main program in the department. It is focused
on developing modern mathematical skills with a strong emphasis on rigor in reasoning and
logical deduction. It provides a solid foundation for students wishing to pursue graduate studies
in mathematics.
Aside from Statistics, the concentrations are all focused on specific application areas. The B.S. in
math, the Statistics concentration, and the Actuarial Science concentrations have the largest number of
students. Note that these two specialized concentrations benefit from the fact that they give students
distinctly marketable skills and, particularly in the case of Actuarial Science, a clear path to a specific
career.
We propose a general (not specialized) concentration in applied mathematics. The structure of the
concentration would include skills in computation, statistics, and emphasis on areas of mathematics
with common and direct applications (e.g. differential equations, vector calculus, linear algebra,
numerical analysis, and optimization). The concentration will offer enough flexibility that students
preparing for mathematics graduate school can get the courses they need, but the emphasis of the
concentration will be on building skills that are useful for employment doing applied work in the
mathematical sciences.
Regarding growth of the program, we will attempt to make the concentration appealing enough to draw
in new math majors. Regarding retention and progress to graduation, the curriculum will provide
additional flexibility for students who are not planning on graduate study to choose courses that that are
relevant to their goals and do not pose a barrier to graduation. Finally, we note that the computational
aspect of the program is something that is clearly desired by students, as demonstrated by the
undergraduate surveys (see Appendix 5).
•
Assess and strengthen current concentrations: The B.S. in mathematics with no concentration
and the concentration in statistics are likely to be the main concentrations for students preparing
for graduate school in mathematics or statistics. We intend review these concentrations to make
sure that they provide a smooth transition to graduate programs.
•
Provide tutoring support for upper level math courses. We have experimented with providing
tutoring for upper level math courses. Academic support for upper level courses is no less
important for progress to graduation than support for lower level courses. We propose to
expand tutoring and academic support for upper level courses.
.4.d Growth of Graduate Programs
The following points reflect the plan for the on-going development of the department's graduate
programs.
•
•
•
•
•
The Ph.D. program has grown significantly in the last five years. Currently, the department
supports about 35 Ph.D. students. It is expected that the department will support 40-60 Ph.D.
students/per year.
Since most of the financial resources in graduate programs are used to support Ph.D. students,
the financial support for M.S. program is very limited. However, to maintain the normal
operation of MILE, 40-50 M.S. graduate assistants are needed in the MILE each semester.
Funding for the MILE and financial support for M.S. students is badly needed.
Increasing the size of the graduate faculty is essential to the growth of the graduate programs.
Currently, the department has only three statistics faculty members. At least 2-3 more statistics
faculty members are needed to maintain the current size of the graduate program in statistics.
Enriching the graduate courses offered by the department is crucial to the growth of graduate
programs. The graduate student survey showed that the demand for wider choice of courses is
strong. This is something that also requires additional graduate faculty.
The current support for Ph.D. students is not competitive with national standards. It is
important that the department increase the graduate assistant support to competitive levels in
order to enable the recruitment of strong Ph.D. Students.
.4.e Enhancing the Department's Research Profile
We propose the following for raising the department's research profile.
•
Pursue new tenure-track hires in areas that complement research groups in the department and
help existing faculty to add new dimension to their work by contributing to current national
trends in research. This includes, but should not be limited to, recently growing departmental
areas such as bioinformatics and biostatistics.
•
The department has recently started hiring visiting assistant professors in research areas that
mesh well with the interests of current faculty. We propose to continue doing this. Visiting
assistant professors who are well positioned to collaborate with departmental faculty can greatly
enhance productivity.
•
Strongly encourage faculty to pursue external funding. There has been substantial growth in
external grants and grant applications. We propose to strongly encourage faculty to pursue
external funding.
•
Balance the emphasis on fast publication and external funding against the need to provide
conditions that allow faculty to pursue time-consuming projects of high prestige, such as
writing books for the main mathematics and statistics publishers, or working on open problems
at the forefront of research in both disciplinary and interdisciplinary research that could take
several years in completion. This is a difficult issue on which to provide precise prescriptions,
but it is something that should not be overlooked. It is an essential consideration when
considering the quality and impact of research within the department and when assessing the
contributins of faculty.
•
Pursue 2CI hiring to bring research leaders to the department as well as funds to support Ph.D.
students. The department has already been pursuing this and is currently participating in such a
hire.
•
Continue active participation in GSU interdisciplinary initiatives, such as the Brains and
Behavior program, the Molecular Basis of Disease Program, and the Neuroscience Institute.
Departmental participation in these areas has already had a positive impact on faculty
collaborations, grants, and grant proposals.
Appendices
.5 Data on Freshman Math Majors
Avg HS GPA
FA2012
FA2011
FA2010
FA2009
3.44
3.59
3.49
3.44
Avg Freshman
Index
2895
2959
2858
2848
Avg SAT
Avg ACT
1227
1181
1113
1152
22
24
24
23
.6 Assessment Reports
.6.a General Learning Outcomes
General Education Learning Outcomes in the Core Report of Mathematics & Statistics
Date: August 30, 2012
By Yichuan Zhao.
The report is based on the assessment report by Dr. Valerie Miller before. Yichuan Zhao
acknowledges the report and the established assessment procedure by Dr. Miller. He also
appreciates the work by all five coordinators of core courses.
General Education Outcome Assessed: Quantitative Literacy
Core Courses Assessed: Math1070; Math1101; Math 1111; Math 1113; Math 2211
Description of Core Assessment Plan for the 2012-20013 academic year
• Description of student behavior(s) to be assessed,
• Brief description of assessment methods,
• Description of data collection and analysis,
• Plan for having your department review the results and implement any curricular changes).
Report of assessment data for the 2011-2012 academic year: Provide a brief summary of the results
from 2011 – 2012 assessments on student learning as described in plan.
Quantitative Literacy Assessment Plans for 2011-2012
Goal of Improvement of Instruction Committee: To develop a plan for assessing Goal V of the
university’s general learning outcomes. Specifically, develop a plan to evaluate students enrolled in
MATH 1101, 1111, 1113, 2211 (the four courses most often used by students in Area A of the
core) and MATH 1070 (the most frequently taken non-Area A course) in
Goal V. Quantitative Literacy
1. Students effectively apply symbolic representations to model and solve problems.
2. Students express and manipulate mathematical information, concepts, and thoughts in verbal,
numeric, graphical and symbolic forms while solving a variety of problems.
These elements are 2 of 5 elements of quantitative literacy (QL) in the new Board of Regents General
Education Goals started in fall 2011, the area in the core course is A2 Quantitative Outcomes.
Learning Goal A2 Quantitative Outcomes
.
Students demonstrate a strong foundation in mathematical concepts, processes, and structure.
.
Students effectively apply symbolic representations to model and solve problems.
.
Students model situations from a variety of settings in generalized mathematical forms.
.
Students express and manipulate mathematical information, concepts, and thoughts in verbal,
numeric, graphical and symbolic forms while solving a variety of problems.
.
Students solve multiple-step problems through different (inductive, deductive and symbolic)
modes of reasoning.
In the assessment we require a mathematics placement test be given to each new freshman or transfer
student without Area A credit. This proposed test assesses a student’s current algebra and trigonometry
skill levels and prerequisite checking ensures that students can only register for classes that they are
prepared to take. Our goal is that the success level will be 70% or higher.
The department depends on the many different instructors of these classes to upload a ULearn quiz into
their respective courses and then export the results and send them to the assessment coordinator in
Math2211. In Fall 2009 coordinator level templates are being created in uLearn for Math2211. The QL
quizzes will be placed in the coordinator course so that when an instructor opens their course for the first
time these quizzes will automatically be put in place.
Below is the current assessment plan:
1) Pre/Post testing of student abilities basic quantitative literacy.
Our idea was to test during the first week, middle of the semester as well as at the end. This
would tell us the length of time associated with their learning. We have currently
implemented the first two weeks and end of the semester quizzing. Regular course embedded
assessments are used for the “middle of the semester” time. We intend on studying how to
improve this by tracking those students that progress through lower level sequences.
2) Technology Component.
Effective fall 2008, Math 1111 and 1113 changed to a “modified emporium” model of instruction.
Students meet with their instructor once a week for 1 hour and are required to be in the MILE for
a minimum of 3 hours each week. Students meet with their instructor once a week for 50 minutes,
effective fall 2010.
Effective fall 2010, Math 1070 changed to a “modified emporium” model of instruction. Students
meet with their instructor once a week for 50 minutes and are required to be in the MILE for a
minimum of 3 hours each week.
Math 1101 and 1070 implemented a more uniform requirement of projects requiring the use of
technology (most often Microsoft Excel) in their solution. The use of a word processor or
presentation software for presenting the results was required in some sections, but not all.
Description of Core Assessment Plan for the 2012-2013 academic year
• Description of student behavior(s) to be assessed
We will attempt to measure our students ability to perform arithmetic operations, as well as reason
and draw appropriate conclusions from numerical information. Some of these problems will ask
students to translate problem situations into their symbolic representations and use those
representations to solve problems.
• Brief description of assessment methods, i.e., tests, scoring rubrics, etc. used to evaluate student
learning.
Assessment quizzes will be administered at the beginning and end of each semester via either
MyMathLab (Math1070, Math1101, Math1111, Math1113) or uLearn (Math2211).
We will simplify the process of using uLearn (Math2211) having a course designer
section of all these classes be given the assessments so that the instructors of the
individual sections may import it into theirs. We ask the instructors to have their students
complete the quiz and report the data, and we wish to get a better sense of how to literate
our students are both before they take our classes and after.
The QL online quizzes in uLearn are graded right or wrong so no other rubric is needed. Partial
credit is given in MML for questions requiring more than a single answer, but we do not include
those in the comparisons.
Targets for the QL quizzes:
• 50% response rate
• 70% success rate
The coordinators of each of these five classes will collect data during the semester and from the
final exams to get formative assessment data. The final exams will be consistent with the content
standards so that data can be collected.
The course coordinator will complete an alignment/performance table of student success on each
of the learning outcomes using item analysis. This feature is readily available in the software
package, MML. The categories for this table are
Totally Correct
Partially Correct
Totally Incorrect
No Response
This information will be combined to produce a student success on each standard. "Success in
Achieving the Learning Outcomes" will be determined as follows:
A Boolean approach is implemented using the following:
Totally Correct = 100%;
Partially Correct = 70%;
Totally Incorrect or No Answer = 0%
The “Success Rate” is calculated as
1.0*(Totally Correct %) + .7*(Partially Correct %)
Examples:
1. Using 50% Totally Correct AND 75% Totally or Partially Correct (25%
Partially Correct) would equate to a total of 67.5%, which is "almost a low C".
2. Using 50% Totally Correct AND 80% Totally or Partially Correct (30%
Partially Correct) would equate to at total of 71%, which would be a low C.
The targeted “Success Rate” is 70% on each standard.
• Description of data collection and analysis—including projected number of students to be assessed,
Item analyses will be performed on the outcome of each assessment (both QL quizzes and course
assessments), both aggregated and disaggregated by class. It is expected that the assessment data will be
available for ½ of the enrolled students (unless the assessments are required course
elements). We can express it as follows.
Expected Enrollment
Expected # of Responses
Course
Fall
Spring
Fall
Spring
MATH 1070
1500
1500
750
750
MATH 1101
1500
1000
750
500
MATH 1111
800
600
400
300
MATH 1113
580
580
290
290
MATH 2211
460
460
230
230
Total
4840
4140
2420
2070
• Plan for having your department review the results and implement any curricular or instructional
changes.
The Improvement of Instruction Committee will review the results of the assessments at the end
of each semester and during the summer semester and will make recommendations on
modification of the instrument, curriculum, and/or instruction for the succeeding terms. The
course coordinators of each of the individual courses will review the results of the assessments at
the end of each semester and will make recommendations on modification of the instrument,
curriculum, and/or instruction for the succeeding term.
Results
Quantitative Literacy quizzes were made available to all Math 1070, 1101, 1111, 1113, and 2211 students
this past academic year (both at the beginning of the semester and at the end of the semester). Completing
the quizzes was voluntary with bonus points to tests awarded for each correct response. It was thought
that this would encourage students to engage the assessment with an honest effort.
“Pre-Assessment Quiz”
The following questions were on the Pre-QL quiz. All numbers were algorithmically generated in MML
and multiple versions of the problem were available in ULearn.
1. Inscribed Square – Suppose you are given a circle with an inscribed square as shown below. If
the area of the square is 49 m2 what is the area of the circle? (Use 3.14159 for .)
_____ m2
2. Cars – Jim and Jack were curious about the number of red cars they saw each day. One
Saturday they decided to keep track of the red cars that passed their house each hour. They
recorded their findings in the table below.
Hour
Number of Red Cars
1
5
2
10
3
7
4
3
5
7
What was the average number of red cars that they saw each hour?
3. Coin Toss Tree Diagram – Use the tree diagram below to predict the probability of flipping 3
coins and getting all heads or all tails.
a. 1
b. ½
c. ¼
d. 2
4. Ducks and Cows – Farmer Brown had ducks and cows. One day she noticed that the animals
had a total of 16 heads and 36 feet. How many of the animals were ducks and how many were
cows?
_____ ducks _____ cows
5. Fish – The number of tropical fish that an aquarium can hold depends on the volume of the fish
tank. The interior dimensions of the fish tank are 20 cm, 40 cm, and 50 cm. Each fish requires
10,000 cubic centimeters of water. How many tropical fish will this tank hold?
6. Pie Chart – The pie chart below represents the number of students enrolled in each course at
Tri-Cities High School. Using this information, find the percentage of students enrolled in
Math. Round your answer to the nearest tenth of a percent.
What percentage of students are enrolled in Math? ____ %
(Round your answer to the nearest tenth of a percent.)
7. Elevator – The capacity of an elevator is either 15 children or 11 adults. If 8 children are
currently in the elevator, how many adults can still get in?
____ adults can still get in the elevator
Alignment with QL: All of these questions ask students to
• effectively perform arithmetic operations, as well as reason and draw appropriate conclusions
from numerical information.
• effectively translate problem situations into their symbolic representations and use those
representations to solve problems.
Post QL Assessment –
1. Basketball Mean – If the mean number of people who attended six basketball games is 7380,
what was the total attendance at the six games?
The total attendance at the six games was ______ .
2. Elevator – The capacity of an elevator is either 25 children or 19 adults. If 11 children are
currently in the elevator, how many adults can still get in?
3. Fish – The number of tropical fish that an aquarium can hold depends on the volume of the fish
tank. The interior dimensions of the fish tank are 20 cm, 30 cm, and 50 cm. Each fish requires
10,000 cubic centimeters of water. How many tropical fish will this tank hold?
4. Pie Chart – The pie chart below represents the number of students enrolled in each course at TriCities High School. Using this information, find the percentage of students enrolled in Math.
Round your answer to the nearest tenth of a percent.
What percentage of students are enrolled in Math? ____ %
(Round your answer to the nearest tenth of a percent.)
5. Coin Toss Tree Diagram – Use the tree diagram below to predict the probability of flipping 3
coins and getting all heads or all tails.
a. 1
b. ½
c. ¼
d. 2
6. Wilma and Betty - Two neighbors, Wilma and Betty, each have a swimming pool. Both Wilma’s
and Betty’s pools hold 9000 gallons of water. If Wilma’s garden hose fills at a rate of 800 gallons
per hour while Betty’s garden hose fills at a rate of 500 gallons per hour, how much longer does
it take Betty to fill her pool than Wilma?
It takes Betty ____ hours and ____ minutes longer to fill her pool than Wilma.
(Round your answer to the nearest minute.)
7. Inscribed Circle - Suppose you are given a square with an inscribed circle as shown below. If
the area of the square is 36 m2 what is the area of the circle? (Use 3.14159 for .)
Results are displayed in the following tables:
Fall PreQL % Correct
Spring PreQL % Correct
Course
1070 1101 1111 1113 2211 Together
1070 1101 1111 1113 2211 Together
Enrolled
1358 1163 642 562 389
4114
1484 845 428
472
359 3588
Number of respondents
1070
275 503 352 223
2423
1034 279 318
366 159 2156 Response
Rate
78.8
23.6 78.3 62.6 57.3 58.9
69.7 33.0 74.3 77.5 44.3 60. 1
Geometry
Means
Probability
Pie Charts
Ratios
40.8
76.9
61.8
70.4
36.2
39.6 48.5 51.1
79.8 83.5 84.7
60.6 67.6 72.7
59.9 68.1 76.7
33.6 39.5 48.0
64.6 45.3
91.0 80.7
24.2 59.8
76.2 69.4
58.3 39.7
42.4 30.8 44.9
81.4 79.7 85.1
63.3 53.8 63.1
72.6 57.9 70.6
41.0 32.5 36.4
54.6 63.5 45.5
87.7 91.8 83.7
76.8 31.5 61.0
79.0 79.3 72.4
48.1 67.9 43.4
Reasonable Answers
Linear Equations
75.0
77.4
79.0 82.3
85.1 87.1
82.7 93.7
82.4 91.9
79.6
82.4
77.7 68.6
80.9 66.9
Fall PostQL % Correct
Course
Remaining students
Number of respondents
Response Rate
Geometry
Means
Probability
Pie Charts
Ratios
Reasonable Answers
Linear Equations
1070 1101
1259 1075
847
397
67.3 36.9
Means: Cars and Basketball
Probability: Coin Toss
Reasonable Answers: Aquarium
Ratios: Elevator
62.1
92.3
85.4
83.2
61.5
92.6
65.7
59.4
98.1
57.6
76.6
67.1
94.3
62.0
86.6 91.2 79.9
84.7 93 .1 81.8
Spring PostQL % Correct
1111 1113 2211 Together
642
514 331
3821
437
364 158
2203
68.1 70.8 47.7 57.7
55.5 41.2 63.8
86.0 74.2 95.3
76.4 61.5 82.8
78.6 64.8 81.8
50.1 37.4 60.4
84.9 79.8 92.0
67.8 58.5 76.3
82.1
85.9
56.6
88.6
74.4
77.9
54.1
87.7
67.0
1070 1101 1111 1113 2211 Together
1274 759 428 500
294
3255
775 238 285 334
128
1760
60.8
31.4 66.6 66.8 43.5
54.1
54.6
88.1
76.6
77.7
52.4
85.2
68.6
36.7 57.6 61.4 57.8
75.5 89.8 92.5 94.5
59.1 75.1 87.4 53.1
64.6 77.6 84.4 81.3
29.5 58.2 64.1 60.9
69.0 89.9 93.7 92.9
56.0 71.5 62.6 58.6
53.0
87.2
73.3
76.7
51.4
84.7
64.8
Pie Chart: High School Enrollments
Linear Equations: Cows and Ducks & Betty and Wilma
Geometry: Inscribed Circle/Square
Our goal of response rates of 50% were met by Math 1070, 1111, 1113 in the fall and spring. Math
1070, Math1111 and Math1113 can access the quiz report very easily. The response rate for Math 1101
was not good in Fall 2011 and Spring 2012. The response for Math2211 is around 50%. Some
instructors in Math2211 were unwilling to pull the data by hand as this is an extremely time
consuming. The change in success rates are reported in following tables.
Fall 2011
Geometry
Means
Probability
Pie Charts
Ratios
Reasonable Answers
Linear Equations
1070
14.7
9.1
14.6
8.2
13.9
9.9
-9.6
1101
1.6
-5.6
0.9
4.9
3.8
0.8
-26.6
Change in Success
1111
1113
15.3
11.0
11.8
7.6
15.2
12.7
13.7
6.5
20.9
13.5
9.7
9.9
-10.8
-16.7
2211
-5.2
7.1
33.4
0.4
8.8
0.6
-29.9
1070
1101
Change in Success
1111
1113
2211
Spring 2012
Geometry
Means
Probability
Pie Charts
Ratios
Reasonable Answers
Linear Equations
12.2
6.7
13.3
5.1
11.4
7.5
-12.3
5.9
-4.2
5.3
6.7
-3.0
0.4
-10.9
12.7
4.7
12.0
7.0
21.8
7.8
-14.4
6.8
4.8
10.6
5.4
16.0
7.1
-22.1
-5.7
2.7
21.6
2.0
-7.0
1.7
-34.5
Summary of the tables
We can see from the tables above, the “Betty and Wilma” problem is more difficult by students than
the “Ducks and Cows” problem. The most common error is the conversion of a decimal hour to
minutes. MML which gives partial credit on this problem for the correct number of hours. But, uLearn
would mark this completely wrong if either part is incorrect.
The results of the “Coin Toss” problem are positive, since probability is a prominent subject in
elementary statistics Math1070. As can be seen for both Fall semester 2011 and Spring semester 2012
the success rate increases from less than 70% on the pre QL's to above 70% on the Post QL. This
gives clear evidence that Math 1070 helps the students improve their quantitative reasoning for these
types of concepts. Though probability is not a topic covered in four of the five classes, an
improvement on this question is clear at the end of the semester. Further analysis needs to be done in
order to determine if the basic problem solving skills that are developed during the class or the diagram
for the probability question that led to this improved student performance on this question for
Math1070 students. It is interesting to note that the two classes that had formal Problem Solving
Activities (Math1111 and Math1113) often outperformed students in Math2211. Though probability is
not a topic covered in Math1113, it is clear to see an improvement on this question. As students
complete the Problem Solving Activities (in Math 1113) during the semester, it is possible students
might have developed analytical abilities to solve those QL problems and their performance shows an
improvement at the end of the semester.
From the tables we find that in more than half cases students are reaching the success rate of 70% on
these activities. In addition, we have seen improvement in the performance of students from the Preto the Post QL tests. In particular, we see the improvement of Math1070 compared to that of Math1070
last year. Since we adopted a new model. Math1101 has not reached the success rate of 70% in most
cases. There is a big room for the improvement.
Our department had a target of 50% response (these are voluntary quizzes) and this target was met in
Math 1070, 1111,1113 both semesters. If we try the same teaching model of Math111, Math1113,
Math1070 for Math1101, the success rate will reach 70%. It was hoped that using the “course
coordinator” course would help these response rates, but too often the quizzes did not “drop” down to
the individual instructor classes and so had to be manually uploaded.
Action Plan
1. Better communicate with all instructors the importance of providing the students with these QL
Quizzes since some instructors do not import the QL quizzes to students.
2. Math1070 has improved the students’ performance. The response rate is also higher. We will
continue to enhance the rate.
3. During the summer semester, the results of these assessments (exams and projects) as well as actual
student work will be reviewed by the coordinating committee of Math 1113 to determine the
appropriateness of the questions, projects, and resultant student success levels. These results will
determine what, if any, support needs to be provided to further student learning. The coordinator will
continue the assessment and validation of each homework, quiz and test questions and make necessary
modifications when needed.
4. In Math1101, an effective way to engage students in the learning process is to use online
software for homework and quizzes on which students can complete on their own pace within a set up
time frame. We adopted MyMathLab to deliver the online assignments. It shows some improvement.
We will continue to use it and try to improve the response rate and students’ performance.
5. Track students to see how many are progressing from Math 1113 to 2211 to see if “seeing” the
quizzes more than once is inflating the success rates of later classes.
6. Seek new ways to improve response rates for Math1101 and Math2211. The new system of
Math2211that is going to replace uLearn will be more user-friendly -- in particular, hopefully it's more
convenient for generating reports.
7. Determine the effects of the Problem Solving Activities in Math 1111 and 1113 on the Post-QL quiz.
8. Determine the effect of excel projects on the Post-QL quiz.
9. For projects that required Excel, some instructors provided their students the opportunity to attend
Excel seminars in the Mathematics Interactive Learning Environment, while others simply provided
handouts (these handouts were available to all instructors). An analysis and comparison of student
performance based on these two activities and the impact they had will be performed this summer.
Additional seminars can be scheduled if they are found to be beneficial to student performance.
10. Ways to improve student communication of their findings will be determined and implemented in
the fall semester. Many instructors required the responses to be “typed up” using a Word Processor
(e.g., Microsoft Word) and others required in-class presentations with PowerPoint.
11. A close supervision of GTAs has been performed by the coordinator.
12. Supplemental study group sessions were held in the MILE. Experienced SAs and GLAs, some
instructors, and the coordinator conducted those study groups under the supervision of the coordinator.
13. The textbook will be customized for GA State University and some online assignment additions
will be made and coordinator’s class notes are published to enhance student learning and provide a
grant.
.6.b Pre and Post QL Data
The following gives success rates on quizzes on quantitative skills given at the beginning of the
semester (pre-QL) and at the end of the semester (post-QL).
2011-2012
Fall PreQL % Correct
Course
Enrolled
Number of respondents
Response Rate
1070 1101 1111 1113 2211 Together
1358 1163 642 562 389
4114
1070
275 503 352 223
2423
78.8
23.6 78.3 62.6 57.3 58.9
Geometry
Means
Probability
Pie Charts
Ratios
Reasonable Answers
Linear Equations
40.8
76.9
61.8
70.4
36.2
75.0
77.4
39.6
79.8
60.6
59.9
33.6
79.0
85.1
48.5
83.5
67.6
68.1
39.5
82.3
87.1
51.1
84.7
72.7
76.7
48.0
82.7
82.4
64.6 45.3
91.0 80.7
24.2 59.8
76.2 69.4
58.3 39.7
93.7 79.6
91.9 82.4
Spring PreQL % Correct
1070
1484
1034
69.7
1101
845
279
33.0
1111 1113 2211 Together
428
472
359 3588
318
366 159 2156
74.3 77.5 44.3 60.1
42.4 30.8 44.9
81.4 79.7 85.1
63.3 53.8 63.1
72.6 57.9 70.6
41.0 32.5 36.4
77.7 68.6 82.1
80.9 66.9 85.9
54.6 63.5 45.5
87 .7 91.8 83.7
76.8 31.5 61.0
79.0 79.3 72.4
48.1 67.9 43.4
86.6 91.2 79.9
84.7 93 .1 81.8
2010-2011
Fall PreQL % Correct
Course
Enrolled
Number of respondents
Response Rate
1070 1101 1111 1113 2211 Together
1390 1422 602
542 436
4392
1029
369 405 202 226
2231
74.0 25.9 67.2 37.3 51.8 50.8
Geometry
Means
Probability
Pie Charts
Ratios
Reasonable Answers
40.7
80.5
64.0
73.7
37.2
73.1
25.7 50.4 60.4
81.0 86.0 86.6
15.7 64.0 71.3
44.2 70.4 79.2
28.7 37.4 52.0
65.9 85.8 87.6
59.3 43.6
91.2 83.2
23.9 52.6
75.7 68.9
57.1 39.2
85.8 76.8
Spring PreQL % Correct
1070 1101 1111 1113 2211 Together
1474 918 389 471 322 3574
1065 227 251 317 215 2075
72.3 24.7 64.5 67.3 66.8 58.1
46.0 34.4 53.2
83.7 79.5 87.8
67.2 57.8 65.5
73.0 65.9 72.9
40.2 29.5 42.1
79.7 72.6 82.8
56.5 6 4.2 49.1
89. 0 93.0 83.5
76.0 40.5 64.5
82.6 78.6 74.3
47.9 63.3 42.8
88.3 91.6 81.8
Linear Equations
80.3
69.6 86.1
87.6
93.4
81.6
82.8 77.7
86.8
88.0 93.0 84.6
2009-2010
Fall PreQL % Correct
Spring PreQL % Correct
Course
Enrolled
Number of respondents
Response Rate
1070 1101 1111 1113 2211 Together
1258 1578 684
545 465
4530
256
342 412 353 219
1582
34.9 21.7 60.2 64.7 47.1
34.9
Geometry
Means
Probability
Marathon
Ratios
Reasonable Answers
Linear Equations
30.2
84.3
17.4
11.1
37.0
64.3
76.2
21.4
84.5
9.7
16.5
25.6
64.4
71.8
46.8
87.1
61.5
65.7
33.8
84.2
84.7
Means: Cars and Basketball
Probability: Coin Toss
Reasonable Answers: Aquarium
Ratios: Elevator
55.0
86.1
67.4
75.9
40.5
84.1
86.7
60.2 42.3
73.6 83.9
24.7 39.3
82.4 50.8
53.1 36.7
73.9 75.2
85.7 81.1
Geometry
Means
Probability
Pie Charts
Ratios
Reasonable Answers
Linear Equations
33.5
83.6
20.9
53.5
36.7
69.2
77.6
21.3
81.3
11.1
41.6
33.0
64.8
69.8
43.2
83.0
59 .3
69.2
29.7
75.4
83.2
62.3 58.7 41.4
88.8 76.2 83.0
71.3 32.9 36.1
81.4 71.5 61.2
46.3 54.6 39.1
87.9 81.5 7 4.5
88.5 86.3 80.1
Pie Chart: High School Enrollments
Linear Equations: Cows and Ducks & Betty and Wilma
Geometry: Inscribed Circle/Square
Fall PostQL % Correct
Course
Remaining students
Number of respondents
Response Rate
1070 1101 1111 1113 2211 Together
1576 911 341 481 4 44 3753
478 337 236
313 206 1570
30.3 36.9 69.2 65.1 46.3 41.8
1070 1101
1259 1075
847
397
67.3 36.9
Spring PostQL % Correct
1111 1113 2211 Together
642
514 331
3821
437
364 158
2203
68.1 70.8 47.7 57.7
55.5 41.2 63.8
86.0 74.2 95.3
76.4 61.5 82.8
78.6 64.8 81.8
50.1 37.4 60.4
84.9 79.8 92.0
67.8 58.5 76.3
62.1
92.3
85.4
83.2
61.5
92.6
65.7
59.4
98.1
57.6
76.6
67.1
94.3
62.0
56.6
88.6
74.4
77.9
54.1
87.7
67.0
1070 1101 1111 1113 2211 Together
1274 759 428 500
294
3255
775 238 285 334
128
1760
60.8
31.4 66.6 66.8 43.5
54.1
54.6
88.1
76.6
77.7
52.4
85.2
68.6
36.7 57.6 61.4 57.8
75.5 89.8 92.5 94.5
59.1 75.1 87.4 53.1
64.6 77.6 84.4 81.3
29.5 58.2 64.1 60.9
69.0 89.9 93.7 92.9
56.0 71.5 62.6 58 .6
2010-2011
Fall PostQL % Correct
Spring PostQL % Correct
53.0
87.2
73.3
76.7
51.4
84.7
64.8
Course
Remaining students
Number of respondents
Response Rate
Geometry
Means
Probability
Pie Charts
Ratios
Reasonable Answers
Linear Equations
1070 1101
1206 1297
831
215
68.9 16.6
62.8
89.2
81.7
84.7
56.4
81.7
69.8
15.3
78.6
26.5
41.9
33.0
71.6
18.1
1111 1113 2211 Together
560
502 345
3910
292
253 241
1832
52.1 50.4 69.8 46.9
62.4 67.6
89.3 94.1
75.6 83.0
86.5 85.4
57.9 60.9
92.7 92.1
76.9 65.6
58.9
96.3
51.0
73.9
66.0
88.8
63.9
57.3
89.6
70.4
78.6
55.8
84.6
63.5
1070 1101 1111 1113 2211 Together
1320 828 360 432
266
3206
906
365
211 339 148
1969
68.6
44.1 58.6 78.5 55.6
61.4
62.2
88.6
79.0
86.2
51.4
85.7
68.8
39.0
74.8
53.5
61.7
33.9
73.6
56.4
62.1
91.9
77.3
81.7
58.9
92.6
76.4
67.6 57.4
92.3 97.9
87.9 60.8
85.3 73.7
65.5 69.6
92.9 91.2
65.8 64.9
58.5
87.7
74.3
80.1
52.8
85.8
66.5
2009-2010
Fall PostQL % Correct
Course
Remaining students
Number of respondents
Response Rate
Geometry
Means
Probability
Marathon
Ratios
Reasonable Answers
Linear Equations
Spring PostQL % Correct
1070 1101 1111 1113 2211 Together
1096 1473 625
477 283
3954
186
234 357
305 127
1209
16.9 15.8 57.1 63.9 44.8
30.5
28.2 15.9 67.3 77.7
81.6 75.0 87.7 92.1
22.7 17.3 75.9 79.7
52.0 43.4 75.1 89.2
42.5 28.4 51.2 59.0
72.9 72.3 89.9 92.8
28.2 17.4 75.6 68.2
Means: Cars and Basketball
Probability: Coin Toss
Reasonable Answers: Aquarium
Ratios: Elevator
63.5
94.5
47.8
76.1
49.3
86.3
50.6
53.5
86.1
54.3
69.0
47.2
84.2
52.5
1070 1101 1111 1113 2211 Together
1435 812 314 433
288
3282
341 229 177 269
132
1148
23.7 28.2 56.3 62.1 45.8
34.9
26.3
82.9
33.4
52.6
44.9
79.5
33.1
23.4 60.6 68.4 59.3
81.8 89.1 92.6 91.7
26.2 81.9 86.0 42.7
45.3 81.8 87.7 70.8
40.7 57.4 59.5 47.9
72.4 91.9 94.8 82.9
29.9 73.6 63.9 47. 2
44.6
86.9
52.8
65.9
49.7
83.9
47.5
Pie Chart: High School Enrollments
Linear Equations: Cows and Ducks & Betty and Wilma
Geometry: Inscribed Circle/Square
Fall 2011
Geometry
Means
Probability
Pie Charts
Ratios
Reasonable Answers
Linear Equations
1070
14.7
9.1
14.6
8.2
13.9
9.9
-9.6
1101
1.6
-5.6
0.9
4.9
3.8
0.8
-26.6
Change in Success
1111
1113
15.3
11.0
11.8
7.6
15.2
12.7
13.7
6.5
20.9
13.5
9.7
9.9
-10.8
-16.7
2211
-5.2
7.1
33.4
0.4
8.8
0.6
-29.9
1101
5.9
-4.2
5.3
6.7
-3.0
0.4
-10.9
Change in Success
1111
1113
12.7
6.8
4.7
4.8
12.0
10.6
7.0
5.4
21.8
16.0
7.8
7.1
-14.4
-22.1
2211
-5.7
2.7
21.6
2.0
-7.0
1.7
-34.5
Spring 2012
Geometry
Means
Probability
Pie Charts
Ratios
Reasonable Answers
Linear Equations
1070
12.2
6.7
13.3
5.1
11.4
7.5
-12.3
.6.c Math 2211 Calculus I Assessment
.6.d Math 1113 Precalculus Assessment
.6.e Math 1111 College Algebra Assessment
.7 Retention and Graduation
Total
Fresh
men
FA 06
1-Yr
Enrol
led
FA 07
2-Yr
Enrol
led
FA 08
3-Yr
Enroll
ed
FA 09
3-Yr
Grad
_By
FA
09
15
12
11
9
0
100%
80.0%
73.3%
60.0%
0%
4Yr
Enr
olle
d
FA
10
4
26.7
%
3-Yr
Total
Retai
ned
9
60.0
%
Total
Fresh
men
FA 07
1-Yr
Enrol
led
FA 08
2-Yr
Enrol
led
FA 09
3-Yr
Enroll
ed
FA 10
3-Yr
Grad
_By
FA
10
21
17
10
9
0
9
100%
81.0%
47.6%
42.9%
0%
42.9%
3-Yr
Total
Retain
ed
4Yr
Gra
d_B
y
FA
10
3
20.0
%
4Yr
Gr
ad
_B
y
FA
11
1
4.8
%
4Yr
Enr
olle
d
FA
11
8
38.1
%
4Yr
Tot
al
Ret
aine
d
7
46.7
%
5Yr
Enr
olle
d
FA
11
2
13.3
%
5Yr
Gra
d_B
y
FA
11
7
46.7
%
5Yr
Tota
l
Ret
aine
d
9
60.0
%
6Yr
En
rol
led
FA
12
0
6Yr
Gra
d_B
y
FA
12
8
53.3
%
4Yr
Tot
al
Ret
aine
d
5Yr
En
roll
ed
FA
12
5Yr
Gra
d_B
y
FA
12
5Yr
Tota
l
Ret
aine
d
6Yr
En
rol
led
FA
13
6Yr
Gra
d_B
y
FA
13
9
42.9
%
2
9.5
%
5
23.8
%
7
33.3
%
2
9.5
%
6
28.6
%
0%
Total
Fresh
men
FA 08
1-Yr
Enrol
led
FA 09
2-Yr
Enrol
led
FA 10
3-Yr
Enroll
ed
FA 11
3-Yr
Grad
_By
FA
11
3-Yr
Total
Retain
ed
4-Yr
Enrol
led
FA 12
4-Yr
Grad
_By
FA 12
4-Yr
Total
Retai
ned
5-Yr
Enro
lled
FA
13
5-Yr
Grad_
By
FA 13
21
14
8
8
0
8
4
3
7
2
4
100%
66.7%
38.1%
38.1%
0%
38.1%
19.0%
14.3%
33.3%
9.5%
19.0%
Total
Fresh
men
FA 09
1-Yr
Enrol
led
FA 10
2-Yr
Enrol
led
FA 11
3-Yr
Enroll
ed
FA 12
27
100%
22
81.5%
16
59.3%
16
59.3%
3-Yr
Grad
_By
FA
12
0
0%
3-Yr
Total
Retain
ed
4-Yr
Enrol
led
FA 13
4-Yr
Grad
_By
FA 13
4-Yr
Total
Retai
ned
16
59.3%
10
37.0%
6
22.2%
16
59.3%
.8 Ethnic and Gender Diversity of Undergraduates
5Yr
Tota
l
Reta
ined
6
28.6
%
6Yr
Tota
l
Reta
ined
8
53.3
%
6-Yr
Total
Retai
ned
8
38.1%
Ethnic Composition of Mathematics Undergraduate Students
Term
Asian
Black
White
Natv HI/Pa Isld
Am Ind/AA Natv
Not Reported
Multi-Racial
FA 2008
18
59
72
1
1
13
7
FA 2009
28
61
67
1
1
8
2
FA 2010
36
74
81
1
1
10
8
FA 2011
27
83
69
1
2
14
9
FA 2012
30
99
67
1
3
18
9
Ethnic Composition of All Undergraduate Students
Term
Asian
Black
White
Natv HI/Pa Isld
Am Ind/AA Natv
Not Reported
Multi-Racial
FA 2012
4059
11460
13283
41
97
1920
1232
Male
Female
FA 2008
64%
36%
FA2009
60%
40%
FA2010
61%
39%
FA2011
58%
42%
FA2012
61%
39%
.9 Student Survey Data
.9.a Undergraduate Alumni Surveys
November 2013
N = 11
Response rate = 24%
General Outcomes
Table 1.
1. My program of study has made a positive contribution to the quality of my life.
2. I have applied the skills I learned in my program to help resolve issues I've faced in my professional life.
3. Overall I was satisfied with my degree program.
SD
1.
2
3
4
5
SA
%
%
%
%
%
%
N
9.1
0.0
0.0
0.0
18.2
72.7
11
2.
0.0
9.1
0.0
9.1
18.2
63.6
11
3.
0.0
10.0
0.0
10.0
30.0
50.0
10
Note. Mean scale: 1=Strongly disagree to 6=Strongly agree.
Employment
Table 2.
Table 3.
Are you currently employed?
Have you been employed at any time over the last year?
N = 11
Yes
No
N=1
Yes
No
%
90.9
9.1
%
0.0
100.0
Table 4.
Please indicate the general area of employment.
N=8
%
Agriculture/Natural Resources
Arts
Business/Finance
College Faculty/Administration
Counseling/Mental Health
Education K-12
Government/Public Administration
Hospitality/Tourism
Journalism/Publication
Law
Library Work
Manufacturing/Construction
Marketing
Media/Communication
Medicine/Nursing
Non-Profit or Community Org.
Religious Organization
Transportation
Other
Other: N=4
Information Technology
Medical Insurance
Retail
Software Development
Table 5. Skills and Employment
The following questions focus on the skills you may have learned in your degree program at Georgia State and
whether you listed them on your resume, discussed them during your job interview, or use(used) them in your job.
List on
resume
Yes
%
Using(us
ed) on
job
No
Yes
%
%
No
%
Yes
%
Research skills
70.0
30.0
40.0
60.0
Communication skills
(writing and speaking)
Ability to interpret
data/information in a
critical manner.
50.0
50.0
90.0
10.0
100.0
90.0
10.0
90.0
10.0
90.0
Ability to analyze
problems from
different
perspectives
80.0
20.0
100.0
0.0
100.0
Ability to work with
diverse populations
60.0
40.0
70.0
30.0
80.0
N = 10
Discuss in
job interview
60.0
Further Education
Table 6.
Table 7.
Are you currently enrolled in a graduate program?
N = 11
Yes
No
N=2
Ed.D.
J.D.
M.A.
M.B.A.
M.D.
M.Div.
M.F.A.
M.S.
M.S.W.
M.S.L.S.
M.T.S.
Ph.D.
Th.D.
Other
What is your program of study? (N = 2)
What degree are you seeking?
%
18.2
81.8
%
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
100.0
0.0
0.0
Bioinformatics
Operations Research
At what institution are you pursuing your degree? (N = 2)
Georgia State University
North Carolina State University
Table 8.
Since graduating from Georgia State,
have you earned an additional degree(s)?
N = 11
Yes
No
%
0.0
100.0
Qualitative Comments:
The following statements are in response to the comment sections in the online questionnaire. All
responses are exported directly into a Word document without any changes to wording,
punctuation, or grammar. Please note that each paragraph represents one respondent’s
comments.
Looking back, what aspects of your program do you believe were the most valuable in contributing to your earning a degree
at Georgia State?
MATH 3000 was a big turning point in my academic career, as it gave me the ability to communicate in the language of
mathematics more effectively. (1 Count)
The teachers were helpful. (1 Count)
Because I am pursuing a math heavy degree; theory of math and stat i and II, Analysis, all calculus classes, linear algebra,
and discrete math. Basically every math class I ever took. (1 Count)
Meeting with advisers to receive concerning my career path. (1 Count)
Senior Seminar class which exposed me how I could apply math in other fields and how to do research. There is also
Analysis class which exposed me to different ways of thinking compared to traditional ways to solve math problems. (1
Count)
The Intro to Higher Mathematics class was the most helpful. Having a class that focused on proof techniques helped me
tremendously in later courses. (1 Count)
I believe that the most valuable aspects of my program was the critical thinking through writing classes I took. Especially,
Senior Seminar. This class allowed me to research and present my findings with classmates from different backgrounds.
And as a result of that class, I was able to apply these skills to my other classes enabling me to complete my program (1
Count)
The access to various faculty in the department to explore personal mathematics related interest outside of the
curriculum. (1 Count)
What kinds of improvements would you suggest the department make in order to enhance the educational experience of
current students in the program?
I feel that there needs to be a stronger emphasis on technology in Mathematics application (Python/Numpy, Matlab, etc). (1
Count)
I think the senior seminar class was a complete waste, could have been implemented better. With bridge to higher math and
proofs I think it would be beneficial to have training with some mathematical computing training(matlab or another
program). Those were two things that I wish I had more experience with in school. Now I pretty much have to learn it on
my own. (1 Count)
Make computer programming a requirement for graduation. All of the professional jobs I have held have required me to use
either Matlab, Excel VBA, SAS, R, KML, or Java. I think having at least a basic foundation in programming would go a
long way towards a great professional career and definitely will assist in graduate school. (1 Count)
Promote club activities to build community among math majors. (1 Count)
I think GSU should significantly increase the use of computers/programming/CAS in their Mathematics program. My
Numerical Analysis class was the only class I took during my undergraduate program that used computers for solving
problems. (1 Count)
First the program must require a programming class and second allocate more time for Senior Seminar class therefore the
student will be more exposed to research skills and will be able to conduct a project by his or her own besides the review
paper projcet. (1 Count)
Career Outreach - Internships (1 Count)
I think that the department needs to create more hands on opportunities for current students such as internship within that
program. In that way, students will be able to apply the experience they learned from internship toward their career. (1
Count)
Broaden the course offerings, several courses in the catalog rarely get offered. (1 Count)
General Comments:
My professors were great people and I continue to be inspired them. (1 Count)
Besides to become a teacher a lot of jobs consider people with degree as good programmers and best analysts because of
their ways of thinking. That is why is very important that the student seeks a degree in math must know at least basic
programming and expose more to research skills. (1 Count)
Go Panthers (1 Count)
.9.b Undergraduate Student Surveys
N = 48
Response rate = 20%
Table 1. General Learning Outcomes
To what degree is your major program of study contributing to your doing or achieving the following:
1. Writing clearly and effectively.
2. Speaking clearly and effectively.
3. Locating and organizing information from multiple sources.
4. Integrating new information with past knowledge.
5. Analyzing problems from various points of view.
6. Developing original ideas.
7. Understanding ethical standards.
NC=No contribution; SC=Significant contribution.
NC
2
3
4
5
SC
%
%
%
%
%
%
N
1.
4.2
20.8
12.5
16.7
27.1.
18.8
48
2.
4.2
20.8
12.5
20.8
22.9
18.8
48
3.
0.0
8.3
8.3
31.3
27.1
25.0
48
4.
0.0
4.2
2.1
6.3
31.3
56.3
48
5.
2.1
6.3
0.0
8.3
39.6
43.8
48
6.
2.1
6.3
18.8
29.2
20.8
22.9
48
7.
6.3
22.9
10.4
35.4
10.4
14.6
48
Note. Mean scale: 1=No contribution to 6=Significant contribution.
Table 2. Program Preparation/Challenge
Please indicate the extent to which you agree with the following statements:
1. My program of study is preparing me for my career or future educational goals.
2. My experience in the department has fostered my interest in my program of study.
3. My program of study is academically challenging.
4. Overall, instructors in the department stress high quality.
SD = Strongly disagree; SA = Strongly agree.
SD
2
3
4
5
SA
%
%
%
%
%
%
N
1.
2.1
4.3
6.4
14.9
34.0
38.3
47
2.
6.4
10.6
10.6
14.9
21.3
36.2
47
3.
0.0
2.1
0.0
8.5
29.8
59.6
47
4.
0.0
2.1
17.0
12.8
36.2
31.9
47
Note. Mean scale: 1=Strongly disagree to 6=Strongly agree.
Table 3. Program Quality
Please rate the following items:
1. Overall quality of the undergraduate courses in the department.
2. Availability of undergraduate courses in the department.
3. Overall quality of undergraduate instruction in the department.
4. Procedures used to evaluate student performance.
Poor
2
3
4
5
Excellent
%
%
%
%
%
1.
2.1
4.2
14.6
22.9
43.8
12.5
% N
4
8
2.
12.5
6.3
12.5
33.3
20.8
14.6
4
8
3.
2.1
4.2
18.8
29.2
29.2
16.7
4
8
4.
2.1
12.5
18.8
25.0
29.2
12.5
4
8
Note. Mean scale: 1=Poor to 6=Excellent.
Table 4. Faculty Interaction
Please indicate the extent to which you agree with the following statements:
1. In my department, students have opportunities to do research related activities with faculty.
2. In my department, faculty are available to answer questions or duscuss my concerns about my program of study.
3. In general, faculty in my department are appropriately prepared for the courses they teach.
4. In general, faculty in the department motivate me to do my best.
5. My department promotes an environment of inclusiveness and respect.
6. I would recommend my department to students like myself.
SD = Strongly disagree; SA = Strongly agree.
SD
2
3
4
5
SA
%
%
%
%
%
%
N
1
6.3
6.3
14.6
33.3
14.6
25.0
48
2
2.1
8.3
14.6
20.8
27.1
27.1
48
3
0.0
6.3
8.3
22.9
35.4
27.1
48
4
4.2
8.3
0.0
37.5
27.1
22.9
48
5
2.1
4.3
6.4
19.1
38.3
29.8
47
6
4.2
8.3
2.1
27.1
29.2
29.2
48
Note. Mean scale: 1=Strongly disagree to 6=Strongly agree.
The following statements are in response to the comment section in the online questionnaire.
All responses are exported directly into a Word document without any changes to wording,
punctuation, or grammar. Please note: Each paragraph denotes an individual response.
General Comments:
I'm a freshmen and don't have quite the proper interaction with my department yet. (1 Count)
I am really satisfied with the math/stat department overall. The teachers are approachable and willing to help, even though I
don't use them as much as I should. Every professor I have experienced in this department have a zealous for students to
learn and grasp the concept of the material and not just memorize and regenerate. This makes me feel like I will be more
prepared for my dream career and anything they throw my way.I just wish that part 2 of an upper level series math/stat class
could be offered in the fall and not only the spring and summer. This makes it very difficult for me to make my schedule
from semester to semester, because of the lack of available courses. (1 Count)
Some of the professors in my department, mathematics, in my opinion, are bad. They do not explain clearly, they look down
upon students for asking questions, thus leading to less knowledge transmission, and they misguide the student with regards
to what is on their exam. (1 Count)
I love mathematics, but freshman year almost killed my interest. Sophomore year made me frustrated. (1 Count)
I am proud of the GSU math department. The professors are excellent and they really care for their students, which says a
lot when looking at the problems most people have with their professors in other disciplines. I hope that I can one day be of
use to the department if I am accepted into the graduate program. (1 Count)
The math department definitely has a challenging and high quality curriculum. Would like more options in terms of courses.
I work during the day most of the time so I need evening and night courses, sometimes one course is at night and another
during the day. I have to make a decision between one course or the other which makes my graduation date extend. (1
Count)
overall the university staff is ready to work with you but only when you get a chance of their time. Office hours are not
usually at a good time for those of us with a full time job. (1 Count)
n/a (1 Count)
The math department at GSU is of very poor quality. None of the professors speak clear English and the language barrier for
such a difficult subject is a big challenge. There are not many electives offered for undergrads and cross listed 4000/6000
level courses are unfair to both parties. The professor has to struggle to make the class challenging enough to grad students
while still appropriate for undergrads. If I could choose a different major at GSU, I would if I wasnt so close to
graduation. (1 Count)
Many professors are brilliant mathematicians, however the are not brilliant teachers. Meaning that while most professors
have a phenomenal researching ability they lack the necessary tool set to communicate ideas effectively to undergraduate
students. (1 Count)
.9.c Graduate Alumni Surveys
N = 13
Response rate = 33%
Table 1. General Outcomes
Please indicate the extent to which you agree with the following statements:
1. My program of study has made a positive contribution to the quality of my life.
2. I have applied the skills I learned in my program to help resolve issues I've faced in my professional life.
3. Overall, I was satisfied with my degree program.
Please indicate the extent to which you agree
with the following statements:
Strongly
disagree
2
3
4
5
Strongly
agree
%
%
%
%
%
%
N
My program of study has made a positive contribution
to the quality of my life.
0.0
0.0
7.7
15.4
15.4
61.5
13
I have applied the skills I learned in my program to
help resolve issues I’ve faced in my professional life.
0.0
7.7
7.7
15.4
23.1
46.2
13
Overall, I was satisfied with my degree program.
0.0
0.0
7.7
23.1
23.1
46.2
13
Note. Mean scale: 1=Strongly disagree to 6=Strongly agree.
Employment
Table 2: Current Employment
Table 3: Recent Employment
Are you currently employed?
Have you been employed at any time over the last year?
N = 13
Yes
No
N =1
Yes
No
%
12
1
%
0.0
100.0
Table 4. General area of employment
Please indicate the general area of employment.
N = 12
Agriculture/Natural Resources
Arts
Business/Finance
College Faculty/Administration
Counseling/Mental Health
Education K-12
Government/Public
Administration
Hospitality/Tourism
Journalism/Publication
Law
Library Work
Manufacturing/Construction
Marketing
Media/Communication
Medicine/Nursing
Non-Profit or Community Org.
Religious Organization
Transportation
Other
%
0.0
0.0
16.7
33.3
0.0
0.0
25.0
0.0
0.0
0.0
0.0
7.7
0.0
0.0
0.0
0.0
0.0
0.0
7.7
Other: N=2
Clinical research organization
Information technology
Table 5. Skills and Employment
The following questions focus on the skills you may have learned in your degree program at Georgia State and
whether you listed them on your resume, discussed them during your job interview, or use(used) them in your job.
List on resume
Yes
%
No
%
Yes
%
Using(
used)
on job
No
Yes
%
%
Research skills
75.0
25.0
91.7
8.3
100.0
Communication skills (writing and speaking)
66.7
33.7
91.7
8.3
100.0
Ability to interpret data/information in a critical manner.
83.3
16.7
0.0
91.7
Ability to analyze problems from different perspectives
75.0
25.0
91.7
8.3
83.3
Ability to work with diverse populations
83.3
16.7
100.0
0.0
100.0
N = 12
Further Education
Table 6. Graduate Enrollment
Table 7. Degree sought
Are you currently enrolled in a graduate program?
What degree are you seeking?
N = 13
Yes
No
N=1
Ed.D.
J.D.
M.A.
M.B.A.
M.D.
M.Div.
M.F.A.
M.S.
M.S.W.
M.S.L.S.
M.T.S.
Ph.D.
Th.D.
Other
What is your program of study?
%
7.7
92.3
%
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
100.0
0.0
0.0
Discuss in
job interview
100.0
Statistics
At what institution are you pursuing your degree?
Georgia State
Table 8. Additional Degrees Earned
Since graduating from Georgia State,
have you earned an additional degree(s)?
N = 13
Yes
No
%
0.0
100.0
Qualitative Data
The following statements are in response to the comment sections in the online questionnaire. All
responses are exported directly into a Word document without any changes to wording,
punctuation, or grammar. Please note that each paragraph represents one respondent’s
comments.
Looking back, what aspects of your program do you believe were the most valuable in contributing to your earning a degree
at Georgia State?
Training in statistical analysis (1 Count)
Most of programs were equally valuable to earn my degree. (1 Count)
The diversity of students and instructors help me see things in different ways. (1 Count)
nice faculty members with research experience (1 Count)
I was employed full time while working towards the degree and evening class scheduling for working students was
crucial. (1 Count)
The structure of the program (courses offered and suggested sequencing) was extremely important. Equally important was
the support offered by the faculty and staff. The support included both financial and mentoring elements. The department is
particularly strong in regards to the quality of instruction provided. (1 Count)
Flexibility of delivery of learning content. (1 Count)
Software and IT skills. Project work. (1 Count)
Programming skills and the diversity of people to work with. (1 Count)
Scholarship (1 Count)
Afternoon classes and accessibility to professors. (1 Count)
What kinds of improvements would you suggest the Institute make in order to enhance the educational experience of current
students in the program?
More in-depth and up-to-date materials to be taught in class (1 Count)
Hiring more professor and who can speak more clearly. (1 Count)
set-up more cutting-edge courses and increase the scholarship (1 Count)
Ideally the department would have offered courses like multivariate stats and time series analysis more often and during
evenings. (1 Count)
More on-line offerings. (1 Count)
Internships and networking. More software training. (1 Count)
Intern opportunity!! (1 Count)
Keep closer interaction with the industry (at least follow the industrial trend a little bit) and train skills that are highly in
demand. (1 Count)
Increase number of statistics professors, establish statistical consulting center to allow other GSU researchers to interact
with statistical students, and require a technical writing class for students. (1 Count)
General Comments:
I think stat department head does not do his job correct. I personally don't agree his performance. (1 Count)
All aspects of the questionnaire items are some what addressed in the program but more emphasis should be ensured. (1
Count)
Qualitative Comments:
The following statements are in response to the comment sections in the online questionnaire. All
responses are exported directly into a Word document without any changes to wording, punctuation,
or grammar. Please note that each paragraph represents one respondent’s comments.
Looking back, what aspects of your program do you believe were the most valuable in contributing to your earning a degree
at Georgia State?
What kinds of improvements would you suggest the department make in order to enhance the educational experience of
current students in the program?
What kinds of programs would you like to see us offer that would help strengthen our alumni network?
General Comments:
.9.d Graduate Student Surveys
N = 21
Response rate = 19%
Table 1. General Learning Outcomes
To what degree is your major program
of study contributing to your doing or
achieving the following:
No contribution
2
3
4
Significant
contribution
5
%
%
%
%
%
Writing clearly and effectively
14.3
0.0
0.0
9.5
38.1
38.1
% N
21
Speaking clearly and effectively
23.8
0.0
0.0
19.0
23.8
33.3
21
Locating and organizing information from
multiple sources
14.3
0.0
4.8
4.8
33.3
42.9
21
Awareness of historical contexts
surrounding your area of study
14.3
14.3
4.8
19.0
14.3
33.3
21
Demonstrating competence in specific research
methods appropriate to your area of
specialization
4.8
4.8
4.8
14.3
28.6
42.9
21
Effectively evaluate implications and
applications of research in your field
9.5
0.0
14.3
19.0
23.8
33.3
21
Collaborating effectively with colleagues (e.g.,
other students, researchers, faculty)
4.8
9.5
4.8
14.3
28.6
38.1
21
Knowledge about the tenets of ethical practice
0.0
14.3
9.5
14.3
23.8
38.1
21
Note. Mean scale: 1=No contribution to 6=Significant contribution.
Table 2. Program Preparation/Challenge
Please indicate the extent to which you
agree with the following statements:
Strongly
disagree
2
3
4
5
Strongly
Agree
%
%
%
%
%
%
N
My program of study is academically
challenging.
4.8
0.0
4.8
19.0
19.0
52.4
21
My program requirements are clear to me.
4.8
4.8
4.8
14.3
23.8
47.6
21
There are sufficient research opportunities
available to me in the department.
9.5
4.8
14.3
14.3
23.8
33.3
21
My program's curriculum is broad enough to
prepare me for my career choice.
4.8
0.0
14.3
23.8
23.8
33.3
21
Overall, instructors in the department stress high
quality work from students.
5.0
5.0
5.0
25.0
20.0
40.0
20
Note. Mean scale: 1=Strongly disagree to 6=Strongly agree.
Table 3. Program Quality
Please rate the following items:
Poor
2
3
4
5
Excellent
%
%
%
%
%
%
N
9.5
4.8
4.8
14.3
38.1
28.6
21
19.0
9.5
14.3
38.1
4.8
14.3
21
Overall quality of graduate instruction in the
department
0.0
19.0
0.0
28.6
19.0
33.3
21
Academic advisement available in the
department
9.5
4.8
9.5
23.8
14.3
38.1
21
Career preparation and guidance available in the
department
10.0
10.0
5.0
25.0
25.0
25.0
20
Availability of graduate research/teaching
assistantships
10.0
0.0
10.0
5.0
35.0
40.0
20
5.0
0.0
10.0
30.0
35.0
20.0
20
Overall quality of graduate courses in the
department
Availability of graduate courses in the department
Support for student conference presentations and
publications
Note. Mean scale: 1=Poor to 6=Excellent.
Table 4. Faculty Interaction
Please indicate the extent to which
you agree with the following
statements:
Strongly
disagree
2
3
4
5
Strongly
Agree
%
%
%
%
%
%
N
In general, faculty in my department are
appropriately prepared for the courses
they teach.
0.0
9.5
9.5
9.5
28.6
42.9
21
In general, faculty are up-to-date in
emerging trends and information in my
field of study.
0.0
0.0
9.5
4.8
33.3
52.4
21
In my department, faculty are available to
answer questions or discuss my concerns
about my program of study.
0.0
4.8
4.8
4.8
23.8
61.9
21
In general, faculty in the department
motivate me to do my best.
4.8
4.8
4.8
19.0
23.8
42.9
21
Faculty are fair and unbiased in their
treatment of students in my graduate
program.
4.8
4.8
0.0
19.0
14.3
57.1
21
Administrative staff in the department are
helpful to me.
5.0
0.0
5.0
5.0
30.0
55.0
20
My department promotes an environment
of inclusiveness and respect.
5.0
0.0
0.0
10.0
40.0
45.0
20
I would recommend my department to
other students like myself.
5.0
5.0
5.0
25.0
25.0
35.0
20
Note. Mean scale: 1=Strongly disagree to 6=Strongly agree.
The following statements are in response to the comment section in the online questionnaire.
All responses are exported directly into a Word document without any changes to wording, punctuation, or grammar.
Please note: Each paragraph denotes an individual response.
General Comments:
Most of the professors are supportive and knowledgeable. There aren't enough options for classes for statistics graduate
students, though, and this has been very problematic for me personally this semester. (1 Count)
I really enjoyed my time at GSU's Dept. of Math&Statistics. Everyone was very helpful, from staff to faculty members. If I
had to choose again, I would choose GSU again. It is an excellent school to learn, very welcoming and very convenient.
Haci Akcin (1 Count)
Academic advisement is poor in this department. I did not receive any individual guidance when I first arrived at Georgia
State. It was very difficult for me to determine what courses I would be required to take for my program as well as when I
would need to take them. I took great pains to plan months ahead for administrative concerns related to my program, only to
be told that the information that I had received months beforehand was incorrect and that I would be responsible for making
adjustments at the last minute. One difficulty that I faced was that some courses that are supposedly required for my
program are not offered regularly enough for every new student to be able to take them, which unfairly limits opportunities
to students solely on chance. Another difficulty that I faced was that the new student orientation was scheduled two days
after the fee deadline for the autumn semester; the only reason for which I was able to avoid paying late fees was by sheer
foresight and also by a lot of luck. The administration must give more guidance to their graduate students, especially new
students. Funding and financial management are also poor in this department. There are currently no research assistantships
in pure mathematics, and the teaching assistantships that are available offer stipends that are lower than many other
comparable universities. Worse, the department website claims that a PhD student that is granted a teaching assistantship
will be paid $15,000 a year when really every first-year students is paid 30% less than that ($10,000 a year). The department
needs offer more research opportunities to its pure mathematics graduate students, and it needs to be more clear about the
teaching assistantships that it does offer. Worst of all, the department is very limited in terms of the graduate courses that it
offers and the faculty available to teach the few courses that are offered. There is a huge bias toward discrete mathematics
and mathematical biology, and the courses offered in discrete mathematics are largely applied in nature. There are currently
no courses in analysis, topology, geometry, or logic offered above the masters level. Students needing experience in these
other areas of mathematics are forced to study on their own. The department needs to hire faculty in other areas of
mathematics. The department needs to give its graduate students proper preparation for research careers in pure
mathematics. (1 Count)
Excellent faculty, courses, and instruction. The department (and in particular the statistics faculty) does an amazing job with
a small number of instructors having to teach a large number of courses. It would be helpful if the department could increase
the number of advanced courses in areas such as stochastic processes and measure-based probability theory. Thanks! (1
Count)
To be fair, GSU is not known for its Math and Statistics department. And overall I would say that it is average, with some
really good professors, some poor, and most just average. The statistics department in general has suffered in recent years,
as many professors have left. And there is a rumor that the department will be moving into the school of public health in the
near future. I don't know if this is true. All I can say is that I wish the instruction had been better. The instruction was
reasonable, in that all the minimum requirements were met, but few of the teachers stood out as being especially good at
teaching. And I've felt that under the right tutelage I could have learned a great deal more. Though I should mention the
professors I've had I've liked very much personally, and have recognized their talents. But effective teaching is a different
matter. Perhaps the Math Stat department is focused more on professors with high status in the research world than on
people who can teach well. Perhaps this is the way academia is in this country. I don't know this either. If so I think this is a
mistake. Departments should attempt to recognize who their quality instructors are and put them in a place to effect the
largest number of students possible. Leave the researchers to do their research. I think this is especially true for
undergraduate math courses. A good teacher can provide clarity and enjoyment into a difficult subject, and encourage
students to follow a career path that is known to be worthwhile. While a poor teacher can scare students away. I hope GSU
can add some common sense practices to its decisions within the math department in the future. Thank you.(1 Count)
Some administrative staff member is not nice enough to all the graduate students, somewhat unfair and biased. (1 Count)
I definitely felt favoritism and bias from most professors in the math department. There are a few who were encouraging
and inspirational. In the future I would love to see the very few African Americans in the department receive some sort of
financial assistance. Bias was definitely shown in this area specifically.(1 Count)
There are few faculty members exempt but in general underestanding the chinese instructors considering their accent is not
that easy in the class. Moreover, in my opinion it is really disrespectful that the instructors are talking in chinese to chinese
students right after the class regarding the course material. However, I want to atke a moment and thank whom ever hired
Dr. Samara, he is the best insatructor I have had in my whole studies in the Math department. (1 Count)
.10 Syllabi
.10.a
Math 1070 Elementary Statistics
1. Quantitative Reasoning: Students will use quantitative reasoning in problem solving including:
Geometric and symbolic representation and manipulation and pattern recognition.
2. Graphical and numerical summaries, normal distribution. Students will be able to construct and
interpret graphical displays of univariate data such as the stem plot, histogram, box plot, and
time plot; calculate and interpret summary statistics such as the mean, median, standard
deviation, and five number summary; describe and use density curves such as the uniform and
normal density curves; use the normal density curve to calculate proportions.
3. Graphical and numerical summaries for bivariate data. Students will be able to construct and
interpret graphical displays of bivariate data: scatter plots, regression lines, residual plots,
outliers, and influential points; discuss the meaning of the correlation coefficient and the leastsquares regression line.
4. Samples and experimental designs. Students will be able to select a simple random sample
using a table of random digits; recognize biased sampling such as voluntary and convenience
sampling; describe some experimental designs such as completely randomized and block
designs.
5. Sample distributions, probability and random variables. Students will demonstrate knowledge
and be able to examine and understand and use basic probability concepts including the
following: sample spaces of possible outcomes of random experiments, random variables and
their probability distributions, the sampling distribution of the mean and the central limit
theorem.
6. Z-tests and confidence intervals for means. Students will demonstrate the ability to understand
and use the vocabulary of statistical inference including: confidence intervals, confidence levels
and margins of error in general, confidence level in general as the probability to give a correct
estimate of the confidence intervals for the mean of a normal population of known variance, or
the difference between means of two normal populations of know variances, null and alternative
hypotheses, rejection region in terms of the population(s) standard deviation(s) and sample
size(s), level of significance and p-values for one and two sided tests for means, when the
variance(s) of the underlying normal population(s) is (are) known, or the sample is large.
7. Z-tests and confidence intervals for proportions. Students will demonstrate the ability to make
design and make correct inferential statements about: sampling distribution of a sample
proportion, confidence intervals for a (difference between two) population proportion(s), and
sample size for a required margin of error.
8. T-tests and confidence intervals for means of normal populations. Students will demonstrate the
ability to understand and apply inferential statements including: confidence level as the
probability to give a correct estimate of the mean (difference of means) of a (two) normal
population(s), when the standard deviation(s) is (are) unknown; level of significance and pvalues for one and two sided tests for means, when the variance(s) of the underlying normal
population(s) is (are) unknown.
9. Chi-square tests for two-way tables. Students will be able to arrange general bivariate
categorical data in several groups into a two-way table of counts in all the groups. Students
explain what null hypothesis the chi-square statistic tests in a specific two-way table; use
percents, comparison of expected and observed counts, and the components of the chi-square
statistic to see what deviations from the null hypothesis are important; make a quick assessment
of the significance of the statistic by comparing the observed value to the degrees of freedom.
10. Applications. When applying analytic, algebraic, geometric, and algorithmic techniques to
solving applied statistical problems students will: Use appropriate technology, Communicate
how the problem is modeled by a mathematical/statistical formulation and how to interpret the
results of the statistical analysis.
.10.b
Math 1101, Introduction to Mathematical Modeling
1. Algebra. Students will demonstrate the ability to: Graph points; Graph linear, piecewise linear,
exponential, logarithmic, and quadratic equations and functions; identify horizontal asymptotes;
Determine the equation of a line given two points or one point and the slope; Determine the
absolute value of a quantity; Solve and estimate solutions to linear, quadratic, exponential, and
logarithmic equations, including use of the properties of exponents and common and natural
logarithms; Solve linear systems of two equations by substitution and elimination, including
systems that have a unique solution, no solution, or many solutions; Simplify expressions using
the laws of exponents and logarithms; Calculate average rate of change of any function;
Perform arithmetic calculations to answer questions regarding two-variable data presented in
tabular, graphical, or equation form; Express and compare very large and very small numbers
using scientific notation and orders of magnitude; Employ the relationship y = bx ⇔log b y = x
to solve exponential and logarithmic equations; Factor quadratic expressions; Complete the
square of quadratic expressions; Express the square root of negative numbers in terms of the
imaginary unit, i; Given conversion factors, convert units of measure; Use the quadratic formula
to solve quadratic equations;
2. Functions. Students will demonstrate: An understanding of the definitions of function, domain,
range, independent and dependent variables, and input and output; The ability to determine if
tables, graphs, and equations represent functions; The ability to determine the domain and range
of functions as mathematical abstractions or in a physical context; The ability to compose
functions; The ability to determine from the graph of a function the values of the independent
variable for which the function increases, decreases, or remains constant.
3. Linear Functions. Students will demonstrate the ability to: Determine when two real-world
variables are related by a linear or piecewise linear function; Model the behavior of two realworld variables that are directly proportional or are related by a linear or piecewise linear
function using tables, graphs, equations, or combinations thereof; Use a linear function to
approximate the value of a non-linear function; Interpret the intersection of the graphs of linear
functions as equilibrium points; Evaluate linear and piecewise linear functions; Define,
calculate, and interpret average rate of change as slope; Define the linear function and the
general equation of the linear function.
4. Exponential Functions. Students will demonstrate the ability to: Determine when two real-world
variables are related by an exponential function; Model the behavior of two real-world variables
that are related by an exponential function using tables, graphs, equations, or combinations
thereof including such applications as population growth and decay, radioactive decay, simple
and compound interest, inflation, the Malthusian dilemma, musical pitch, and the Rule of 70;
Change the base of an exponential function to determine rate of growth/decay, growth/decay
factor, and effective and nominal interest rate; Express continuous growth/decay in terms of the
number e; Evaluate exponential functions; Determine the exponential equation model from the
table or graphical model; Compare linear to exponential growth.
5. Logarithmic Functions. Students will demonstrate: The ability to determine when two realworld variables are related by a logarithmic function; The ability to model the behavior of two
real-world variables that are related by a logarithmic function using tables, graphs, equations, or
combinations thereof including such applications as pH and the decibel system; Their
understanding of the natural logarithm; The ability to graph logarithmic functions.
6. Polynomial and Quadratic Functions. Students will demonstrate the ability to: Predict the shape
of graphs of polynomial functions degree n; Estimate horizontal intercepts of polynomial
functions from their graphs; Determine the horizontal intercepts of polynomial functions in
factored form; Determine when two real-world variables are related by a quadratic function by
calculating the average rate of change of the average rates of change; Model the behavior of two
real-world variables that are related by a quadratic function using tables, graphs, equations, or
combinations thereof including such applications as maximum area for fixed perimeter,
minimum perimeter for fixed area, free fall, maximum profit, and break-even analysis;
Determine the vertex, axis of symmetry, and horizontal and vertical intercepts of quadratic
functions in either the a-b-c or a-h-k forms; Convert quadratic functions from the a-b-c form to
the a-h-k form and vice versa.
.10.c
Math 1111 College Algebra
1. Understand the general definition of a function and be able to: Illustrate a function verbally,
graphically, with charts/tables, and with set notation; Determine the domain and range of a
function; Identify where a function is increasing, decreasing or constant.
2. Understand linear functions and be able to: Identify, graph, and find equations of linear
functions (including parallel and perpendicular lines); Interpret the slope and y-intercept as an
average rate of change and an initial amount, respectively; Students will be able to interpret and
apply these ideas in applied settings.
3. Identify, understand and apply graph transformations of y =X2 , y =X1/2 , and y = |x| using:
Vertical and horizontal shifts; Vertical stretching and compressions; Reflections.
4. Understand, identify, graph, interpret and apply the following in applied settings:
(a) Quadratic functions of the form y = ax2+bx+c; Determine the vertex and intercepts.
(b) Power functions and transformations of power functions
(c) Polynomial functions where the polynomial is factorable: Students will be able to describe
the end behavior of polynomials and the relationship between end behavior and the degree
of the polynomial; Students will be able to determine intercepts of factorable polynomials
exactly; Students will be able to use appropriate technology to approximate x-intercepts and
local extrema of polynomials.
(d) Identify and graph transformations of y = 1/x and y = 1/ x2: Students will be able to
recognize and determine vertical and horizontal asymptotes, end behavior, and behavior
near vertical asymptotes.
(e) Piece-wise defined functions.
(f) Compose two functions and determine the domain and range of the composite function.
(g) Inverse functions: Get a rule for an inverse function; Graph a function and its inverse
(h) Exponential functions of the form y =ax and their transformations.
(i) Logarithmic functions: Define a logarithm; Convert between logarithmic and exponential
forms; Understand the inverse relationship between logarithmic and exponential functions.
5. Determine, both algebraically and graphically, solutions to the following types of equations and
apply these solutions to concepts related to functions and other applications: Linear; Quadratic;
Factorable polynomial; Rational; Radical (involving more than one radical); Equations of the
form xn=k; Simple exponential equations; Logarithmic equations using properties of
logarithms.
6. Use graphical and algebraic techniques to find solutions to the following kinds of inequalities
and apply these solutions to concepts related to functions and other applications: Linear;
Quadratic; Factorable Polynomial; Rational; Exponential.
7. Solve linear systems of two equations in two unknowns using; Elimination; Substitution;
Matrices; as well as use linear systems to solve application problems.
8. Solve simple non-linear systems of equations algebraically and graphically.
.10.d
Math 1113 Precalculus
1. Quantitative Reasoning: Students will use quantitative reasoning in problem solving situations
including: geometric an symbolic representation and manipulation; pattern recognition;
translating mathematics into words and words into mathematics; recognizing incorrect answers
and arguments and knowing when an answer is reasonable; being able to write out a solution in
a logical and clear form rather than presenting a collection of unidentified intermediate numbers
that may end with the final numerical answer.
2. Algebraic Functions: Students will use functions and related concepts including: recognition of
a function in either graphical, table, implicit, or explicit form; be able to find domains and
ranges and determine if a function is one-to-one; perform operations of functions including
composition, finding inverses, and finding difference quotients; graphically determine when a
functions is increasing, decreasing, constant, one-to-one, continuous, and even or odd; apply
basic graph transformations including af(x), f(x) + d, f(x – c), f(bx), , f( ) to the parent
functions; graph a function defined as piecewise.
3. Defining the Trigonometric Functions: Students will use circular and trigonometric functions
and related concepts including: find exact values of the functions by using the unit circle,
wrapping function, and special triangles; know the relationship between radian measure and
degree measure and be able to convert from one unit to the other; know the definition of the six
(6) trigonometric functions as related to the right triangle; distinguish between right angled and
oblique triangles and recognize the appropriate method needed to solve the triangle (Law of
Sines, Law of Cosines, Pythagorean Theorem)
4. Use of Trigonometric Functions: Students will demonstrate knowledge of and be able to use
trigonometry. Specifically: (1) given one of the trig values of an angle in a certain quadrant, be
able to find the other five trigonometric functions through identities not limited to Pythagorean,
identity, reciprocal identities, even/odd identities and quotient identities, (2) solve oblique
triangles using the Law of Sines, and Law of Cosines, and work related applied problems, (3)
graph the basic six trigonometric functions, including sine and cosine functions with applied
graph transformations; identify the domain, range, period, amplitude and phase shifts of the
functions. (4) find the exact values of the inverse trig functions, (5) solve linear and quadratic
trigonometric equations and equations with compound angles.
5. Mathematical Proofs: Students will demonstrate an understanding of mathematical proofs and
related concepts by specifically developing: sum, difference, and co-function identities, double
angle and half angle formulas, and sum to product and product to sum identities.
6. Analytic Geometry: Students will be demonstrate knowledge of and be able to use analytic
geometry concepts and related techniques, including polar coordinates and conic sections
including: convert polar to rectangular coordinates and vice versa; sketch graphs of polar
functions including cardioids, roses, circles, and spirals; identify equations of parabolas,
hyperbolas, and ellipses and sketch their graphs.
7. Vectors: Students will demonstrate an understanding of algebraic and geometric vectors and be
able to use them to model situations and solve problems.
8. Applications and Technology: When applying analytic, algebraic, geometric and algorithmic
techniques to solving applied problems, students should be able to use technology when
appropriate. Care should be taken to ensure that use of technology is not accompanied by a
decrease in mathematical or fundamental understanding.
.10.e
Math 1220 Survey of Calculus
1. Locate and describe discontinuities in functions.
2. Evaluate limits for polynomial and rational functions
3. Compute and interpret the derivative of a polynomial, rational, exponential, or logarithmic
function.
4. Write the equations of lines tangent to the graphs of polynomial, rational, exponential, or
logarithmic functions at given points.
5. Compute derivatives using the product, quotient and chain rules on polynomial, rational,
exponential, and logarithmic functions.
6. Solve problems in marginal analysis in business and economics using the derivative.
7. Interpret and communicate the results of a marginal analysis.
8. Graph functions and solve optimization problems using the first and second derivatives and
interpret the results.
9. Compute antiderivatives and indefinite integrals using term by term integration or substitution
techniques.
10. Evaluate certain definite integrals.
11. Compute areas between curves using definite integrals.
12. Solve application problems for which definite and indefinite integrals are mathematical models.
13. Solve application problems involving the continuous compound interest formula.
14. Describe functions of several variables.
15. Compute partial derivatives.
16. Find local extrema of functions of two variables
.10.f
Math 2211 Calculus of One Variable I
1. Quantitative Reasoning: Students will use quantitative reasoning in problem solving situations
including: Geometric, symbolic, algebraic, and analytic representation and manipulation of
quantitative information; Pattern recognition.
2. The Real Number System: Students will use algebraic and order properties of the real number
system and subsystems of the set of real numbers.
3. Functions. Students will use and investigate functions and related concepts including:
Representations of functions using formulas, graphs, and parameters; Operations on functions
defined by arithmetic operations, composition, and inversion; Types of elementary functions
such as polynomial, rational, radical, absolute value, trigonometric, and piecewise-defined
functions.
4. Functions. Students will use and investigate properties of functions and their graphs involving
monotonicity, extrema, concavity, and other salient features.
5. Limits and Continuity. Students will demonstrate knowledge of and be able to use concepts and
techniques related to limits and continuity including: Performing analytic and graphical
interpretations of concepts; Evaluating limits; Determining points of continuity/discontinuity of
functions; Applying properties of limits and continuity related to operations on functions.
6. Analytic Geometry. Students will demonstrate knowledge of and be able to use analytic
geometry concepts and related techniques including: Conic sections; Representations and
transformations involving rectangular coordinate systems.
7. Differentiation. Students will demonstrate an understanding of the derivative at a point,
derivative functions, and related concepts including: Interpretation of the derivative at a point in
terms of difference quotients, slopes of tangent lines and (instantaneous and average) rates of
change; The Mean Value Theorem for derivatives and related results; Applying properties of
differentiation related to elementary functions and operations on functions; Application of the
derivative to investigating properties of functions; Implicit differentiation and differentials.
8. Integration. Students will demonstrate an understanding of integration and related concepts
including: The definite integral as an accumulation of small quantities; The Fundamental
Theorem of Calculus and antiderivatives; The Mean Value Theorem for integrals; Applying
properties of integration related to elementary functions, operations on functions, and
elementary substitutions; Applications of integration in a variety of contexts.
9. Applications. While applying analytic, algebraic, geometric, and algorithmic techniques to
solving applied problems students will: Use appropriate technology; Communicate how the
problem is modeled by a mathematical formulation, and how to interpret the result of the
mathematical analysis.
10. Mathematical Proof. Students will demonstrate an understanding of mathematical proof and
related concepts including: Analysis of the logical structure of mathematical proofs and
derivations; Use contradictions and counter examples appropriately; Use mathematical
induction.
11. Mathematical Proof. Students will demonstrate an understanding of the rudiments of e,dproofs.
.10.g
Math 2212 Calculus of One Variable II
1. Quantitative Reasoning. Students will use quantitative reasoning in problem solving situations
including: Geometric, symbolic, algebraic, and analytic representation and manipulation of
quantitative information; Pattern recognition.
2. The Real Number System. Students will use algebraic and order properties of the real number
system and subsystems of the set of real numbers.
3. Functions. Students will use and investigate functions and related concepts including:
Representations of functions using formulas, graphs, and parameters; Operations on functions
defined by arithmetic operations, composition, and inversion; Types of elementary functions
such as polynomial, rational, radical, absolute value, trigonometric, and piecewise-defined
functions. Properties of functions and their graphs involving monotonicity, extrema, concavity,
and other salient features.
4. Limits and Continuity. Students will demonstrate knowledge of and be able to use concepts and
techniques related to limits and continuity including: Performing analytic and graphical
interpretations of concepts; Evaluating limits; Determining points of continuity/discontinuity of
functions; Applying properties of limits and continuity related to operations on functions.
5. Limits and Continuity. Students will evaluate limits of indeterminate form.
6. Analytic Geometry. Students will demonstrate knowledge of and be able to use analytic
geometry concepts and related techniques including conic sections.
7. Analytic Geometry. Students will demonstrate knowledge of and be able to use representations
and transformations involving rectangular and polar coordinate systems.
8. Differentiation. Students will demonstrate an understanding of the derivative at a point,
derivative functions, and related concepts including: Interpretation of the derivative at a point in
terms of difference quotients, slopes of tangent lines and (instantaneous and average) rates of
change; The Mean Value Theorem for derivatives and related results; Applying properties of
differentiation related to elementary functions and operations on functions; Application of the
derivative to investigating properties of functions; Implicit differentiation and differentials.
9. Differentiation. Students will use the derivatives of exponential/logarithmic functions, and
apply the technique of logarithmic differentiation.
10. Integration. Students will demonstrate an understanding of integration and related concepts
including: The definite integral as an accumulation of small quantities; The Fundamental
Theorem of Calculus and antiderivatives; The Mean Value Theorem for integrals; Applying
properties of integration related to elementary functions, operations on functions, and
elementary substitutions; Applications of integration in a variety of contexts.
11. Integration. Students will demonstrate an understanding of integration and related concepts
including:Integrals involving exponential and logarithmic functions; Integration by parts and
other techniques of integration; Evaluation of improper integrals.
12. Sequences and Series. Students will demonstrate an understanding of sequences, series, and
related concepts including: Limits of sequences, sums of series, and radii of convergence;
Geometric series, alternating series, power series, and Taylor polynomials; Tests of convergence
and absolute convergence.
13. Applications. While applying analytic, algebraic, geometric, and algorithmic techniques to
solving applied problems students will: Use appropriate technology; Communicate how the
problem is modeled by a mathematical formulation, and how to interpret the result of the
mathematical analysis.
14. Mathematical Proof. Students will demonstrate an understanding of mathematical proof and
related concepts including: Analysis of the logical structure of mathematical proofs and
derivations; Use contradictions and counter examples appropriately; Use mathematical
induction.The rudiments of e,d- proofs.
.10.h
Math 2420 Discrete Mathematics
1. Identify logical form, form compound statements using the connectives and, or and not,
determine truth tables of more general compound statements, determine whether two statement
forms are logically equivalent or nonequivalent, apply De Morgan’s laws to form negations of
and and or, determine whether a statement is a tautology or a contradiction, and use logical
equivalences to simplify statement forms.
2. Determine truth tables for compound statements containing conditional and biconditional
connectives, represent if-then as or, and then use this representation to negate an if-then
statement, determine the negation, contrapositive, converse and inverse of a conditional
statement, rewrite a conditional statement as an “only if” statement, and as sufficient and
necessary conditions.
3. Determine whether and argument is valid or invalid, use valid argument forms such as modus
ponens, modus tollens, etc. to do complex deductions, and illustatrate a proof by contradiction
using the knights and knaves example.
4. Give the input/output table for the following gates: OR, AND and NOT, find a Boolean
expression (input/output table, respectively) of a circuit, find a circuit corresponding to a
Boolean circuit (input/output table, respectively) by finding the disjunctive-normal or sum-ofproducts form, determine whether two logical circuits are equivalent, and simplify a
combinatorial circuit.
5. Represent a binary (hexadecimal, octal) number as a decimal number, represent a decimal
(hexadecimal, octal) number in binary notation, represent a binary number in hexadecimal
(octal) notation, and add and subtract binary numbers.
6. Determine the domain and the truth set of a predicate variable, identify universal and existential
statements, be able to write these statements in formal and informal language, and identify
universal conditional statements, negate universal and existential statements, as well as
statements containing both universal and existential statements.
7. Define an even (odd) integer, prove an existential statement using an example, use a direct proof
to prove universal statements such as “The sum of an even integer and an odd integer is odd”,
“If the difference of any two integers is odd, then so is their sum”, etc., disprove a universal
statement by an example, follow the directions for writing proofs of universal statements, and
identify common mistakes in proving statements.
8. Use direct proofs or counterexamples to prove or disprove statements involving the rational
numbers.
9. Use direct proofs or counterexamples to prove or disprove statements involving the divisibility
of integers, and use the quotient-remainder theorem to illustrate a proof by division into cases.
10. Use methods of proofs by contradiction and contraposition to prove various statements.
11. Find the explicit formula for a sequence, and be able to do calculations involving factorial,
summation and product notations.
12. Be able to prove statements using mathematical induction.
13. Determine whether one set is a subset of another, whether two sets are equal, whether an
element is in a set or not, be able to determine the union, intersection, difference and
complement of sets, illustrate sets using Venn diagrams, determine the Cartesian product of two
or more sets, prove set identities, use set identities to derive new set properties from old set
properties, use Venn diagrams to prove set identities, determine whether sets form a partition of
a given set, and determine the power set of a set.
14. Determine whether a relationship is a function or not, determine the domain, co-domain, range
of a function, and the inverse image of x, prove or disprove whether a function is one-to-one or
not, determine whether a function is onto or not, determine the inverse of a one-to-one
correspondence, determine the composition of two functions, and show that if two functions are
one-to-one (onto) so too is their composition.
15. Determine the arrow diagram of a relation, whether a relation is a function or not, determine the
inverse of a relation, whether a relation is reflexive, symmetric or transitive, determine the
transitive closure of a relation, show that the binary relation induced by a partition is an
equivalence relation, and show that the set of equivalence classes of an equivalence relation on
A forms a partition of A.
16. Identify loops, parallel edges, etc. in a graph, draw the complete graph on n vertices, and the
complete bipartite graph on (m,n) vertices, determine whether a graph is bipartite or not, list all
the subgraphs of a given graph, determine the degree of a vertex in a graph, prove that the sum
of the degrees of the vertices is equal to twice the number of edges, show that in any graph there
is an even number of vertices of odd degree, apply these results, and determine the complement
of a simple graph.
17. Determine whether a walk is a path, simple path, closed walk, circuit or a simple circuit,
determine whether a graph is connected or not, prove that a graph has an Euler circuit if and
only if the graph is connected and every vertex of the graph has even degree, determine whether
a given graph has an Euler circuit and, if so, indicate one, prove that a graph has an Euler path if
and only if the graph is connected and has exactly two vertices of odd degree, determine
whether a given graph has an Euler path and, if so, indicate one, and determine whether a graph
has a Hamiltonian circuit and, if so, indicate one.
18. Determine whether a graph is a tree or not, show that any tree with more than one vertex has
two leaves, show that any tree with n vertices has n-1 edges, show that if G is an connected
graph with n vertices and n-1 edges, then G is a tree, determine in a rooted tree, the root, level
of a given vertex, height of the tree, children, parent, siblings, ancestors and descendants of a
vertex, determine whether a given tree is a binary or full binary tree, and prove results regarding
binary trees.
19. Apply Kruskal’s algorithm or Prim’s algorithm to determine a minimal spanning tree for a
given graph.
.10.i
Math 3000 Bridge to Higher Mathematics
1. Develop a truth table for a logical expression
2. Express the negation of a logic statement
3. Correctly decide if two statements are logically equivalent
4. Express the converse, inverse and contrapositive of a logic statement
5. Express universally and existentially quantified statements, and their negations
6. Understand the definition of a set
7. Correctly express the union, intersection and complement of sets
8. Do a direct proof
9. Correctly decide if a given proof is valid
10. Do a proof by contrapositive, contradiction or exhaustion
11. Understand indexed families of sets, their unions, intersections and complements
12. Do a proof using mathematical induction: the statement to be proved may be an equality or an
inequality
13. Correctly decide if a given relation is an equivalence relation
14. Correctly determine the equivalence classes of an equivalence relation
15. Understand the division algorithm and its implications in divisibility problems
16. Correctly express the power set of a given set, and its cardinality
17. Correctly decide if a function is one-to-one, onto, or has an inverse
18. Correctly formulate the composition of two functions
19. Correctly decide if a set is finite, countable or uncountable
20. Correctly use the epsilon definition of greatest lower bound and least upper bound in proofs
21. Correctly apply the concepts of open and closed sets to proofs
22. Correctly apply the concepts of limit points, deleted neighborhoods and closure to Proofs
23. Correctly decide if a sequence is monotone and/or bounded
24. Prove that a sequence converges to a limit, using the definition of convergence
25. Correctly decide if a function is bounded or monotone
.10.j
Math 3435 Introductory Linear Algebra
1. Students will be able to identify a system of linear equations and form the augmented matrix for
the system.
2. Students will be able to identify when a matrix is in row echelon form or reduced row echelon
form.
3. Students will be able to identify when an augmented matrix in row echelon form corresponds to
an inconsistent system, a system with a single solution, or a system with multiple solutions.
4. Students will be able to use elementary row operations to reduce an augmented matrix to row
echelon form and to use the form, together with back-substitution, to solve the corresponding
system.
5. Students will be able to perform algebraic operations on vectors in n-dimensional space.
6. Students will be able to interpret the geometric properties of vectors in R n and of algebraic
operations on vectors in R n.
7. Students will know the definitions of a linear combination and of the span of a set of vectors
and the geometric significance of a vector being in the span of a set of vectors.
8. Students will be able to represent a set of linear equations as a combination of the columns of
the system matrix A and also as the matrix-vector product Ax.
9. Students will recognize consistent systems as those in which the right hand side is a
combination of the columns of the system matrix A.
10. Students will be able to computationally determine if a given set of vectors is linearly
independent and determine if a given vector is in the span of a set of vectors.
11. Students will know the definition of a linear transformation and will be able to represent linear
transformations as matrices.
12. Students will be able to identify one to one and onto linear transformations.
13. Students will be able to apply the theory of linear systems to simple applied problems.
14. Students will be able to apply basic matrix operations, including products, sums and transposes.
15. Students will be able to determine if two matrices are inverses of each other.
16. The student will be able to deduce the uniqueness of solutions from invertibility.
17. Students will be able to compute the inverse matrix using elementary row operations.
18. Students will be able to apply the equivalent conditions of the invertible matrix theorem to
determine if matrices are invertible.
19. Students will be able to apply the definition of the determinant to compute the determinant of a
matrix.
20. The student will know the effect of elementary row operations on the determinant.
21. Students will be able to compute determinants using elimination.
22. Students will know the properties and definition of a vector space and be able to apply these
properties in computations involving vectors.
23. Students will be able to tell if a given set is a subspace.
24. Students will be able to find the null space and column space of a matrix and be able to relate
them to kernel and range of a linear transformation.
25. Students will be able to apply the definition of linear independence and to recognize linearly
independent and linearly dependent sets of vectors.
26. Students will be able to recognize a basis for a subspace and be able to construct a basis for the
span of a set of vectors.
27. Students will be able to define a coordinate system with a basis and be able to find the
coordinates of a vector with respect to a given basis.
28. Students will be able to change a basis and represent the basis change as a matrix.
29. Students will be able to determine the dimension of a subspace.
30. Students will be able to compute the rank of a matrix and to relate the rank to the dimension of
the null and column spaces of a matrix.
31. Students will be able to describe a Markov chain using its probability transition matrix.
32. Students will be able to find the steady state of a Markov chain.
.10.k
Math 4441 Modern Algebra I
1. Elements of set theory.
2. Equivalence and order relations.
3. Semigroups, monoids.
4. Groups, subgroups, normal subgroups, Lagrange's theorem. Construction of the integers.
5. Homomorphisms, isomorphisms, kernel, image.
6. Quotient groups.
7. Isomorphisms theorems for groups.
8. Center, normalizer, centralizer.
9. Direct products.
10. Cauchy's theorem.
11. Permutation groups.
12. Structure of finitely generated abelian groups (time permitting).
13. Action of groups on sets, Sylow theorems, semidirect products (time permitting).
.10.l
Math 4661 Introduction to Analysis I
1. The Real Number System. Students will demonstrate an understanding of the axiomatic
structure of the real number system. This includes notions such as countable and uncountable
sets, completeness and ordering principles, and Cantor sets.
2. Topology of Metric Spaces. Students will exhibit knowledge of metric properties and
topological concepts including open and closed sets, compact and connected sets in the context
of metric spaces.
3. Sequences. Students will understand and be able to use various concepts regarding sequences
such as the limit of a sequence, subsequences, Cauchy sequences, comparison theorems, and the
Bolzano-Weierstrass Theorem.
4. Series. Students will demonstrate an understanding of the main theorem on convergence of
series, and notions like positive series, absolutely convergent series, alternating series, and
power series. They should be able to apply various criteria for convergence of a series.
5. Limits and Continuity. Students will be familiar with the rigorous epsilon and delta treatment of
limits of functions between metric spaces and different characterizations of continuity. They
will understand the notion of uniform continuity, will know the properties of continuous
function on compact and connected sets, and will be able to classify discontinuities of real
functions.
6. Mathematical Proofs. All results in this course will be rigorously proven. Students will develop
an ability to read, understand, and reproduce proofs in this course.
.10.m
Math 4662 Introduction to Analysis II
1. Differentiation. Students will understand the concept of derivative of a real function and
different Mean-Value Theorems and their consequences. They will also understand and be able
to apply L’Hospital’s Rule and various theorems involving derivates of higher order.
2. Integrability. Students will demonstrate an understanding of the rigorous treatment of the
Riemann Integral based on Riemann and Darboux sums, the properties of the integral, and the
Fundamental Theorem of Calculus.
3. Sequences and Series of Functions. Students will exhibit understanding of the concepts of
pointwise and uniform convergence of sequences of functions and uniform convergence of
series of functions. They will be able to apply theorems on uniform convergence of sequences
of continuous, differentiable, and integrable functions as well as power and Taylor series,
respectively analytic functions.
4. Functions of Several Variables. Students will understand the generalization of the concept of
derivative and Riemann integral to several variables.
5. Mathematical Proofs. All results in this course will be presented with proofs. Students will
further develop their ability to read, understand, and reproduce proofs in the area of
Mathematical Analysis. They will become familiar with various new proof techniques.
.10.n
Math 4751 Mathematical Statistics I
1. Students will understand the basic probability theory.
2. Students will understand the concepts of probability distributions and distribution functions.
3. Students will understand how the most commonly used discrete as well as continuous
distributions arise in the real world.
4. Students will understand the random variables and their distributions.
5. Students will understand the concept of joint distributions.
6. Students will understand the mathematical expectations of random variables.
7. Students will understand the moments, joint moments and moment generating functions of the
random variables.
8. Students will be able to apply the various techniques they learned in calculus to the field of
Statistics.
9. Students will be able to derive the marginal and conditional distributions from the joint
distributions.
10. Students will be able to derive the distributions of the functions of random variables by various
different methods.
.11 Degree Requirements B.S. in Mathematics
.11.a
No Concentration
Area F: Courses Appropriate to the Major Field (18)
Required Courses: Select the course(s) not taken in Area A or D. (7-11)
MATH 2211 Calculus of One Variable I (4)
MATH 2212 Calculus of One Variable II (4)
MATH 2215 Multivariate Calculus (4)
MATH 2420 Discrete Mathematics (3)
Select additional courses to complete 18 hours in Area F. (7-11)
ACCT 2101, ACCT 2102, BIOL 2107K, BIOL 2108K, CHEM 1211K, CHEM 1212K, CHEM 2400,
CSC 2010, CSC 2310, ECON 2105, ECON 2106, Lang 2001, Lang 2002, PHIL 1010, PHYS 2211K,
PHYS 2212K
Area G: Major Courses (33)
Required Courses to fulfill CTW requirement (6)
MATH 3000 Bridge to Higher Mathematics-CTW (3)
MATH 4991 Senior Seminar-CTW (3)
Required Courses (18)
MATH 3435 Introductory Linear Algebra (3)
MATH 4435 Linear Algebra (3)
MATH 4441 Modern Algebra (3)
MATH 4442 Modern Algebra II (3)
MATH 4661 Analysis I (3)
MATH 4662 Analysis II (3)
MATH 4751 Mathematical Statistics I (3)
Mathematics Electives: Select 12 additional hours of 3000/4000-level mathematics courses, of which
six hours at most may be at the 3000 level (excluding Math 3030, 3050, 3070, and 3090).
.11.b
Concentration in Actuarial Science
Students must receive credit for the calculus courses Math 2211, 2212, and 2215 and for Math 2420,
CSc 2010, CSc 2310, Econ 2105 and Econ 2106 in the core curriculum Areas A-F or as electives.
Required Courses to fulfill CTW requirement (6)
MATH 3000 Bridge to Higher Mathematics-CTW (3)
MATH 4991 Senior Seminar-CTW (3)
Mathematics Requirements (24)
MATH 3435 Introductory Linear Algebra (3)
MATH 4211 Optimization (3)
MATH 4435 Linear Algebra (3)
MATH 4610 Numerical Analysis I (3)
MATH 4661 Analysis I (3)
Either MATH 4662 Analysis II or MATH 4441 Modern Algebra I (3)
MATH 4751 Mathematical Statistics I (3)
MATH 4752 Mathematical Statistics II (3)
Actuarial Science Requirements (15)
AS 4140 Mathematical Foundations of Actuarial Science (3)
AS 4230 Theory of Interest (3)
AS 4340 Life Contingencies I (3)
Two of AS 4320, AS 4350, or AS 4510 (6)
Required Economics Courses (6) (if not completed in Area F)
ECON 2105 Principles of Macroeconomics (3)
ECON 2106 Principles of Microeconomics (3)
.11.c
Concentration in Computer Information Systems
Required Courses to fulfill CTW requirement (6)
MATH 3000 Bridge to Higher Mathematics-CTW (3)
MATH 4991 Senior Seminar-CTW (3)
Mathematics Requirements (15)
MATH 3435 Introductory Linear Algebra (3)
MATH 4435 Linear Algebra (3)
MATH 4661 Analysis I (3)
Either MATH 4662 Analysis II or MATH 4441 Modern Algebra I (3)
MATH 4751 Mathematical Statistics I (3)
Select one additional upper-level mathematics course (exclusive of Math 3030, 3050, 3070, and 3090).
(3)
Computer Science Requirements (12-15)
CSC 2310 Principles of Computer Programming I (3)
CSC 3210 Computer Organization and Programming (3)
CSC 3410 Data Structures-CTW (3)
Select one of the following:
CSC 3320 System-Level Programming (3)
CSC 4210 Computer Architecture (4)
CSC 4320 Operating Systems (4)
Computer Information Systems Requirements (12)
CIS 3210 End User Applications Programming (3)
CIS 3300 Systems Analysis (3)
CIS 3310 Systems Design (3)
Select one additional CIS course, preapproved by the director of undergraduate advisement. (3)
.11.d
Concentration in Computer Science
Required Courses to fulfill CTW requirement (6)
MATH 3000 Bridge to Higher Mathematics-CTW (3)
MATH 4991 Senior Seminar-CTW (3)
Mathematics Requirements (15)
MATH 3435 Introductory Linear Algebra (3)
MATH 4435 Linear Algebra (3)
MATH 4661 Analysis I (3)
MATH 4662 Analysis II (3)
MATH 4751 Mathematical Statistics I (3)
Select one additional upper-level mathematics course (exclusive of Math 3030, 3050, 3070, and 3090).
(3)
Computer Science Requirements (16)
CSC 3210 Computer Organization and Programming (3)
CSC 3410 Data Structures-CTW (3)
CSC 4520 Design and Analysis of Algorithms (4)
CSC 4610 Numerical Analysis I (3)
CSC 4620 Numerical Analysis II (3)
Additional Computer Science Courses (6-8)
Select two additional upper-level computer science courses with at least one selected from the
following:
CSC 3320 System-Level Programming (3)
CSC 4210 Computer Architecture (4)
CSC 4330 Programming Language Concepts (4)
CSC 4350 Software Engineering-CTW (4)
Additional courses must be taken as electives to complete a minimum of 120 semester hours, exclusive
of KH 1010. (6-8)
.11.e
Concentration in Managerial Sciences
Required Courses to fulfill CTW requirement (6)
MATH 3000 Bridge to Higher Mathematics-CTW (3)
MATH 4991 Senior Seminar-CTW (3)
Mathematics Requirements (12)
MATH 3435 Introductory Linear Algebra (3)
MATH 4435 Linear Algebra (3)
MATH 4661 Analysis I (3)
MATH 4662 Analysis II (3)
Statistics Requirements: Select a two-course sequence. (6)
MATH 4751 Mathematical Statistics I (3) and
MATH 4752 Mathematical Statistics II (3) or
MATH 4547 Introduction to Statistical Methods (3) and
MATH 4548 Methods of Regression and Analysis of Variance (3)
Two additional courses in mathematics and/or computer science (6-7) (exclusive of Math 3030, 3050,
3070, and 3090), including one of the following:
MATH 4211 Optimization (3) or
CSC 4830 System Simulation (4)
Managerial Sciences Requirements (15)*
MGS 3100 Business Analysis (3)
MGS 4000 Managerial Decision Making (3)
MGS 4020 Introduction to Business Intelligence (3)
MGS 4110 Analysis of Business Data (3)
MGS 4120 Optimal Resource Allocation (3)
Managerial Sciences Electives: Select one course. (3)
MGS 4140 Business Modeling (3)
MGS 4760 Total Quality Management (3)
.11.f
Concentration in Statistics
Required Courses to fulfill CTW requirement (6)
MATH 3000 Bridge to Higher Mathematics-CTW (3)
MATH 4991 Senior Seminar-CTW (3)
Mathematics and Statistics Requirements. (21)
MATH 3435 Introductory Linear Algebra (3)
MATH 4435 Linear Algebra (3)
MATH 4548 Methods of Regression and Analysis of Variance (3)
MATH 4661 Analysis I (3)
MATH 4662 Analysis II (3)
MATH 4751 Mathematical Statistics I (3)
MATH 4752 Mathematical Statistics II (3)
Select one course. (3-4)
MATH 4544 Biostatistics (3)*
MATH 4547 Introduction to Statistical Methods (3)*
MATH 4767 Statistical Computing (3)
CSC 4830 System Simulation (4)
*At most, one semester of Math 4544 and 4547 may be counted in this program.
Select one course. (3)
MATH 4211 Optimization (3)
MATH 4610 Numerical Analysis I (3)
Any one of the courses not taken in group 2 (3)
Related Field Courses: Select 12 hours of course work in a field other than mathematics. (12) Related
field courses must be pre-approved by a faculty adviser and must include at least nine hours of upperdivision course work. Examples of related fields are: Actuarial Science, Biology, Computer
Information Systems, Computer Science, Economics, Managerial Sciences, and Marketing.
.12 Course Descriptions
MATH
0098
Elementary Algebra
Credit Hours 4.0
(Formerly LSP 0098.) Topics include review of real numbers (order of operations,
fractions, decimals, percents, and integers), solving and graphing linear equations and
Description inequalities, operations with polynomials. An introduction to solving systems of linear
equations and inequalities, factoring, and operations with rationales. Applications will be
emphasized.
MATH
0099
Intermediate Algebra
Credit Hours 4.0
(Formerly LSP 0099.) A transition from elementary algebra to college algebra. Topics
include operations with radicals, graphing of linear and nonlinear functions, algebra of
Description
linear and nonlinear functions, systems of linear equations and inequalities, review of
factoring and quadratic functions. Applications will be emphasized.
MATH
1070
Elementary Statistics
Credit Hours 3.0
Prerequisites High School Algebra II or equivalent
Descriptive statistics, basic probability, and distribution of random variables,
estimation and hypothesis tests for means and proportions, regression and correlation,
Description
analysis of count data.
MATH
1090
Honors Statistics
Credit Hours 3.0
Prerequisites consent of Honors Program director
Nondeterministic conceptualizations of phenomena as a foundation for inference.
Descriptive and inferential methods of statistics, including synopses of real
Description
experiments, means, variances, regression and correlation, probability, sampling,
hypotheses testing.
MATH
1101
Introduction to Mathematical Modeling
Credit Hours 3.0
Prerequisites High School Algebra II or equivalent
Three lecture hours a week. Mathematical modeling using graphical, numerical,
symbolic, and verbal techniques to describe and explore real-world data and
Description phenomena. Emphasis is on the use of elementary functions to investigate and analyze
applied problems and questions, on the use of appropriate supporting technology, and
on the effective communication of quantitative concepts and results.
MATH 1111 College Algebra
Credit Hours 3.0
Prerequisites MATH 0099 with grade of C or higher or a suitable score on the math placement test
Graphs; equations and inequalities; complex numbers; functions; polynomial, rational,
Description
exponential, and logarithmic functions; and linear systems.
MATH
1112
College Trigonometry
Credit Hours 3.0
Prerequisites MATH 1111 with a grade of C or higher, or appropriate score on the placement test
This course is a functional and visual approach to trigonometry that incorporates the use
of appropriate technology. Emphasis will be placed on the study of trigonometric
functions and their graphs incorporating a unit circle approach as well as special
Description
triangles angles. Topics include circular functions, solutions of triangles, trigonometric
identities and equations, graphs of trigonometric functions, Law of Sines, Law of
Cosines, and vectors. Appropriate applications will be included.
MATH 1113 Precalculus
Credit Hours
Prerequisites
Description
3.0
MATH 1111 with grade of C or higher or a suitable score on the math placement test,
or departmental approval
Trigonometric functions, identities, inverses, and equations; vectors; polar coordinates;
conic sections.
MATH 1220 Survey of Calculus
Credit Hours
Prerequisites
Description
MATH
2008
3.0
MATH 1111 with grade of C or higher
Differential and integral calculus of selected real-valued functions of one and several
real variables with applications.
Foundations of Numbers and Operations
Credit Hours 3.0
Prerequisites MATH 1001, MATH 1101, MATH 1111, or MATH 1113 with grade of C or higher
This course is an Area F introductory mathematics course for early childhood education
majors. This course will emphasize the understanding and use of the major concepts of
Description
number and operations. As a general theme, strategies of problem solving will be used
and discussed in the context of various topics.
MATH
2201
Calculus for the Life Sciences I
Credit Hours 4.0
MATH 1112 or MATH 1113 with a grade of C or higher, or appropriate score on the
Prerequisites
placement test
Limits, derivatives and applications. Exponential and logarithmic functions. Integrals,
antiderivatives and the Fundamental Theorem of Calculus. Examples and Applications
Description
are drawn from the life sciences. This course is not appropriate for mathematics,
computer science, geology, and physics majors or minors.
MATH
2202
Calculus for the Life Sciences II
Credit Hours 4.0
Prerequisites MATH 2201 with a grade of C or higher
Matrices, functions of several variables, differential equations and solutions with
applications. Examples and applications are drawn from the life sciences. This course is
Description
not appropriate for mathematics, computer science, geology, and physics majors or
minors.
MATH
2211
Calculus of One Variable I
Credit Hours 4.0
Prerequisites MATH 1113 with grade of C or higher or a suitable score on the math placement test
Limits and Continuity, Differentiation, Mean Value Theorem for Derivatives;
applications of differentiation; definition of the integral; Fundamental Theorem of
Description
Calculus; applications of integration to area.
MATH
2212
Calculus of One Variable II
Credit Hours 4.0
Prerequisites MATH 2211 with grade of C or higher
Applications and techniques of integration; transcendental and trigonometric functions;
polar coordinates; infinite sequences and series; indeterminate forms; improper
Description
integrals.
MATH
2215
Multivariate Calculus
Credit Hours 4.0
Prerequisites
Description
MATH
2420
MATH 2212 with grade of C or higher
Real-valued functions of several variables, limits, continuity, differentials, directional
derivatives, partial derivatives, chain rule, multiple integrals, applications.
Discrete Mathematics
Credit Hours 3.0
Prerequisites MATH 1113 or MATH 1220 with grade of C or higher
Introduction to discrete structures which are applicable to computer science. Topics
include number bases, logic, sets, Boolean algebra, and elementary concepts of graph
Description
theory.
MATH
3000
Bridge to Higher Mathematics-CTW
Credit Hours 3.0
Prerequisites MATH 2212 and MATH 2420 with grades of C or higher
Topics from set theory, real numbers, analysis, and algebra, which illustrate a formal
approach to the presentation and development of mathematical concepts and proofs.
Description
Serves as one of the two Critical Thinking Through Writing (CTW) courses required of
all mathematics majors.
MATH
3030
Mathematical Models for Computer Science
Credit Hours 3.0
Prerequisites MATH 2212 and MATH 2420 or CSC 2510 with grades of C or higher
Elements of mathematical modeling including: multivariate functions, probability,
distributions of random variables, sampling, statistical inference, operators, vector
Description
analysis; elements of linear algebra.
MATH
3050
Geometry and Spatial Sense
Credit Hours 3.0
Prerequisites MATH 2030 or MATH 2008 with grade of C or higher
Building on Euclidean geometry this course is designed to develop a more visual
understanding of geometry and enhance geometric intuition in two- and threeDescription dimensions. Topics include measurement, two-dimensional geometry, threedimensional geometry, spherical geometry, symmetry, tesselations, efficient shapes,
transformations.
MATH
3070
Introduction to Probability and Statistics
Credit Hours 3.0
Prerequisites MATH 2030 or MATH 2008 with grade of C or higher, or consent of instructor
Three lecture hours a week. This course will not be accepted as a part of the
requirements for a major in mathematics. This course is intended to provide an
Description
overview of the basics of probability and descriptive statistics. Various forms of
technology will be used.
MATH
3090
Algebraic Concepts
Credit Hours 3.0
Prerequisites MATH 2030 or MATH 2008 with grade of C or higher
Description
This course will not be accepted as a part of the requirements for a major in
mathematics. This course is designed to broaden understanding of fundamental
concepts of algebra with particular attention given to specific methods and materials of
instruction. The principle algebra topics to be taught in this course are: the Language of
Algebra; Patterns, Relations, and Functions; and Balance, Equations, and Inequalities.
MATH 3260Differential Equations
Credit Hours 3.0
Prerequisites MATH 2215 with grade of C or higher
First-order equations, linear differential equations with special emphasis on constant
Description
coefficient and Euler equations, systems of equations, applications.
MATH
3300
Problem Solving with Computers
Credit Hours 3.0
Prerequisites MATH 3000 with grade of C or higher
Three lectures a week. This course explores various mathematical contexts and
develops mathematical knowledge necessary to solve, or attempt to solve, mathematical
problems in the computer enhanced environment. The problems come from many
Description
sources and contexts. Computer programs such as Maple, Matlab, spreadsheets,
Geometer’s Sketch Pad, Study Works, etc. will be used. No previous experience with
computers is required.
MATH
3420
Applied Combinatorics
Credit Hours 3.0
Prerequisites MATH 2212 or MATH 2420 with grade of C or higher
Counting principles; topics include combinations, permutations, generating functions,
recurrence relations, principle of inclusion and exclusion, and Polya’s theory of
Description
counting.
MATH 3435Introductory Linear Algebra
Credit Hours 3.0
Prerequisites MATH 2215 with grade of C or higher
Corequisites MATH 3000
Theory and applications of matrix algebra and linear transformations. Topics include
Description
linear equations, vector spaces, matrices, subspaces, and bases.
MATH
3610
Special Problems and Solving Strategies
Credit Hours 3.0
Prerequisites MATH 2212 with grade of B or higher, or consent of instructor
The course will concentrate on developing solving strategies of difficult mathematical
problems which require creativity and profound understanding of mathematics. Among
Description
topics to be covered: induction and pigeonhole principle, arithmetic, algebra,
summation of series, intermediate real analysis, inequalities.
MATH 3690 Honors Readings
Credit Hours
Prerequisites
Description
MATH
1.0 TO 3.0
consent of Honors Program director
Discussion and readings on selected topics.
Historical and Cultural Development of Mathematics I
3820
Credit Hours 3.0
Prerequisites MATH 1101 or MATH 1111 with grade of C or higher
Three lecture hours a week. Exploration of the historical and cultural development of
mathematics between ~3000 B.C. and ~A.D. 1600. Mathematics topics to include the
Description
development of arithmetic, geometry (practical, deductive, and axiomatic), number
theory, trigonometry, syncopated and symbolic algebra, probability, and statistics.
MATH
3821
Historical and Cultural Development of Mathematics II
Credit Hours 3.0
Prerequisites MATH 3000 with grade of C or higher
Three lecture hours a week. Exploration of the historical and cultural development of
mathematics from ~A.D. 1600 to present. Mathematics topics to include the
Description
development of algebraic geometry, logarithms, calculus, non-Euclidean geometry,
abstract algebra, probability, and analysis.
MATH
4010
Mathematical Biology
Credit Hours 3.0
Prerequisites MATH 2212 or MATH 1220 with grade of C or higher
(Same as BIOL 4010.) This course provides an introduction to the use of continuous
and discrete differential equations in the biological sciences. Biological topics will
include single species and interacting population dynamics, modeling infectious and
dynamic diseases, regulation of cell function, molecular interactions and receptor-ligand
binding, biological oscillators, and an introduction to biological pattern formation.
Description
There will also be discussions of current topics of interest such as Tumor Growth and
Angiogenesis, HIV and AIDS, and Control of the Mitotic Clock. Mathematical tools
such as phase portraits, bifurcation diagrams, perturbation theory, and parameter
estimation techniques that are necessary to analyze and interpret biological models will
also be covered.
MATH
4211
Optimization
Credit Hours 3.0
Prerequisites MATH 3435 or MATH 3030 with a grade of C or higher
Lagrange multipliers, gradient methods (steepest descent), search techniques,
variational methods and control problems; other varying topics such as dynamic
Description
programming, nonlinear programming.
MATH 4250 Complex Analysis
Credit Hours
Prerequisites
Description
3.0
MATH 3000 with grade of C or higher
Complex numbers, analytic functions, complex series, Cauchy theory, residue
calculus, conformal mapping.
MATH 4258Vector Calculus
Credit Hours 3.0
Prerequisites MATH 2215 with grade of C or higher
(Same as PHYS 4510.) Vector algebra, curvilinear motion, vector fields, gradient,
Description
divergence, Laplacian, line and surface integrals, integral theorems.
MATH
4265
Partial Differential Equations
Credit Hours 3.0
Prerequisites MATH 3260 with grade of C or higher
(Same as PHYS 4520.) First-order equations, classification of linear second-order
equations, separation of variables, Fourier series, orthogonal functions, Green’s
Description
functions.
MATH
4275
Applied Dynamical Systems
Credit Hours 3.0
Prerequisites MATH 3260, and MATH 3435 or MATH 3030 with grades of C or higher
Three lecture hours per week. An introduction to discrete and continuous dynamical
systems. Topics include: phase space; linear and nonlinear systems; structural stability;
classification of equilibrium states, invariant manifolds; poincare maps, fixed points
Description
and period orbits; stability boundaries; local bifurcations; homoclinic orbits; routes to
chaos in dissipative systems; applications from physics, biology, population dynamics,
economics.
MATH
4301
College Geometry
Credit Hours 3.0
Prerequisites MATH 3000 with grade of C or higher
Axioms of planar Euclidean Geometry. The 5th postulate. Congruence and Similarity.
Theorem of Thales. Similar Triangles: SAS, AA, and SSS. Theorem of Ceva. The
Pythagorean Theorem. Polygons. Circles, secants and tangents, measurement of an
Description angle with respect to a circle. Perimeters, areas, circumference. Inscribed and
circumscribed polygons. Coordinate Geometry in the plane. Mirror symmetries,
rotations, translations and dilations. Isometries and the fundamental theorem of
Euclidean Geometry. Transformations in the plane and tessellations.
MATH 4371 Modern Geometry
Credit Hours
Prerequisites
Description
MATH
4391
3.0
MATH 3000 with grade of C or higher
Euclidean and non-Euclidean geometry, including incidence, order, and the parallel
postulate.
Introduction to Differential Geometry and Its Applications
Credit Hours 3.0
Prerequisites MATH 2215 with grade of C or higher
(Same as PHYS 4391.) Three lecture hours a week. The theory of curves and surfaces
in parametric and implicit form. Curvature and torsion of a curve; the shape operator
and the total and mean curvature of a surface. The Gauss-Weingarten equations; the
Description Egregium Theorem; surfaces of constant curvature and non-Euclidean geometry.
Minimal surfaces; the Gauss Bonnet Theorem; submanifolds in Euclidian spaces, vector
fields, differential forms, and the theorems of Frobenius and Stokes. Applications to
physics.
MATH
4420
Graph Theory
Credit Hours 3.0
Prerequisites MATH 3000 with grade of C or higher
Introduction to graph theory; topics include structure of graphs, trees, connectivity,
Eulerian and Hamiltonian graphs, planar graphs, graph colorings, matchings,
Description independence, and domination. Additional topics may include symmetry of graphs,
directed graphs, extremal graph theory and Ramsey theory, graph embeddings, and
probabilistic methods in graph theory.
MATH
4435
Linear Algebra
Credit Hours 3.0
Prerequisites MATH 3435 and MATH 3000 with grades of C or higher
Theory and applications of matrix algebra, vector spaces, and linear transformations;
Description
topics include characteristic values, the spectral theorem, and orthogonality.
MATH 4441 Modern Algebra I
Credit Hours
Prerequisites
Description
3.0
MATH 3435 and MATH 3000 with grades of C or higher
Axiomatic approach to algebraic structures, groups, permutations, homomorphisms,
and factor groups.
MATH 4442 Modern Algebra II
Credit Hours
Prerequisites
Description
MATH
4444
3.0
MATH 4441 with grade of C or higher
Rings, integral domains, and fields; polynomials over a field, matrices over a field,
algebraic numbers and ideals.
Polynomials
Credit Hours 3.0
Prerequisites MATH 3000 with grade of C or higher
Three lecture hours a week. The topic of polynomials is one of the oldest in
mathematics and has applicability to almost every area of mathematics. The course will
use algebra and analysis to study polynomials. Among topics to be covered: roots of
Description
polynomials (inequalities, relationship between the root of a polynomial and its
derivative), resultants, discriminant, irreducible polynomials, special classes of
polynomials (symmetric, cyclotomic, Chebysev).
MATH 4450 Theory of Numbers
Credit Hours
Prerequisites
Description
MATH
4455
3.0
MATH 3000 with grade of C or higher
Properties of integers, divisibility, congruence of problems.
Error Correcting Codes
Credit Hours 3.0
Prerequisites MATH 3030 or MATH 3435 with grade of C or higher
Three lectures a week. This course provides an elementary, yet rigorous introduction to
the theory of error correcting codes. Topics include survey of groups, finite fields and
Description
polynomials, linear algebra, Huffman codes, data compression and entropy, linear
codes, Reed-Muller codes, cyclic codes, BCH codes, and fast decoding BCH codes.
MATH
Cryptography
4460
Credit Hours 3.0
MATH 3030 or MATH 3435 with grade of C or higher and the ability to program highPrerequisites
level language
Three lectures a week. This course covers the mathematical background of
computational and algorithmic methods for cryptography. This includes information
Description
theory, computational complexity and number theory. Methods covered incluce public
key cryptosystems and secure methods for authentication and digital signatures.
MATH
4544
Biostatistics
Credit Hours 3.0
MATH 2211 and BIOL 1104K or BIOL 1108K or BIOL 2108K with grades of C or
Prerequisites
higher
(Same as BIOL 4744.) Degree credit will not be given for both MATH
4544 and MATH 4547. Principles and methods of statistics as applied to biology and
Description
medicine.
MATH
4547
Introduction to Statistical Methods
Credit Hours 3.0
Prerequisites grade of C or higher in a course in calculus
Degree credit will not be given for both MATH 4544 and MATH 4547. Data analysis,
sampling, and probability; standard methods of statistical inference, including t-tests,
Description
chi-square tests, and nonparametric methods. Applications include use of a statistical
computer package.
MATH
4548
Methods of Regression and Analysis of Variance
Credit Hours 3.0
grade of C or higher in a course in calculus, and a course covering methods of
Prerequisites
statistical inference
Simple and multiple regression, model selection procedures, analysis of variance,
simultaneous inference, design and analysis of experiments. Applications include use
Description
of a statistical computer package.
MATH
4610
Numerical Analysis I
Credit Hours 3.0
MATH 2215 with grade of C or higher and the ability to program in a high-level
Prerequisites
language
(Same as CSC 4610.) Nature of error; iteration; techniques for nonlinear systems; zeros
of functions; interpolation; numerical differentiation; Newton-Cotes formulae for
Description
definite integrals; computer implementation of algorithms.
MATH
4620
Numerical Analysis II
Credit Hours 3.0
MATH 3030 or MATH 3045 with grade of C or higher, and the ability to program in a
Prerequisites
high-level language
(Same as CSC 4620.) Gaussian Elimination for linear systems; least squares; Taylor,
Description
predictor-corrector and Runge- Kutta methods for solving ordinary differential
equations; boundary value problems; partial differential equations.
MATH
4650
Inverse and Ill-Posed Problems
Credit Hours 3.0
MATH 3030 or MATH 3435, and Math/CSC 4610 or Math/CSC 4620 with grades of C
Prerequisites
or higher
Three lecture hours a week. Ill-posed problems that arise in astrophysics, geophysics,
spectroscopy, computerized tomography, and other areas of science and engineering are
Description considered in this course. Topics to be covered: a general regularization theory;
variational regularization and the discrepancey principle; iterative regularization;
convergence analysis and stopping rules; numerical aspects.
MATH 4661 Analysis I
Credit Hours
Prerequisites
Corequisites
Description
3.0
MATH 3435 with grade of C or higher
MATH 4435
The real number system, basic topology of metric spaces, sequences and series, limits
and continuity.
MATH 4662Analysis II
Credit Hours 3.0
Prerequisites MATH 4661 with grade of C or higher
Differentiation of real functions, Reimann integrals, sequences and series of functions,
Description
differentation and integration of functions of several variables.
MATH 4671 Transforms in Applied Mathematics
Credit Hours
Prerequisites
Description
3.0
MATH 3030 or MATH 3435 with grade of C or higher
The Laplace transform, discrete and continuous Fourier Transforms, z-transforms,
discrete filters, and wavelets.
MATH 4751 Mathematical Statistics I
Credit Hours
Prerequisites
Description
3.0
MATH 2215 with grade of C or higher
Probability, random variables and their distributions, mathematical expectation,
moment generating functions, sampling distributions.
MATH 4752 Mathematical Statistics II
Credit Hours
Prerequisites
Description
MATH
4767
3.0
MATH 4751 with grade of C or higher
Theory of estimation and hypothesis testing, applications of statistical inference,
introduction to regression and correlation.
Statistical Computing
Credit Hours 3.0
MATH 4752 or MATH 4548, and MATH 3435 with grades of C or higher and the
Prerequisites
ability to program in a high-level language
Computational implementation of statistical methods such as descriptive statistics and
Description graphs, testing for normality, one and two sample tests, Wilcoxon rank sum tests,
Wilcoxon signed rank tests, basic regression and analysis of variance (ANOVA).
Standard statistical packages (SAS) will be used as well as user-written programs. 3.0
credit hours.
MATH 4870 Honors Thesis: Research
Credit Hours
Prerequisites
Description
3.0
consent of the instructor and Honors Program director
Readings or research preparatory to Honors thesis or project.
MATH 4880 Honors Thesis: Writing
Credit Hours
Prerequisites
Description
3.0
MATH 4870, consent of the instructor and Honors Program director
Writing or production of Honors thesis or project.
MATH 4982 Undergraduate Research in Mathematics
Credit Hours
Prerequisites
Description
MATH
4991
3.0
at least 12 upper-division hours in mathematics with grades of C or higher
Authorization required. Independent investigation of topics of common interest to
student and instructor.
Senior Seminar-CTW
Credit Hours 3.0
Prerequisites MATH 4435 with grade of C or higher
This course introduces students to independent research in mathematics and related
areas. Serves as one of the two Critical Thinking Through Writing (CTW) courses
Description
required of all mathematics majors.
MATH
4995
Directed Readings B.I.S.-CTW
Credit Hours 3.0 TO 4.0
Directed Readings designed for Bachelor of Interdisciplinary Studies students. This
Description course may satisfy the junior and/or senior-level Critical Thinking Through Writing
requirements.
MATH 4998 Selected Topics
Credit Hours
Prerequisites
Description
1.0 TO 3.0
consent of instructor
No more than six credit hours may be applied toward the major. May be repeated if
topics are different.
.13 List of Courses
Year
Level
Course
FY 2011
CORE
FY 2011
FY 2011
Cross List
Sections
Students
Avg.
Students
per section
MATH 1070
73.0
3257
44.6
CORE
MATH 1101
60.0
2564
42.7
CORE
MATH 1111
30.0
1096
36.5
FY 2011
CORE
MATH 1113
28.0
1173
41.9
FY 2011
CORE
MATH 1220
4.0
133
33.3
FY 2011
CORE
MATH 2211
26.0
942
36.2
FY 2011
CORE
MATH 2212
11.0
628
57.1
FY 2011
CORE
MATH 2215
7.0
223
31.9
FY 2011
CORE
MATH 2420
5.0
148
29.6
FY 2011
LOWER
MATH 0098
1.0
19
19.0
FY 2011
LOWER
MATH 0099
7.0
326
46.6
FY 2011
LOWER
MATH 2008
7.0
227
32.4
FY 2011
UPPER
BIOL 4010
BIOL
0.4
6010/MAT
H
4010/MAT
H 6010
9
23.0
FY 2011
UPPER
BIOL 4744
BIOL
0.7
6744/MAT
H
4544/MAT
H 6544
17
25.0
FY 2011
UPPER
BIOL 4744
BIOL
0.4
6744/MAT
H 6544
11
27.0
FY 2011
UPPER
CSC 4610
MATH
0.2
4610/MAT
H 6610
4
25.0
FY 2011
UPPER
MATH 3000
3.0
57
19.0
FY 2011
UPPER
MATH 3030
3.0
78
26.0
FY 2011
UPPER
MATH 3050
6.0
154
25.7
FY 2011
UPPER
MATH 3050
1.9
88
45.5
MATH
7050
FY 2011
UPPER
MATH 3070
2.0
44
22.0
FY 2011
UPPER
MATH 3070
3.6
155
43.5
FY 2011
UPPER
MATH 3090
3.0
105
35.0
FY 2011
UPPER
MATH 3090
3.7
128
34.8
FY 2011
UPPER
MATH 3260
2.0
62
31.0
FY 2011
UPPER
MATH 3420
0.4
7
20.0
FY 2011
UPPER
MATH 3435
3.0
45
15.0
FY 2011
UPPER
MATH 3820
MATH
7820
0.3
12
39.0
FY 2011
UPPER
MATH 4010
BIOL
0.1
4010/BIOL
6010/MAT
H 6010
3
23.0
FY 2011
UPPER
MATH 4211
MATH
6211
0.8
18
24.0
FY 2011
UPPER
MATH 4250
MATH
6250
0.8
4
5.0
FY 2011
UPPER
MATH 4258
MATH
0.8
6258/PHY
S 6510
18
23.0
FY 2011
UPPER
MATH 4265
PHYS
0.8
4520/PHY
S 6520
17
22.0
FY 2011
UPPER
MATH 4275
MATH
6275
0.4
4
10.0
FY 2011
UPPER
MATH 4301
MATH
6301
0.5
10
20.8
FY 2011
UPPER
MATH 4371
MATH
6371
0.5
7
13.0
FY 2011
UPPER
MATH 4435
MATH
6435
1.8
56
31.0
MATH
7070
MATH
7090
MATH
7420
FY 2011
UPPER
MATH 4441
MATH
6441
1.8
31
16.8
FY 2011
UPPER
MATH 4442
MATH
6442
0.2
3
14.0
FY 2011
UPPER
MATH 4444
MATH
6444
0.6
8
14.0
FY 2011
UPPER
MATH 4544
BIOL
0.0
4744/BIOL
6744/MAT
H 6544
1
25.0
FY 2011
UPPER
MATH 4547
MATH
6547
1.9
40
21.0
FY 2011
UPPER
MATH 4548
MATH
6548
0.8
10
13.0
FY 2011
UPPER
MATH 4610
CSC
0.5
4610/MAT
H 6610
12
25.0
FY 2011
UPPER
MATH 4620
MATH
6620
0.3
5
15.0
FY 2011
UPPER
MATH 4661
MATH
6661
1.6
34
20.7
FY 2011
UPPER
MATH 4662
MATH
6662
1.6
33
20.9
FY 2011
UPPER
MATH 4751
MATH
6751
2.4
80
34.0
FY 2011
UPPER
MATH 4752
MATH
6752
2.3
44
19.0
FY 2011
UPPER
MATH 4982
3.0
3
1.0
FY 2011
UPPER
MATH 4991
2.0
33
16.5
FY 2011
UPPER
MATH 4999
2.0
2
1.0
FY 2011
UPPER
PHYS 4520
MATH
0.0
4265/PHY
S 6520
1
22.0
FY 2011
GRADUATE BIOL 6010
BIOL
0.3
4010/MAT
H
4010/MAT
H 6010
7
23.0
FY 2011
GRADUATE BIOL 6744
BIOL
0.2
4744/MAT
H
4544/MAT
H 6544
5
25.0
FY 2011
GRADUATE BIOL 6744
BIOL
0.5
4744/MAT
H 6544
13
27.0
FY 2011
GRADUATE MATH 6010
BIOL
0.2
4010/BIOL
6010/MAT
H 4010
4
23.0
FY 2011
GRADUATE MATH 6211
MATH
4211
0.3
6
24.0
FY 2011
GRADUATE MATH 6250
MATH
4250
0.2
1
5.0
FY 2011
GRADUATE MATH 6258
MATH
4258/
0.1
3
23.0
FY 2011
GRADUATE MATH 6275
MATH
4275
0.6
6
10.0
FY 2011
GRADUATE MATH 6301
1.0
19
19.0
FY 2011
GRADUATE MATH 6301
MATH
4301
1.5
32
21.1
FY 2011
GRADUATE MATH 6371
MATH
4371
0.5
6
13.0
FY 2011
GRADUATE MATH 6435
1.0
14
14.0
FY 2011
GRADUATE MATH 6435
MATH
4435
1.2
38
31.8
FY 2011
GRADUATE MATH 6441
MATH
4441
1.2
22
19.0
FY 2011
GRADUATE MATH 6442
1.0
1
1.0
FY 2011
GRADUATE MATH 6442
MATH
4442
0.8
11
14.0
FY 2011
GRADUATE MATH 6444
MATH
4444
0.4
6
14.0
FY 2011
GRADUATE MATH 6544
BIOL
0.1
4744/BIOL
6744
3
27.0
FY 2011
GRADUATE MATH 6544
BIOL
0.1
4744/BIOL
6744/MAT
H 4544
2
25.0
FY 2011
GRADUATE MATH 6547
1.0
11
11.0
FY 2011
GRADUATE MATH 6547
MATH
4547
1.1
37
33.9
FY 2011
GRADUATE MATH 6548
MATH
4548
0.2
3
13.0
FY 2011
GRADUATE MATH 6610
CSC
0.4
4610/MAT
H 6610
9
25.0
FY 2011
GRADUATE MATH 6620
MATH
4620
0.7
10
15.0
FY 2011
GRADUATE MATH 6661
MATH
4661
0.4
7
19.4
FY 2011
GRADUATE MATH 6662
MATH
4662
0.4
9
21.3
FY 2011
GRADUATE MATH 6751
MATH
4751
0.6
23
35.7
FY 2011
GRADUATE MATH 6752
MATH
4752
0.7
13
19.0
FY 2011
GRADUATE MATH 6999
1.0
1
1.0
FY 2011
GRADUATE MATH 7000
1.0
20
20.0
FY 2011
GRADUATE MATH 7008
2.0
33
16.5
FY 2011
GRADUATE MATH 7050
0.1
3
46.6
MATH
3050
FY 2011
GRADUATE MATH 7070
MATH
3070
0.4
19
43.1
FY 2011
GRADUATE MATH 7090
MATH
3090
0.3
10
30.8
FY 2011
GRADUATE MATH 7420
MATH
3420
0.7
13
20.0
FY 2011
GRADUATE MATH 7820
1.0
18
18.0
FY 2011
GRADUATE MATH 7820
0.7
27
39.0
FY 2011
GRADUATE MATH 7821
1.0
19
19.0
FY 2011
GRADUATE MATH 8110
1.0
14
14.0
FY 2011
GRADUATE MATH 8120
1.0
6
6.0
FY 2011
GRADUATE MATH 8200
1.0
23
23.0
FY 2011
GRADUATE MATH 8201
1.0
8
8.0
FY 2011
GRADUATE MATH 8220
1.0
13
13.0
FY 2011
GRADUATE MATH 8221
1.0
7
7.0
FY 2011
GRADUATE MATH 8250
1.0
5
5.0
FY 2011
GRADUATE MATH 8330
1.0
2
2.0
FY 2011
GRADUATE MATH 8420
1.0
11
11.0
FY 2011
GRADUATE MATH 8450
1.0
10
10.0
FY 2011
GRADUATE MATH 8515
1.0
6
6.0
FY 2011
GRADUATE MATH 8530
1.0
1
1.0
FY 2011
GRADUATE MATH 8560
0.8
4
5.0
FY 2011
GRADUATE MATH 8620
1.0
3
3.0
FY 2011
GRADUATE MATH 8800
2.0
9
4.5
FY 2011
GRADUATE MATH 8801
3.0
20
6.7
FY 2011
GRADUATE MATH 8802
8.0
37
4.6
FY 2011
GRADUATE MATH 8950
2.0
2
1.0
MATH
3820
NEUR
8360
FY 2011
GRADUATE MATH 8999
10.0
13
1.3
FY 2011
GRADUATE MATH 9116
1.0
15
15.0
FY 2011
GRADUATE MATH 9126
1.0
5
5.0
FY 2011
GRADUATE MATH 9136
1.0
4
4.0
FY 2011
GRADUATE MATH 9999
9.0
11
1.2
FY 2011
GRADUATE NEUR 8360
MATH
8560
0.2
1
5.0
FY 2011
GRADUATE PHYS 6510
MATH
0.1
4258/MAT
H 6258
2
23.0
FY 2011
GRADUATE PHYS 6520
MATH
0.2
4265/PHY
S 4520
4
22.0
FY 2011
GRADUATE STAT 8310
1.0
20
20.0
FY 2011
GRADUATE STAT 8320
1.0
7
7.0
FY 2011
GRADUATE STAT 8440
1.0
22
22.0
FY 2011
GRADUATE STAT 8540
1.0
18
18.0
FY 2011
GRADUATE STAT 8561
1.0
28
28.0
FY 2011
GRADUATE STAT 8582
1.0
13
13.0
FY 2011
GRADUATE STAT 8600
1.0
18
18.0
FY 2011
GRADUATE STAT 8630
1.0
28
28.0
FY 2011
GRADUATE STAT 8670
1.0
22
22.0
FY 2011
GRADUATE STAT 8678
1.0
21
21.0
FY 2011
GRADUATE STAT 8680
1.0
32
32.0
FY 2011
GRADUATE STAT 8691
5.0
20
4.0
FY 2011
GRADUATE STAT 8692
6.0
36
6.0
FY 2011
GRADUATE STAT 8800
1.0
12
12.0
FY 2011
GRADUATE STAT 8820
2.0
4
2.0
FY 2011
GRADUATE STAT 8900
1.0
11
11.0
FY 2011
GRADUATE STAT 8950
1.0
1
1.0
FY 2011
GRADUATE STAT 8999
19.0
51
2.7
FY 2011
GRADUATE STAT 9999
12.0
33
2.8
FY 2012
CORE
MATH 1070
71.0
3243
45.7
FY 2012
CORE
MATH 1101
55.0
2214
40.3
FY 2012
CORE
MATH 1111
31.0
1259
40.6
FY 2012
CORE
MATH 1113
28.0
1224
43.7
FY 2012
CORE
MATH 1220
5.0
142
28.4
FY 2012
CORE
MATH 2211
24.0
892
37.2
FY 2012
CORE
MATH 2212
15.0
627
41.8
FY 2012
CORE
MATH 2215
7.0
230
32.9
FY 2012
CORE
MATH 2420
5.0
145
29.0
FY 2012
LOWER
MATH 0098
1.0
12
12.0
FY 2012
LOWER
MATH 0099
9.0
378
42.0
FY 2012
LOWER
MATH 2008
7.0
210
30.0
FY 2012
UPPER
BIOL 4744
BIOL
0.4
6744/MAT
H
4544/MAT
H 6544
17
41.0
FY 2012
UPPER
BIOL 4744
BIOL
0.3
6744/MAT
H 6544
13
41.0
FY 2012
UPPER
CSC 4610
CSC
0.2
6610/MAT
5
30.0
H
4610/MAT
H 6610
FY 2012
UPPER
MATH 3000
4.0
99
24.8
FY 2012
UPPER
MATH 3030
3.0
96
32.0
FY 2012
UPPER
MATH 3050
5.0
125
25.0
FY 2012
UPPER
MATH 3050
2.8
90
31.7
FY 2012
UPPER
MATH 3070
3.0
62
20.7
FY 2012
UPPER
MATH 3070
3.6
156
43.5
FY 2012
UPPER
MATH 3090
5.0
139
27.8
FY 2012
UPPER
MATH 3090
2.8
112
39.8
FY 2012
UPPER
MATH 3260
2.0
89
44.5
FY 2012
UPPER
MATH 3420
0.9
17
18.0
FY 2012
UPPER
MATH 3435
3.0
79
26.3
FY 2012
UPPER
MATH 3820
MATH
7820
0.4
8
21.0
FY 2012
UPPER
MATH 4211
MATH
6211
0.5
14
26.0
FY 2012
UPPER
MATH 4258
MATH
0.7
6258/PHY
S
4510/PHY
S 6510
31
42.0
FY 2012
UPPER
MATH 4265
MATH
0.7
6265/PHY
S 6520
14
21.0
FY 2012
UPPER
MATH 4301
MATH
6301
0.9
12
14.0
FY 2012
UPPER
MATH 4371
MATH
6371
0.5
5
11.0
MATH
7050
MATH
7070
MATH
7090
MATH
7420
FY 2012
UPPER
MATH 4420
MATH
6420
0.6
4
7.0
FY 2012
UPPER
MATH 4435
MATH
6435
1.9
60
31.1
FY 2012
UPPER
MATH 4441
MATH
6441
2.0
39
19.2
FY 2012
UPPER
MATH 4442
MATH
6442
0.9
7
8.0
FY 2012
UPPER
MATH 4450
MATH
6450
0.6
8
13.0
FY 2012
UPPER
MATH 4544
BIOL
0.2
4744/BIOL
6744/MAT
H 6544
8
41.0
FY 2012
UPPER
MATH 4547
MATH
6547
1.8
38
21.4
FY 2012
UPPER
MATH 4548
1.0
12
12.0
FY 2012
UPPER
MATH 4548
MATH
6548
0.9
10
11.0
FY 2012
UPPER
MATH 4610
CSC
0.3
4610/CSC
6610/MAT
H 6610
10
30.0
FY 2012
UPPER
MATH 4620
MATH
6620
0.3
5
17.0
FY 2012
UPPER
MATH 4661
MATH
6661
1.7
46
26.5
FY 2012
UPPER
MATH 4662
MATH
6662
1.6
28
17.1
FY 2012
UPPER
MATH 4751
MATH
6751
2.3
97
42.5
FY 2012
UPPER
MATH 4752
MATH
6752
2.2
59
27.0
FY 2012
UPPER
MATH 4982
1.0
1
1.0
FY 2012
UPPER
MATH 4991
2.0
23
11.5
FY 2012
UPPER
MATH 4999
2.0
2
1.0
FY 2012
UPPER
PHYS 4510
MATH
0.1
4258/MAT
H
6258/PHY
S 6510
4
42.0
FY 2012
GRADUATE BIOL 6744
BIOL
0.3
4744/MAT
H
4544/MAT
H 6544
11
41.0
FY 2012
GRADUATE BIOL 6744
BIOL
0.4
4744/MAT
H 6544
18
41.0
FY 2012
GRADUATE BIOL 6930
MATH
0.1
8530/NEU
R 8790
1
11.0
FY 2012
GRADUATE BIOL 8540
STAT 8540 0.0
1
26.0
FY 2012
GRADUATE CSC 6610
CSC
0.2
4610/MAT
H
4610/MAT
H 6610
6
30.0
FY 2012
GRADUATE MATH 6211
MATH
4211
12
26.0
FY 2012
GRADUATE MATH 6258
MATH
0.1
4258/PHY
S
4510/PHY
S 6510
6
42.0
FY 2012
GRADUATE MATH 6265
MATH
0.2
4265/PHY
S 6520
5
21.0
FY 2012
GRADUATE MATH 6301
1.0
11
11.0
FY 2012
GRADUATE MATH 6301
1.1
16
14.0
MATH
0.5
4301
FY 2012
GRADUATE MATH 6371
MATH
4371
0.5
6
11.0
FY 2012
GRADUATE MATH 6420
MATH
4420
0.4
3
7.0
FY 2012
GRADUATE MATH 6435
1.0
18
18.0
FY 2012
GRADUATE MATH 6435
MATH
4435
1.1
31
29.0
FY 2012
GRADUATE MATH 6441
MATH
4441
1.0
22
22.7
FY 2012
GRADUATE MATH 6442
MATH
4442
0.1
1
8.0
FY 2012
GRADUATE MATH 6450
MATH
4450
0.4
5
13.0
FY 2012
GRADUATE MATH 6544
BIOL
0.2
4744/BIOL
6744
10
41.0
FY 2012
GRADUATE MATH 6544
BIOL
0.1
4744/BIOL
6744/MAT
H 4544
5
41.0
FY 2012
GRADUATE MATH 6547
1.0
11
11.0
FY 2012
GRADUATE MATH 6547
MATH
4547
1.2
28
22.8
FY 2012
GRADUATE MATH 6548
MATH
4548
0.1
1
11.0
FY 2012
GRADUATE MATH 6610
CSC
0.3
4610/CSC
6610/MAT
H 4610
9
30.0
FY 2012
GRADUATE MATH 6620
MATH
4620
0.7
12
17.0
FY 2012
GRADUATE MATH 6661
MATH
4661
0.3
7
26.4
FY 2012
GRADUATE MATH 6662
MATH
4662
0.4
6
16.4
FY 2012
GRADUATE MATH 6751
MATH
4751
0.7
31
43.1
FY 2012
GRADUATE MATH 6752
MATH
4752
0.8
17
20.9
FY 2012
GRADUATE MATH 6999
5.0
5
1.0
FY 2012
GRADUATE MATH 7000
1.0
14
14.0
FY 2012
GRADUATE MATH 7008
1.0
17
17.0
FY 2012
GRADUATE MATH 7050
MATH
3050
0.2
5
30.4
FY 2012
GRADUATE MATH 7070
MATH
3070
0.4
18
43.9
FY 2012
GRADUATE MATH 7090
MATH
3090
0.2
7
37.3
FY 2012
GRADUATE MATH 7300
1.0
13
13.0
FY 2012
GRADUATE MATH 7420
0.1
1
18.0
FY 2012
GRADUATE MATH 7820
1.0
19
19.0
FY 2012
GRADUATE MATH 7820
0.6
13
21.0
FY 2012
GRADUATE MATH 7821
1.0
12
12.0
FY 2012
GRADUATE MATH 8110
1.0
19
19.0
FY 2012
GRADUATE MATH 8120
1.0
7
7.0
FY 2012
GRADUATE MATH 8200
1.0
21
21.0
FY 2012
GRADUATE MATH 8210
1.0
7
7.0
FY 2012
GRADUATE MATH 8220
1.0
7
7.0
FY 2012
GRADUATE MATH 8221
1.0
8
8.0
FY 2012
GRADUATE MATH 8240
1.0
3
3.0
FY 2012
GRADUATE MATH 8440
1.0
9
9.0
MATH
3420
MATH
3820
FY 2012
GRADUATE MATH 8510
1.0
9
9.0
FY 2012
GRADUATE MATH 8520
1.0
10
10.0
FY 2012
GRADUATE MATH 8530
BIOL
0.2
6930/NEU
R 8790
2
11.0
FY 2012
GRADUATE MATH 8540
1.0
4
4.0
FY 2012
GRADUATE MATH 8610
1.0
7
7.0
FY 2012
GRADUATE MATH 8800
2.0
7
3.5
FY 2012
GRADUATE MATH 8801
3.0
19
6.3
FY 2012
GRADUATE MATH 8802
9.0
46
5.1
FY 2012
GRADUATE MATH 8999
11.0
11
1.0
FY 2012
GRADUATE MATH 9116
1.0
24
24.0
FY 2012
GRADUATE MATH 9999
15.0
20
1.3
FY 2012
GRADUATE NEUR 8790
BIOL
0.7
6930/MAT
H 8530
8
11.0
FY 2012
GRADUATE PHYS 6510
MATH
0.0
4258/MAT
H
6258/PHY
S 4510
1
42.0
FY 2012
GRADUATE PHYS 6520
MATH
0.1
4265/MAT
H 6265
2
21.0
FY 2012
GRADUATE STAT 8090
1.0
30
30.0
FY 2012
GRADUATE STAT 8440
1.0
28
28.0
FY 2012
GRADUATE STAT 8540
BIOL 8540 1.0
25
26.0
FY 2012
GRADUATE STAT 8561
1.0
30
30.0
FY 2012
GRADUATE STAT 8581
1.0
9
9.0
FY 2012
GRADUATE STAT 8582
1.0
4
4.0
FY 2012
GRADUATE STAT 8630
1.0
27
27.0
FY 2012
GRADUATE STAT 8670
1.0
23
23.0
FY 2012
GRADUATE STAT 8674
1.0
17
17.0
FY 2012
GRADUATE STAT 8678
1.0
21
21.0
FY 2012
GRADUATE STAT 8691
5.0
19
3.8
FY 2012
GRADUATE STAT 8692
8.0
52
6.5
FY 2012
GRADUATE STAT 8693
2.0
6
3.0
FY 2012
GRADUATE STAT 8694
1.0
11
11.0
FY 2012
GRADUATE STAT 8700
1.0
20
20.0
FY 2012
GRADUATE STAT 8820
1.0
1
1.0
FY 2012
GRADUATE STAT 8999
16.0
38
2.4
FY 2012
GRADUATE STAT 9999
14.0
35
2.5
FY 2013
CORE
MATH 1070
74.0
3136
42.4
FY 2013
CORE
MATH 1101
53.0
2200
41.5
FY 2013
CORE
MATH 1111
37.0
1541
41.6
FY 2013
CORE
MATH 1113
32.0
1286
40.2
FY 2013
CORE
MATH 1220
6.0
125
20.8
FY 2013
CORE
MATH 2201
4.0
122
30.5
FY 2013
CORE
MATH 2202
1.0
12
12.0
FY 2013
CORE
MATH 2211
20.0
845
42.3
FY 2013
CORE
MATH 2212
16.0
668
41.8
FY 2013
CORE
MATH 2215
7.0
289
41.3
FY 2013
CORE
MATH 2420
5.0
181
36.2
FY 2013
LOWER
MATH 0098
1.0
14
14.0
FY 2013
LOWER
MATH 0099
12.0
496
41.3
FY 2013
LOWER
MATH 2008
5.0
156
31.2
FY 2013
UPPER
BIOL 4010
BIOL
0.3
6010/MAT
H
4010/MAT
H 6010
9
27.0
FY 2013
UPPER
BIOL 4744
BIOL 6744 1.0
33
31.5
FY 2013
UPPER
CSC 4610
CSC
0.2
6610/MAT
H
4610/MAT
H 6610
4
22.0
FY 2013
UPPER
EDMT 3420
MATH
0.1
3821/MAT
H 7821
2
19.0
FY 2013
UPPER
MATH 3000
5.0
82
16.4
FY 2013
UPPER
MATH 3030
3.0
137
45.7
FY 2013
UPPER
MATH 3050
6.0
151
25.2
FY 2013
UPPER
MATH 3050
1.0
38
39.0
FY 2013
UPPER
MATH 3070
3.0
78
26.0
FY 2013
UPPER
MATH 3070
2.7
114
42.3
FY 2013
UPPER
MATH 3090
3.0
96
32.0
FY 2013
UPPER
MATH 3090
3.7
80
21.6
FY 2013
UPPER
MATH 3260
3.0
94
31.3
FY 2013
UPPER
MATH 3420
0.8
9
12.0
FY 2013
UPPER
MATH 3435
3.0
86
28.7
MATH
7050
MATH
7070
MATH
7090
MATH
7420
FY 2013
UPPER
MATH 3690
1.0
1
1.0
FY 2013
UPPER
MATH 3820
MATH
7820
0.2
3
18.0
FY 2013
UPPER
MATH 3821
EDMT
0.1
3420/MAT
H 7821
1
19.0
FY 2013
UPPER
MATH 4010
BIOL
0.4
4010/BIOL
6010/MAT
H 6010
11
27.0
FY 2013
UPPER
MATH 4211
MATH
6211
0.7
11
15.0
FY 2013
UPPER
MATH 4250
MATH
6250
0.6
10
17.0
FY 2013
UPPER
MATH 4258
MATH
0.9
6258/PHY
S 4510
25
29.0
FY 2013
UPPER
MATH 4265
MATH
0.6
6265/PHY
S
4520/PHY
S 6520
24
37.0
FY 2013
UPPER
MATH 4275
MATH
6275
0.2
3
14.0
FY 2013
UPPER
MATH 4301
1.0
1
1.0
FY 2013
UPPER
MATH 4301
MATH
6301
0.9
11
12.6
FY 2013
UPPER
MATH 4371
MATH
6371
0.2
3
16.0
FY 2013
UPPER
MATH 4435
MATH
6435
2.2
62
28.8
FY 2013
UPPER
MATH 4441
MATH
6441
2.3
36
15.3
FY 2013
UPPER
MATH 4442
MATH
6442
1.4
11
8.0
FY 2013
UPPER
MATH 4450
1.0
1
1.0
FY 2013
UPPER
MATH 4544
MATH
6544
0.2
5
31.1
FY 2013
UPPER
MATH 4547
MATH
6547
1.9
53
28.6
FY 2013
UPPER
MATH 4548
MATH
6548
0.9
28
30.0
FY 2013
UPPER
MATH 4610
CSC
0.4
4610/CSC
6610/MAT
H 6610
9
22.0
FY 2013
UPPER
MATH 4620
MATH
6620
0.3
3
10.0
FY 2013
UPPER
MATH 4661
MATH
6661
1.6
52
32.2
FY 2013
UPPER
MATH 4662
MATH
6662
1.7
34
19.6
FY 2013
UPPER
MATH 4751
MATH
6751
2.7
108
40.7
FY 2013
UPPER
MATH 4752
MATH
6752
2.1
42
19.7
FY 2013
UPPER
MATH 4982
9.0
12
1.3
FY 2013
UPPER
MATH 4991
2.0
37
18.5
FY 2013
UPPER
MATH 4999
2.0
2
1.0
FY 2013
UPPER
PHYS 4510
MATH
0.1
4258/MAT
H 6258
2
29.0
FY 2013
UPPER
PHYS 4520
MATH
0.1
4265/MAT
H
6265/PHY
S 6520
4
37.0
FY 2013
GRADUATE BIOL 6010
BIOL
0.1
4010/MAT
2
27.0
H
4010/MAT
H 6010
FY 2013
GRADUATE BIOL 6744
BIOL 4744 0.6
19
32.9
FY 2013
GRADUATE BIOL 8540
STAT 8540 0.1
2
22.0
FY 2013
GRADUATE CSC 6610
CSC
0.1
4610/MAT
H
4610/MAT
H 6610
3
22.0
FY 2013
GRADUATE MATH 6010
BIOL
0.2
4010/BIOL
6010/MAT
H 4010
5
27.0
FY 2013
GRADUATE MATH 6211
MATH
4211
0.3
4
15.0
FY 2013
GRADUATE MATH 6250
MATH
4250
0.4
7
17.0
FY 2013
GRADUATE MATH 6258
MATH
0.1
4258/PHY
S 4510
2
29.0
FY 2013
GRADUATE MATH 6265
MATH
0.2
4265/PHY
S
4520/PHY
S 6520
8
37.0
FY 2013
GRADUATE MATH 6275
MATH
4275
0.8
11
14.0
FY 2013
GRADUATE MATH 6301
1.0
15
15.0
FY 2013
GRADUATE MATH 6301
MATH
4301
1.1
17
15.1
FY 2013
GRADUATE MATH 6371
MATH
4371
0.8
13
16.0
FY 2013
GRADUATE MATH 6435
MATH
4435
0.8
24
28.4
FY 2013
GRADUATE MATH 6441
MATH
0.7
13
19.9
4441
FY 2013
GRADUATE MATH 6442
FY 2013
GRADUATE MATH 6450
FY 2013
GRADUATE MATH 6544
FY 2013
GRADUATE MATH 6547
FY 2013
GRADUATE MATH 6547
FY 2013
MATH
4442
0.6
5
8.0
1.0
2
2.0
0.2
7
33.0
1.0
16
16.0
MATH
4547
1.1
29
25.3
GRADUATE MATH 6548
MATH
4548
0.1
2
30.0
FY 2013
GRADUATE MATH 6610
CSC
0.3
4610/CSC
6610/MAT
H 4610
6
22.0
FY 2013
GRADUATE MATH 6620
MATH
4620
0.7
7
10.0
FY 2013
GRADUATE MATH 6661
MATH
4661
0.4
12
31.3
FY 2013
GRADUATE MATH 6662
MATH
4662
0.3
5
18.7
FY 2013
GRADUATE MATH 6751
MATH
4751
0.3
14
40.5
FY 2013
GRADUATE MATH 6752
MATH
4752
0.9
18
20.8
FY 2013
GRADUATE MATH 7000
1.0
18
18.0
FY 2013
GRADUATE MATH 7008
1.0
14
14.0
FY 2013
GRADUATE MATH 7050
MATH
3050
0.0
1
39.0
FY 2013
GRADUATE MATH 7070
MATH
3070
0.3
13
42.6
FY 2013
GRADUATE MATH 7090
MATH
3090
0.3
5
16.6
MATH
4544
FY 2013
GRADUATE MATH 7420
1.0
11
11.0
FY 2013
GRADUATE MATH 7420
0.3
3
12.0
FY 2013
GRADUATE MATH 7610
1.0
13
13.0
FY 2013
GRADUATE MATH 7820
1.0
16
16.0
FY 2013
GRADUATE MATH 7820
0.8
15
18.0
FY 2013
GRADUATE MATH 7821
1.0
13
13.0
FY 2013
GRADUATE MATH 7821
EDMT
0.8
3420/MAT
H 3821
16
19.0
FY 2013
GRADUATE MATH 8110
1.0
11
11.0
FY 2013
GRADUATE MATH 8120
1.0
5
5.0
FY 2013
GRADUATE MATH 8200
1.0
26
26.0
FY 2013
GRADUATE MATH 8201
1.0
7
7.0
FY 2013
GRADUATE MATH 8220
1.0
4
4.0
FY 2013
GRADUATE MATH 8221
1.0
3
3.0
FY 2013
GRADUATE MATH 8420
1.0
12
12.0
FY 2013
GRADUATE MATH 8450
1.0
6
6.0
FY 2013
GRADUATE MATH 8510
1.0
1
1.0
FY 2013
GRADUATE MATH 8515
1.0
10
10.0
FY 2013
GRADUATE MATH 8530
1.0
8
8.0
FY 2013
GRADUATE MATH 8560
1.0
13
13.0
FY 2013
GRADUATE MATH 8620
1.0
7
7.0
FY 2013
GRADUATE MATH 8800
2.0
14
7.0
FY 2013
GRADUATE MATH 8801
3.0
21
7.0
FY 2013
GRADUATE MATH 8802
9.0
64
7.1
FY 2013
GRADUATE MATH 8820
1.0
1
1.0
MATH
3420
MATH
3820
FY 2013
GRADUATE MATH 8950
1.0
31
31.0
FY 2013
GRADUATE MATH 8999
8.0
10
1.3
FY 2013
GRADUATE MATH 9116
1.0
13
13.0
FY 2013
GRADUATE MATH 9126
1.0
5
5.0
FY 2013
GRADUATE MATH 9136
1.0
5
5.0
FY 2013
GRADUATE MATH 9999
22.0
33
1.5
FY 2013
GRADUATE PHYS 6520
MATH
0.0
4265/MAT
H
6265/PHY
S 4520
1
37.0
FY 2013
GRADUATE STAT 8310
1.0
3
3.0
FY 2013
GRADUATE STAT 8320
1.0
11
11.0
FY 2013
GRADUATE STAT 8440
1.0
18
18.0
FY 2013
GRADUATE STAT 8540
BIOL 8540 0.9
20
22.0
FY 2013
GRADUATE STAT 8561
1.0
20
20.0
FY 2013
GRADUATE STAT 8581
1.0
13
13.0
FY 2013
GRADUATE STAT 8600
1.0
17
17.0
FY 2013
GRADUATE STAT 8610
1.0
28
28.0
FY 2013
GRADUATE STAT 8670
1.0
27
27.0
FY 2013
GRADUATE STAT 8678
1.0
37
37.0
FY 2013
GRADUATE STAT 8680
1.0
17
17.0
FY 2013
GRADUATE STAT 8691
3.0
14
4.7
FY 2013
GRADUATE STAT 8692
7.0
59
8.4
FY 2013
GRADUATE STAT 8693
3.0
14
4.7
FY 2013
GRADUATE STAT 8694
1.0
6
6.0
FY 2013
GRADUATE STAT 8820
3.0
10
3.3
FY 2013
GRADUATE STAT 8900
1.0
2
2.0
FY 2013
GRADUATE STAT 8999
16.0
28
1.8
FY 2013
GRADUATE STAT 9999
11.0
23
2.1
FALL 2010
FALL 2011
FALL 2012
3 YR. AVG.
.14 Major Counts
PROGRAM
MAJOR CONC.
MAJORS
MAJORS
MAJORS
MAJORS
BS
MATH
194
193
200
195.7
BS
MATH
ACTUARIAL
SCIENCE
11
13
18
14.0
BS
MATH
COMPUTER
INFORMATION
SYSTEMS
2
1
1.0
BS
MATH
COMPUTER SCIENCE 4
4
6
4.7
BS
MATH
MANAGERIAL
SCIENCES
1
1
1
1.0
BS
MATH
STATISTICS
9
9
12
10.0
BS
MATH
TEACHER
EDUCATION
6
BS
2.0
225
222
238
228.3
14
12
1
9.0
1
0.7
MS
MATH
MS
MATH
BIOINFORMATICS
1
MS
MATH
BIOSTATISTICS
25
18
19
20.7
MS
MATH
DISCRETE
MATHEMATICS
10
8
6
8.0
MS
MATH
SCIENTIFIC
COMPUTING
2
5
7
4.7
MS
MATH
STATISTICS
18
28
29
25.0
MS
MATH
STATISTICS WITH
ALLIED FIELD
6
4
5
5.0
MS
76
75
68
73.0
PHD
MATH
& STAT
4
3
2
3.0
PHD
MATH BIOINFORMATICS
& STAT
6
5
5
5.3
PHD
MATH BIOSTATISTICS
& STAT
21
22
20
21.0
PHD
MATH MATH
& STAT
9
15
20
14.7
40
45
47
44.0
PHD
.15 Degrees Conferred
FY 2011
FY 2012
FY 2013
3 YR. AVG.
DEGREES
DEGREES
DEGREES
DEGREES
PROGRAM MAJOR CONCENTRATION CONFERRED CONFERRED
CONFERRED
CONFERRED
BS
MATH
BS
MATH
BS
20
16
19
55.0
ACTUARIAL
SCIENCE
1
5
5
11.0
MATH
COMPUTER
SCIENCE
1
1
2.0
BS
MATH
MANAGERIAL
SCIENCES
1
BS
MATH
STATISTICS
BS
MS
MATH
MS
MATH
BIOSTATISTICS
1.0
3
3.0
23
21
28
72.0
4
5
1
10.0
10
10
8
28.0
MS
MATH
DISCRETE
MATHEMATICS
1
1.0
MS
MATH
SCIENTIFIC
COMPUTING
1
1.0
MS
MATH
STATISTICS
8
14.0
MS
MATH
STATISTICS WITH 1
ALLIED FIELD
MS
2
17
PHD
MATH
& STAT
PHD
MATH BIOINFORMATICS
& STAT
PHD
MATH BIOSTATISTICS
& STAT
PHD
MATH MATHEMATICAL
& STAT RISK
MANAGEMENT
PHD
MATH MATHEMATICS
& STAT
2
4
1.0
19
19
55.0
1
1
2.0
2
2.0
3
6.0
1
1.0
1
1
3
1.0
2
7
.16 Math Majors Enrolled in RIMMES
Academic Year
2009-2010
2010-2011
2011-2012
2012-2013
2013-2014
Number of RIMMES Students
10
10
8
12
11
.17 Math Majors Enrolled in Honors
Semester
Fall 2009
Number of majors enrolled
in Honors
10
12.0
Spring 2010
Fall 2010
Spring 2011
Fall 2011
Spring 2012
Fall 2012
Spring 2013
Fall 2013
Spring 2014
11
13
16
22
20
27
25
26
26
.18 Graduate Student Publications and Presentations
Student publications:
H. Yang, C. Yau and Y. Zhao. Smooth empirical likelihood inference for the difference
of two quantiles with right censoring, Journal of Statistical Planning and Inference, 146, 95101, 2014.
M. Bouadoumou, Y. Zhao and Y. Lu. Jackknife empirical likelihood for the accelerated failure
time model with censored data, Communications in Statistics - Simulation and Computation, 2013,
to appear.
Y. Zhao and D. Nguyen. Tests for comparison of competing risks under the additive risk model,
Journal of Statistical Planning and Inference, 143, 842-851, 2013.
H. Yang and Y. Zhao. Jackknife empirical likelihood confidence intervals for the difference of two
ROC curves, Journal of Multivariate Analysis, 115, 270–284, 2013.
Z. Chen, Y. Zhao, Y. Cui and J. Kowalski, Methodology and application of adaptive and
sequential approaches in contemporary clinical trials, Journal of Probability and Statistics, 2012,
Article ID 527351, 20 pages.
An, Y., Smoothed empirical likelihood inference for ROC curves with missing data, Open Journal
of Statistics. Vol. 2, 21-27, 2012.
X. Liu and Y. Zhao. Semi-empirical likelihood confidence intervals for ROC curves with missing
data, Journal of Statistical Planning and Inference, 142, 3123-3133, 2012.
H. Yang and Y. Zhao. Smoothed empirical likelihood method for ROC curves with censored data,
Journal of Multivariate Analysis, 109, 254-263, 2012.
H. Yang and Y. Zhao. New empirical likelihood inference for transformation models, Journal of
Statistical Planning and Inference, 142, 1659-1668, 2012.
Y. Zhao and A. Jinnah. Inference for Cox’s regression model via adjusted empirical likelihood,
Computational Statistics, 27, 1-12, 2012.
Y. Zhao and M. Zhao. Empirical likelihood for the contrast of two hazard functions with right
censoring data, Statistics and Probability Letters, 81, 392-401, 2011.
Y. Zhao and G. Wang. Additive risk analysis of microarray gene expression data via correlation
principal component regression, The Journal of Bioinformatics and Computational Biology, 8,
646--659, 2010.
A.B. Smirnova, A.B. Bakushinsky, L. DeCamp, On Application of Abstract Discrepancy Principle
to the Analysis of Epidemiology Models, Applicable Analysis, under review [with a graduate
student].
A.B. Bakushinsky, A.B. Smirnova, H. Liu, A Nonstandard Approximation of Pseudoinverse and a
New Stopping Criterion for Iterative Regularization, Journal of Inverse and Ill-Posed Problems,
under review [with a graduate student].
O.I. Sarajlic, A.B. Smirnova, Numerical Representation of Weirs Using the Concept of Inverse
Problems, International Journal of Hydraulic Engineering, 2, N3, 53-58 (2013) [with a former
undergraduate student; paper resulted from an undergraduate research project].
M. Arav, F. J. Hall, Z. Li, A. Merid, and Y. Gao. Sign patterns that require almost unique rank, Lin.
Alg. and Appl., 430(2009), 7-16.
M. Arav, F. J. Hall, K. Kaphle, Z. Li, and N. Manzagol. Spectrally arbitrary tree sign patterns of
order 4, Electronic Journal of Linear Algebra, 20(2010), 180-197 (rigorously refereed)
M. Fiedler, F. J. Hall, and R. Marsli. Gersgorin discs revisited, Lin. Alg. and Appl., 438(2013),
598-603.
R. Marsli and F. J. Hall. Geometric multiplicities and Gersgorin discs, The American Mathematical
Monthly, 120 (2013), 452-455.
R. Marsli and F. J. Hall. Further results on Gersgorin discs, Lin. Alg. and Appl., 439(2013), 189195.
R. Marsli F. J. Hall. Some refinements of Gersgorin discs, International Journal of Algebra,
7(2013), 573-580.
M. Fiedler, F. J. Hall, and M. Stroev. Dense alternating sign matrices and extensions, Lin. Alg. and
Appl., 444(2014), 219-226,
R. Marsli and F. J. Hall. Some new inequalities on geometric multiplicities and Gersgorin discs, to
appear in the International Journal of Algebra.
M. Fiedler, F. J. Hall, and M. Stroev, Permanents, determinants, and generalized complementary
basic matrices, to appear in Operators and Matrices.
Rozier K., Bondarenko V. E. Some remarkable properties of a Hopfield neural network with time
delay. Int. J. Comput. Math. Sci., 2012, v. 6, p. 20-25.
Mullins P. D., Bondarenko V. E. A mathematical model of the mouse ventricular myocyte
contraction. PLoS ONE, 2013, v. 8, n. 5, e63141.
Qin, G.S. and Hotilovac, L. (2008). Comparison of non-parametric confidence intervals for the
area under the ROC curve of a continuous-scale diagnostic test. Statistical Methods in Medical
Research, 17, 207-221.
Kim, J. and Qin, G.S. (2008). Logit-transformation based confidence intervals for the sensitivity
of a continuous-scale diagnostic test. International Conference on BioMedical Engineering and
Informatics, 2008. Volume 2, 768 – 772.
Qin, G.S., Davis, A.E., and Jing, B.-Y. (2011). Empirical likelihood-based confidence intervals for
the sensitivity of a continuous-scale diagnostic test at a fixed level of specificity. Statistical
Methods in Medical Research, 20, 217- 231. Published online: August 4, 2009
Qin, G.S., Jin, X. P. and Zhou, X. H. (2011). Nonparametric interval estimation for the partial area
under the ROC curve. The Canadian Journal of Statistics, 39, 17-33.
Huang, X., Qin, G.S. and Fang, Y. (2011). Optimal combinations of diagnostic tests based on
AUC. Biometrics, 67, 568–576. Article first published online: Jun 16, 2010
Yang, B.Y., Qin, G.S. and Qin, J. (2011). Empirical likelihood-based inferences for a low income
proportion. The Canadian Journal of Statistics, 39, 1-16.
Yang, B.Y., Qin, G.S., and Belinga-Hall, N. E. (2012). Non-parametric inferences for the
generalized Lorenz curve (in Chinese), SCIENTIA SINICA Mathematica (Science in China Series
A), 42, 235-250.
Yang, B.Y., Qin, G.S. (2012). Empirical Likelihood-based Inferences for the Area under the ROC
Curve with Covariates. SCIENCE CHINA Mathematics (Science in China Series A), 55, 1553–
1564.
Huang, X., Qin, G.S., Yuan, Y. and Zhou, X.H. (2012). Confidence intervals for the difference
between two partial AUCs. Australian and New Zealand Journal of Statistics, 54, 63–79.
Yuan, A., He, W.Q., Wang, B.H., and Qin, G.S. (2012). U-Statistic with side information. J Multi
Anal, 111, 20-38.
Wang, B.H and Qin, G.S. (2012). Empirical likelihood-based confidence intervals for the
sensitivity of a continuous-scale diagnostic test with missing data. Communications in Statistics –
Theory and Methods (Accepted).
Wang, B.H and Qin, G.S. (2012). Imputation-based empirical likelihood inference for the
area under the ROC curve with missing data. Statistics and Its Interface 5, 319–329.
Zhou, H.C. and Qin, G.S. (2012). New nonparametric confidence intervals for the Youden index.
Journal of Biopharmaceutical Statistics 22, 1244-1257.
Zhou, H.C. and Qin, G.S. (2013). Confidence intervals for the difference in paired Youden indices.
Pharmaceutical Statistics 12, 17-27.
Qin, G.S., Yang, B.Y., and Belinga-Hall, N. E. (2013). Empirical likelihood-based inferences for
the Lorenz curve. Ann Inst Stat Math 65, 1–21.
Wang, B. H., Qin, G. S. (2013). Empirical likelihood confidence regions for the evaluation of
continuous-scale diagnostic test in the presence of verification bias. The Canadian Journal of
Statistics, 41, 398–420
Wang, B. H., Qin, G. S. (2014). Jackknife Empirical Likelihood Confidence Regions for the
Evaluation of Continuous- Scale Diagnostic Tests with Verification Bias. To appear in Statistical
Methods in Medical Research (in press)
Song, R. G., Qin, G. S., Harrison, K. M., Zhang, X. J., Hall, H. I. (2014). Modeling survival after
diagnosis of a disease based surveillance data. To appear in International Journal of Statistics in
Medical Research.(in press)
Yanhong Wang, Yixin Fang, Junhui Wang, “Sparse Optimal Discriminant Clustering”. (Submitted
to Journal of Computational and Graphical Statistics).
Y. H. Wang, H. X. Liu, B. F. Xie, and X. M. Zhang, Study and Realization of Automatic Test
simulation System of Computer Interlocking Software, China Railway Science vol.25 No.2 (2004)
16 - 19.
Sara Malec (2013). On the intersection algebra of principal ideals. Communications in Algebra
(Accepted).
Florian Enescu and Sara Malec (2014). Intersection algebras for principal monomial ideals in
polynomial rings (submitted to Journal of Algebra and Its Applications).
Jie Han and Yi Zhao (2013). On multipartite Hajnal-Szemeredi theorems, Discrete Mathematics,
313, 1119–1129.
Jie Han and Yi Zhao (2013). Minimum vertex degree threshold for $C_4^3$-tiling (submitted)
Jie Han and Yi Zhao (2013). Minimum degree thresholds for loose Hamilton cycles in 3-graphs
(submitted).
Xing T, Wojcik J, Zaks M and Shilnikov AL. Multifractal Kaos. Special volume honouring the
memory of John S. Nicolis. World Sci. 2014
Xing T, Wojcik J, Barrio R and Shilnikov A. Symbolic toolkit for chaos exploration, in "Theory
and Applications in Nonlinear Dynamics," Springer, 2014
R. Barrio, F. Blesa, S. Serrano, T. Xing and A. Shilnikov, Homoclinic spirals: theory and
numerics. “Progress and Challenges in Dynamical Systems,” Springer Proceedings in Mathematics
& Statistics, v. 54, 2013
Guantao Chen, Manzhan Gu, and Nana Li*, Maximum Cuts for Connected Digraphs, J. Graph
Theory, online, 26 JUL 2013 DOI: 10.1002/jgt.21746
Guantao Chen and Songling Shan*, Homeomorphically Irreducible Spanning Trees, J. Combin.
Theory B, (online version)
Guoyu Tao, Kun Zhan*, Thomas Gift, Fasheng Qiu, and Guantao Chen, Using a Resource
Allocation Model to Better Guide Local Sexually Transmitted Diseases Control and Prevention
Programs}, Operations Research for Health Care, Vol 1 (2012), Issues 2-3, 23-29
Yingshu Li, Chinh Vu, Guantao Chen, and Yi Zhao, Transforming Complete Coverage Algorithms
to Partial Coverage Algorithms for Wireless Sensor Networks, IEEE Transactions on Distributed
Systems, 22(2011) no. 4, 695-703
Kun Zhao*, Guantao Chen, Thomas Gift, Guoyu Tao, Optimization Model and Algorithm Help to
Screen and Treat Sexually Transmitted Diseases Internat. J. Computational Models and
Algorithms in Medicine, 1(2010), no. 4 1-18
Chinh Vu*, Guantao Chen, Yi Zhao, and Yingshu Li, A Universal Framework for Partial
Coverage in Wireless Sense or Networks, (IPCCC) 2009 IEEE 28th International, 1097-2641, 1-8
(appeared in 2010)
Xue Wang, Fasheng Qiu, S.K. Prasad, Guantao Chen, Efficient Parallel Algorithms for MaximumDensity Segment Problem, Parallel & Distributed Processing -- the 24th IEEE International
Symposium, 1530-2075, 1-9
Michael Kirberger, Xue Wang, Kun Zhao, Shen Tang, Guantao Chen, and Jenny J. Yang,
Integration of diverse research methods to analyze and engineer C_a^{2+}-binding proteins: From
prediction to production, Curent Bioinformatics, 5, no. 1 (2010) 68-80
Xue Wang, Kun Zhao, Michael Kirberger, Hing Wong, Guantao Chen and Jenny J. Yang, Analysis
and prediction of calcium binding pockets from apo-protein structures exhibiting calcium-induced
localized conformational changes, Protein Science, 19, no. 6, (2010) 1180-1190
Y Cui; Z Chen, T Owonikoko, Z Wang, Z Li, R Luo, M Kutner, F Khuri, J Kowalski, “Escalation
with Overdose Control using All Toxicities and Time to Event Toxicity Data in Cancer Phase I
Clinical Trials”, Contemporary Clinical Trials, (on revision) 2014.
Z. Li, Y. Gao, M. Arav, F. Gong, W. Gao, F.J. Hall and H. van der Holst, Sign patterns with
minimum rank 2 and upper bounds on minimum ranks, Linear and Multilinear Algebra 61 (2013),
895-908.
W. Zhou, M. Arav, F.J. Hall, Z. Li, H. van der Holst, L. Zhang, The minimum rank of a sign
pattern matrix with a 1-separation, Linear Algebra and Its Applications, 2014.
S. Jalil, I. Belykh, and A. Shilnikov, Fast reciprocal inhibition can synchronize bursting neurons",
Physical Review E, V.81, 045201, 2010 .
I. Belykh, S. Jalil, and A. Shilnikov, Burst-duration mechanism of in-phase bursting in inhibitory
networks, Regular & Chaotic Dynamics, Vol. 15, no. 2-3, 148-160, 2010.
I. Belykh, V. Belykh, R. Jeter, and M. Hasler, Multistable randomly switching oscillators: the odds
of meeting a ghost, European Physical Journal Special Topics, V. 222, 2497-2507, 2013.
S. Jalil, I. Belykh, and A. Shilnikov, Spikes matter for phase-locked bursting in inhibitory neurons,
Physical Review E, Vol. 85, 036214, 2012.
Jalil S, Allen D, Yourker J and Shilnikov A. Toward robust phase-locking in Melibe swim central
pattern generator model. J. Chaos, 23(4), focus issue "Rhythms and Dynamic Transitions in
Neurological Disease," 2013.
Student presentations:
1. Stalvey, H. E., Vidakovic, D. & Montiel, M. (2014). Developing the Notion of Function
between Sets of Equivalence Classes from the APOS Perspective. Presented at the MAA
Session on Research on the Teaching and Learning of Undergraduate Mathematics, AMS/MAA
Joint Mathematics Meetings, Baltimore, MD, January 15 – 18.
2. Stroev, M., Permanents, determinants, and generalized complementary basic matrices, invited
talk, International Linear Algebra Conference.
3. Stroev, M., Dense alternating sign matrices and extensions, invited talk, 8th Atlanta Lecture
Series on Combinatorics and Graph Theory, Atlanta, Feb. 2013.
4. Stroev, M., Dense alternating sign matrice and extensions, AMS Regional Meeting, Knoxville,
TN, March 2014. (Supported by AMS award).
5. Marsli, R., Geometric Multiplicities and Gershgorin Discs, AMS Regional Meeting, Knoxville
TN, Marh 2014.
6. Rozier K. On the impact of connection type and asymmetry in a Hopfield neural network. The
Ninth Annual Harriett J. Walton Symposium on Undergraduate Mathematics Research, 9 April
2011, Morehouse College, Atlanta, GA.
7. Rozier K. Role of asymmetry and connection type in a Hopfield neural network model. RIMMES,
Undergraduate Research Conference Day, 22 April 2011, Georgia State University, Atlanta, GA.
8. Rozier K. The effects of uniform noise on a Hopfield neural network under excitation. Kennesaw
Mountain Undergraduate Mathematics Conference, 11-12 November 2011, Kennesaw State
University, Kennesaw, GA.
9. Wooldridge B., Bondarenko V. E. A model of action potential regulation by the β1-adrenergic
signaling system in mouse ventricular myocytes. Fifth Georgia Scientific Computing
Symposium, Atlanta, GA, February 23, 2013.
10. Kapustin K. G., Bondarenko V. E. Modeling gating properties of the human cardiac sodium
channel. Fifth Georgia Scientific Computing Symposium, Atlanta, GA, February 23, 2013.
11. Mullins P. D., Bondarenko V. E. Modeling contractions of mouse ventricular myocyte. Fifth
Georgia Scientific Computing Symposium, Atlanta, GA, February 23, 2013.
12. Rozier K., Bondarenko V. E. Dynamics of a Hopfield neural network with time delay and
symmetric and asymmetric connections. Fifth Georgia Scientific Computing Symposium,
Atlanta, GA, February 23, 2013.
13. Wooldridge B., Bondarenko V. E. Modeling β1-adrenergic signaling system in mouse cardiac
cells. Brain & Behavior Retreat, Georgia State University, Atlanta, GA, April 5, 2013.
14. Kapustin K. G., Bondarenko V. E. Investigation of Markov models for the human cardiac sodium
channel. Brain & Behavior Retreat, Georgia State University, Atlanta, GA, April 5, 2013.
15. Mullins P. D., Bondarenko V. E. A computer model of the mouse ventricular myocyte contraction.
Brain & Behavior Retreat, Georgia State University, Atlanta, GA, April 5, 2013.
16. Rozier K., Bondarenko V. E. Dynamics of a Hopfield neural network with time delay and
symmetric and asymmetric connections. Brain & Behavior Retreat, Georgia State University,
Atlanta, GA, April 5, 2013.
17. Mullins P. D., Bondarenko V. E. A model for mouse ventricular myocyte contraction. The 2 nd
Workshop on Biostatistics and Bioinformatics, Atlanta, GA, May 10-12, 2013.
18. Yang, B.Y. and Qin, G.S. "Empirical Likelihood Based Inferences in ROC analysis with
Covariates". First Joint Biostatistics Symposium, Beijing, China, July 15-18, 2010.
19. Yan, F. X. and Qin, G.S. "Racial Disparities Study in Diabetes-related Complications Using
National Health Survey Data", Statistical Applications Using Massive and Emerging Data in
Public Health Thirteenth Biennial CDC Symposium on Statistical Methods, Decatur, Georgia,
USA, May 24-25, 2011.
20. Zhou, H. C. and Qin, G.S."Nonparametric Covariates Adjustment for Youden Index", JSM
2011, American Statistical Association, Miami Beach, Florida, USA, July 30-August 4, 2011.
21. Wang, B.H and Qin, G.S. “Joint Empirical Likelihood Conference Regions for the Evaluation
of Continuous-Scale Diagnostic Tests in the Presence of Verification Bias” , ENAR,
Washington D.C, USA, April 1-4, 2012.
22. Wang, B.H and Qin, G.S. "Comparative Study of Joint Empirical Likelihood Confidence
Regions for the Evaluation of Continuous-Scale Diagnostic Tests in the Presence of Verification
Bias" (Section On Nonparametric Statistics Student Paper Competition Winners), JSM 2012,
American Statistical Association, San Diego, USA, July 28- August 2, 2012.
23. Wang, B.H and Qin, G.S. “Empirical Likelihood Confidence Regions for the Evaluation of
Continuous-Scale Diagnostic Test in the Presence of Verification Bias”, 2012 Quality and
Productivity Research Conference, Long Beach, CA, June 4-7, 2012.
24. Huang, X. and Qin, G.S. "Confidence Intervals for the Difference Between Two Partial AUCs
", JSM 2012, American Statistical Association, San Diego, USA, July 28- August 2, 2012.
25. Luo, S., Huang, X. and Qin, G.S . "Smoothed Jackknife Empirical Likelihood Inferences for
Lorenz Curves", JSM 2012, American Statistical Association, San Diego, USA, July 28- August
2, 2012.
26. Yanhong Wang, “A dimension reduction tool for cluster analysis using optimal scoring",
SRCOS Summer Research Conference in Burns, Tennessee 2013.
27. Yanhong Wang, “Sparse Factor Analysis by Projection”, 2nd Workshop on Biostatistics and
Bioinformatics, Atlanta, GA, USA (2013).
28. Sara Malec, AMS Southeastern Sectional Meeting, Oxford, MS, March 2013
29. Jie Han. Minimum degree conditions for Hamilton k/2-cycles in k-graphs, Atlanta Lecture
Series in Combinatorics and Graph Theory XI, Atlanta, GA, Jan 2014.
30. Jie Han. Exact minimum d-degree thresholds for Hamilton cycles in k-uniform hypergraphs,
26th Cumberland Conference on Combinatorics, Graph Theory & Computing, May 2013.
31. Jie Han. Minimum Vertex Degree Thresholds for Loose Hamilton Cycles in 3-uniform
hypergraphs, SIAM sectional meeting, Knoxville, TN, March 2013.
32. Jie Han. Absorbing lemma for the multipartite Hajnal-Szemerédi Theorem, AMS sectional
meeting, Tampa, FL, March 2012.
33. Jie Han. Poster presentation: Extremal Combinatorics at Illinois 2, Champaign, IL, March 2013.
34. Xiuxiu He. Poster Presentation, The Seventh q-bio Conference, Aug 2013, Santa Fe, NM
35. Xiuxiu He. Student Short Talk, CCBS-2014 National Short Course in Systems Biology, January
2014, Irvine, CA.
36. Xiuxiu He. Poster Presentation, 2014 4th Annual Southern California Systems Biology
Conference (SoCal SysBio), January 2014, Irvine, CA
37. Shuman Guo. Poster Presentation, Statistics Workshop, May 2012, GSU, Atlanta, GA
38. T. Xing, R. Barrio, and A. Shilnikov, Chaos, stirred not shaken. Dynamics Days US, January 25, 2014. Poster.
39. T. Xing*, R. Barrio, J. Wojcik and A. Shilnikov, Kneading Invariants for the elucidation of
chaos. SIAM Meeting on Applied Dynamical Systems, May 19-23, 2013. Poster.
40. T. Xing*, and A. Shilnikov, Symbolic tools for deterministic chaos. SIAM Meeting on Applied
Dynamical Systems, May 19-23, 2013. Contributed talk.
41. T. Xing*, R. Barrio, J, Wojcik and A. Shilnikov, Chaos stirred not shaken. 2013 B&B Retreat,
GSU, Atlanta, April 5 2013. Poster.
42. T. Xing*, R. Barrio, J, Wojcik and A. Shilnikov, Chaos stirred not shaken. International
Conference on Dynamics of Differential, Gatech, Atlanta, March 16-20, 2013. Poster.
43. T. Xing, J. Wojcik, R. Barrio and A. Shilnikov, Kneading in Shimizu-Morioka Model. Georgia
Scientific Computing Symposium (GSCS), Georgia State University, February 23rd, 2013
44. T. Xing and A. Shilnikov, Symbolic Methods in structural stability scanning of dynamical
systems. Department of Mathematics and Statistics seminar, Georgia State University,
November 10th, 2012.
45. T. Xing*, R. Barrio and A. Shilnikov, Kneadings, symbolic dynamics and painting Lorenz
chaos. International Conference on Theory and Applications in Nonlinear Dynamics. Seattle,
WA, August, 26-30 2012. Poster.
46. T. Xing and A. Shilnikov, Kneadings in Lorenz system and Shimizu-Morioka model.
Conference of the Carolina Dynamics Symposium, Clemson University, April 14th, 2012.
Contributed talk.
47. Songling Shan, Finding spanning generalized Halin subgraphs in graphs
48. with large minimum degree, 11th Altanta Lecture Series on Combinatorics and Graph Theory,
Atlanta, GA, Jan. 2014
49. Songling Shan, Disjoint chorded cycles of the same length, 26th Cumberland Conference on
Combinatorics, Graph Theory and Computing, Murfreesboro, TN, May, 2013
50. Amy Yates, Intersection of longest paths in connected K4-minor-free graphs, 26th Cumberland
Conference on Combinatorics, Graph Theory and Computing, Murfreesboro, TN, May, 2013
51. Songling Shan, Homeomorphically irreducible spanning trees, 8th Altanta Lecture Series on
Combinatorics and Graph Theory, Atlanta, GA, Feb. 2013
52. Nana Li, Union Closed Conjecture, 8th Altanta Lecture Series on Combinatorics and Graph
Theory, Atlanta, GA, Feb. 2013
53. Nana Li, On Union Closed Conjecture, EXCILL2: Extremal Combinatorics at Illinois, UrbanaChampaign, Nov. 2013
54. Nana Li, Read-Frost transition on dense graphs, Paul Erdos Memorial Lecture Series,
Memphis, May, 2012
55. Songling Shan, Homeomorphically irreducible spanning trees, Paul Erdos Memorial Lecture
Series, Memphis, May, 2012
56. Nana Li, Read-Frost transition on dense graphs, 6th Altanta Lecture Series on Combinatorics
and Graph Theory, Atlanta, GA, May 2012
57. Songling Shan, Homeomorphically irreducible spanning trees, 6th Altanta Lecture Series on
Combinatorics and Graph Theory, Atlanta, GA, May 2012
58. S Jalil, D Allen, A Shilnikov. Modeling study of a Central Pattern Generator in the Melibe
seaslug. BMC Neuroscience 13, 1-2 2102.
59. Y Cui, TK Owonikoko, Z Wang, K Sungjin, SM Dong, FR Khuri, J Kowalski, and Z Chen,
“Two-Stage Phase II Design to Improve the Success Rate of Follow-up Phase III Trial”, Poster
Presentation on Workshop on Biostatistics and Bioinformatics, 2012.
60. Y Cui, TK Owonikoko, Z Wang, K Sungjin, SM Dong, FR Khuri, J Kowalski, and Z Chen, “A
Novel Phase II Design to Minimize Trial Duration and Improve the Success Rate of Follow-up
Phase III Trial”, Joint Statistical Meetings, San Diego, CA, 2012.
61. Y Cui, Z Chen, Z Wang, R Luo, and FR Khuri, “Time-to-Event Dose Escalation with Overdose
Control Method using Continuous Toxicity Scores”, Society of Clinical Trials, Boston, MA,
2013.
62. Y Cui, Z Chen, Z Wang, R Luo, and FR Khuri, “Time-to-Event Dose Escalation with Overdose
Control Method using Continuous Toxicity Scores”, Poster Presentation on Workshop on
Biostatistics and Bioinformatics, 2013
63. Yichao Yin, and Ruiyan Luo. Identification of Differential Gene Pathways with Sparse Principal
Component Analysis. Poster Presentation on Workshop on Biostatistics and Bioinformatics,
2013.
64. Yanhong Wang, “Sparse Optimal Scoring Clustering”, 1st Workshop on Biostatistics and
Bioinformatics, Atlanta, GA, USA (2012).
65. Wei Gao, “Sign pattern matrices with minimum rank 3 and point-line configurations”, invited
talk at the Minisymposium on Sign Pattern Matrices in the 2013 International Linear Algebra
Society Conference, Providence, RI, USA, June 2013.
66. R. Jeter and I. Belykh, Network synchronization enhanced by slowly switching on-off
connections. Poster. 2014 Georgia Scientific Computing Symposium, Kennesaw State
University, February 22, 2014.
67. R. Reimbayev, K. Zhao and I. Belykh, Repulsive Inhibition Synchronizes Excitatory Networks:
Help from the Enemy, Poster, Fifth Georgia Scientific Computing Symposium, Atlanta, GA,
February 23, 2013.
68. R. Reimbayev, K. Zhao and I. Belykh, Repulsive Inhibition Synchronizes Excitatory Networks.
Poster. Brain & Behavior Retreat, Georgia State University, Atlanta, GA, April 5, 2013.
69. S. Jalil, I. Belykh, and A. Shilnikov, 2011 SIAM Conference on Applications of Dynamical
Systems, Snowbird, USA, May 22-26, 2011 . Contributed talk: Multiple Phase Locked States
in Half-Center Oscillators.
70. S. Jalil, I. Belykh, and A. Shilnikov, 7th Conference on Frontiers in Applied and Computational
Mathematics, Newark, New Jersey, May 21-23, 2010. Talk: Fast inhibition synchronizes
bursting neurons . Travel award to S. Jalil.
71. K. Zhao and I. Belykh, Mathematical Challenges in Neural Network Dynamics, Mathematical
Biosciences Institute (MBI), Ohio State University, Columbus, October 1-5, 2012. Poster:
Synergetic role of inhibition and excitation in bursting synchronization.
72. K. Zhao and I. Belykh, 2011 SIAM Conference on Applications of Dynamical
Systems, Snowbird, USA, May 22-26, 2011. Poster: Augmented graph method for
synchronization in directed networks.
Dissertations
Meng Zhao, Ph. D. in Biostatistics. Dissertation Title: Treatment Comparison in Biomedical
Studies Using Survival Function. Graduated in Spring 2011.
Hanfang Yang, Ph. D. in Biostatistics. Dissertation Title: Jackknife Empirical Likelihood and its
Applications, graduated in Summer 2012.
Kun Zhao, Ph.D. Summer, 2012.
Sajiya Jalil, Ph.D. Summer 2012
Pannkal Mathew, Ph.D. Summer 2011.
Jeremy Wojcik, Ph.D. Summer 2012.
Leslie Meadows, Ph.D. in Math. Dissertation Title: ‘Iteratively Regularized Methods for Inverse
Problems’. Defended in July 2013. Employed as Academic Professional at Math and Stat
Department, GSU.
Hui Liu, Ph.D. in Math. Dissertation Title: ‘Stable Numerical Algorithms for Inverse Parameter
Identification Problems’. Expected graduation date: Summer 2014.
Sara Malec, Ph.D. in Math, Dissertation Title: Intersection Algebras and Pointed Rational Cones.
Graduated in Summer 2013.
Shan Luo, Ph.D. in Statistics, graduated in Summer 2013. Dissertation title: New Non-Parametric
Methods For Income Distributions
Binhuan Wang, Ph.D. in Biostatistics, graduated in Summer 2012. Dissertation title: Statistical
Evaluation of Continuous-scale Diagnostic Tests with Missing Data
Haochuan Zhou, Ph.D. in Biostatistics, graduated in Fall 2011. Dissertation title: Statistical
Inferences for the Youden Index
Baoying Yang, Ph.D. in Mathematics and Statistics (Georgia State University and Sichuan
University), graduated in Fall 2010. Dissertation title: The Studies of α-Relative Hyperbolic Affine
Hyper-sphere and Information Geometry
Ye Cui, Ph.D. in Biostatistics, graduated in Spring 2013. Dissertation title: Advanced Designs of
Phase I and Phase II Cancer Clinical Trials.
Haci M. Akcin. Ph.D. in Biostatistics, graduated in Spring 2013. Dissertation title: Nonparametric
Inferences with Right Truncated Data.
Kun Zhao, Ph.D. in Mathematics, Summer 2012 (funded by an NSF grant). Thesis: “Mathematical
methods for network analysis, proteomics, and disease prevention.” Currently: Researcher at the
Centers for Disease Control and Prevention (CDC).
Sajiya Jalil, Ph.D. in Mathematics, Spring 2012. Thesis: “Stability analysis of phase-locked
bursting in inhibitory neuron networks.” Currently: Postdoc at the University of Texas Health
Science Center at Houston.
Theses
1. Yinghua Lu, M.S. in Biostatistics. Thesis Title: Empirical likelihood inference for the
accelerated failure time model via Kendall equation. Graduated in Summer 2010.
2. Xiaoxia Liu, M.S. in Biostatistics. Thesis Title: Semi-empirical likelihood confidence intervals
for the ROC curve with missing data, graduated in Summer 2010.
3. Hanfang Yang, M.S. in Biostatistics. Thesis Title: Empirical likelihood confidence intervals
for ROC curves with right censoring, graduated in Fall 2010.
4. Yueheng An, M.S. in Biostatistics. Thesis Title: Empirical Likelihood Confidence Intervals for
ROC Curves with Missing Data, defended in Fall 2010.
5. Zhengbo Ma, MS in Biostatistics. Thesis Title: A New Jackknife Empirical Likelihood Method
for U-Statistics, graduated in Summer 2011.
6. Hui Zhao, MS in Biostatistics. Thesis Title: Discrimination of High Risk and Low Risk
Populations for the Treatment of STDS, graduated in Summer 2011 (Co-chair).
7. Etienne Twagirumukiza, MS in Biostatistics. Thesis Title: Analysis of Faculty Evaluation by
Students as a Reliable Measure of Faculty Teaching Performance, graduated in Summer 2011.
8. Maxime Bouadoumou, MS in Biostatistics. Thesis Title: Jackknife Empirical Likelihood for
the Accelerated Failure Time Model with Censored Data, graduated in Summer 2011.
9. Hui-Ling Lin, MS in Biostatistics. Thesis Title: Jackknife Empirical Likelihood for the
variance in the Linear Regression Model, graduated in Summer 2013.
10. Xueping Meng, MS in Biostatistics. Thesis Title: Jackknife Empirical Likelihood Inference for
the Absolute Mean Deviation, graduated in Summer 2013.
11. Mary George Whitney, MS in Math. Thesis Title: ‘Theoretical and Numerical Study of
Tikhonov’s Regularization and Morozov’s Discrepancy Principle’. Defended in April 2009;
resulted in journal publication. Employed as Math Instructor at Gwinnett College.
12. Linda DeCamp, MS in Math. Thesis Title: ‘Local Regularization for a Parameter Estimation
Problem in Avian Influenza’. Expected graduation date: Spring 2014.
13. Chenxue Li, M.S. in Statistics, graduated in Fall 2013. Thesis title: Generalized Confidence
Interval for Partial Youden Index and Its Corresponding Cut-Off Point
14. Yanan Yin, M.S. in Statistics, graduated in Fall 2013. Thesis title: Jackknife Empirical
Likelihood-Based Confidence Intervals for Low Income Proportions with Missing Data
15. Yunfeng Tie, M.S. in Statistics, graduated in Spring 2013. Thesis title: Antiretroviral Regimens
in HIV-Infected Adults Receiving Medical Care in the United States: Medical Monitoring
Project, 2009
16. Fang-Di Yang, M.S. in Statistics, graduated in Fall 2012. Thesis title: Racial Differences in the
Prevalence of Depressive Disorders among U.S. Adult Population
17. Dong Yang, M.S. in Statistics, graduated in Fall 2012. Thesis title: Destination after Entering
Foster Care: Road Toward Stability
18. Shuang Liu, M.S. in Statistics. Thesis title: Interval Estimation for the Coefficient of Variation.
Completed in Spring 2012.
19. Aekyung Jung, M.S. in Statistics. Thesis title: Interval Estimation for the Correlation
Coefficient. Completed in Summer 2011.
20. Michael Fost, M.S. in Statistics. Thesis title: The Path from Foster Care to Permanence: Does
Proximity Outweigh Stability? Completed in Summer 2011.
21. Guangming Han, M.S. in Statistics. Thesis title: The Prevalence of Chronic Diseases and Risk
Factors for Death among Elderly Americans. Completed in Summer 2011.
22. Qin Hui, M.S. in Statistics. Thesis title: Testing an Assumption of Non-differential
Misclassification in Case-Control Studies. Completed in Summer 2011.
23. Fengxia Yan, M.S.in Statistics. Thesis title: Racial Disparities Study in Diabetes-related
Complications Using National Health Survey Data. Completed in Fall 2010.
24. Micheal Folarind Master's Thesis, July 2012
25. Jie Yu, Master's Thesis, November 2012
26. Shuman Guo, Master's Thesis, November 2012
27. Yichao Yin, M.S.in Statistics. Thesis title: Identification of Differential Gene Pathways with
Sparse Principal Component Analysis. Completed in Spring 2013.
28. Laura Vazquez, graduated in Spring 2013. Thesis title: A simulation study of algorithm
performance for sparse canonical correlation analysis.
29. Rachid Marlis, M.S. in Math, Thesis title: Geometric Multiplicities and Gershgorin Discs, Fall
2012.
30. Malcom Devoe, MS in Mathematics, Spring 2012. Thesis: “Cellular neural networks with
switching connections”. Currently: Ph.D. student in the GSU College of Education.
Grants, Prizes and Awards
1. Xin Huang (Ph.D. student) won the Eastern North American Region (ENAR) 2010 Spring
Meeting Distinguished Student Paper Award. International Biometric Society. March 21-24,
2010, New Orleans, LA.
2. Ph.D. student Meng Zhao received the Georgia State University dissertation grant, 2011.
3. Ph.D. student Hanfang Yang received the Georgia State University Dissertation Grant, 2012.
4. Ph.D. student Yueheng An received the Junior Researcher Support from the Design and
Analysis of Experiments Conference, Athens, GA, October 17 - 20, 2012.
5. Ph.D. student Hanfang Yang is the finalist in the student paper competition of
nonparametric statistics section, Joint Statistical Meeting, San Diego, CA, July 28August 2, 2012.
6. Ph.D. student Hanfang Yang received support for 14th Meeting of New
Researchers in Statistics and Probability, University of California, San Diego, CA,
July 26-28, 2012.
7. Ph D student Hanfang Yang received Student Travel Award, The Conference on
Statistical Learning and Data Mining, An Arbor, MI, Jun 5-7, 2012.
8. Binhuan Wang (Ph.D. student) was selected as an ASA Nonparametric Statistics Section
Student Paper Award Finalist, Joint Statistical Meetings 2012. July 28 - August 2, 2012, San
Diego, CA.
9. Binhuan Wang (Ph.D. student) received a Student Scholarship that includes free conference
registration and a $150 travel supplement to present his paper at Quality and Productivity
Research Conference 2012. June 4-7, 2012, Long Beach, CA.
10. Binhuan Wang (Ph.D. student) received a 2012 Eastern North American Region (ENAR) Spring
Meeting travel award. $650. April 1-4, 2012, Washington, D.C.
11. Binhuan Wang (Ph.D. student) was awarded a Dissertation Grant from GSU, 2012, Georgia
State University, Atlanta, GA.
12. Yanhong Wang, ORISE fellowship, CDC, ATSDR, 2013.
13. Yanhong Wang, National Science Foundation travel grant awards, SRCOS summer research
conference, 2013.
14. Jie Han (Ph.D. student) was awarded a Dissertation Grant from Georgia State University, 2013.
15. Jie Han (Ph.D. student) received a V. V. Lavroff Scholarship (for outstanding performance in
and outside classrooms), Dept. of Math & Stat, Georgia State University, 2013
16. Xiuxiu He. 2013 MBDAF Fellow Graduate Seed Grant, US $7500
17. Xiuxiu He. 2014 4th Annual Southern California Systems Biology Conference (SoCal SysBio)
Poster Competition, First Prize.
18. Tingli Xing. Poster “Chaos, stirred not shaken” received best poster prize at Dynamics Days US
conference, January 2-5, 2014.
19. Tingli Xing. Poster “Kneading Invariants for the elucidation of chaos” received the first “Red
Socks” prize award at the SIAM Meeting on Applied Dynamical Systems, May 19-23, 2013.
20. Ye Cui. Awarded Dissertation Grant of FY 2013 at Georgia State University.
21. Russell Jeter (Ph.D. student) received a Travel Award from the IEEE Circuits and Systems
Society for attending the 2013 IEEE International Workshop on Complex Systems and
Networks, Simon Fraser University, Vancouver, Canada, December 11-13, 2013 ( $ 1,500).
22. Kun Zhao (Ph.D. student) received a Travel Award for attending the 9th International
Workshop on Complex Systems and Networks, Institute for Mathematics and its Applications
(IMA) at University of Minnesota, September 5-7, 2012 ($1,000).
23. Reimbay Reimbayev (Ph.D. student) received a Travel Award for attending the 9th
International Workshop on Complex Systems and Networks, Institute for Mathematics and its
Applications (IMA) at University of Minnesota, September 5-7, 2012 ($1,000).
Students’ Ph.D. candidacy and qualifying exams:
1. Annie Burnes, August 2013
2. Harrison Stalvey, November 2013
3. Jeffrey McCammon, December 2012
4. Mikhail Stroev has passed 3 Ph.D. Qualifying Exams and the Candidacy Ph.D. Exam.
5. Rachid Marsli has passed 2 Ph.D. Qualifying Exam.
6. Haochuan Zhou (2010),
7. Xin Huang (2010),
8. Meng Zhao (2010),
9. Amy Fomo (2011),
10. Binhuan Wang (2011),
11. Hanfeng Yang (2011),
12. Israel Hora (2011, 2012),
13. Ye Cui (2011, 2012),
14. Heather King (2012),
15. Ali Jinnah (2011, 2012),
16. Yueheng An (2011, 2012),
17. Yanhong Wang (2012),
18. Leslie Meadows (2012),
19. David Yankey (2013),
20. Jenny Jeyarajah (2012),
21. Haiqi Wang (2013),
22. Jing Wang (2013),
23. Maxime Bouadoumou (2013)
24. Jie Han (2013)
25. Wei Gao (2014)
26. Kun Zhao (2011)
27. Sajiya Jalil (2011)
.19 Student outcomes after graduation
Ph.D. Students:
1. Haci M. Akcin, Ph.D. Concentration in Biostatistics, graduated in Summer 2013.
Employment as a mathematical statistician at CDC.
2. Ye Cui, Ph.D. Concentration in Biostatistics, graduated in Spring 2013. Employment at
CDC after graduation.
3. Hai Deng, Ph.D. graduated in 2007 (In Computer Science, directed by Guantao Chen),
Computer Programmer/Analysis, Missouri State University.
4. Shan Luo, Ph.D. Concentration in Biostatistics, graduated in Summer 2013, Employment as
a Ph.D. level statistician in LexisNexis, Alpharetta, Georgia
5. Sara Malec, Ph.D. Concentration in Mathematics, graduated in Summer 2013, Employed as
a Postdoctoral Fellow at the University of the Pacific.
6. Leslie Meadows, Ph.D. Concentration in Mathematics, graduated in Summer 2013,
Employment as an academic professional at Georgia State University.
7. Binhuan Wang, Ph.D. Concentration in Biostatistics, graduated in Summer 2012,
Employment as a research statistician in New York University School of Medicine.
8. Xue Wang, Ph.D. graduated 2009, Employment as a Research Associate at Mayo College of
Medicine, Jacksonville, FL
9. Yanhong Wang, Ph.D. Concentration in Biostatistics, graduated in Fall 2013, is a Senior
Operations Research and Advanced Analytics Specialist in BSNF railway company.
10. Baoying Yang, Ph.D. Mathematics and Statistics (Georgia State University and Sichuan
University), graduated in Fall 2010, Employment as an assistant professor in Southwest
Jiaotong University, Chengdu, China.
11. Hanfang Yang, Ph.D. Concentration in Biostatistics, graduated in summer 2012,
Employment as an assistant professor, School of Statistics, Renmin University of China.
12. Kun Zhao, Ph.D. Concentration in Bioinformatics, Graduated in Spring 2012. Employment
at CDC.
13. Meng Zhao, Ph.D. Concentration in Biostatistics, graduated in Spring 2011, is a Data
Scientist II in CareerBuilder.com, Atlanta, GA.
14. Haochuan Zhou, Ph.D. Concentration in Biostatistics, graduated in Fall 201, is currently a
Ph.D. level statistician in VISA Company, San Francisco, USA.
15. Sajiya Jalil, Ph.D. in Mathematics, Spring 2012. Currently: Postdoc at the University of
Texas Health Science Center at Houston.
MS students:
1. O. Sarajlic is currently in Ph.D. program at the Department of Physics and Astronomy, GSU
2. Jie Yu got jobs in private companies
3. Dr. Brian Cook, former MS. Student (graduated in 2007) currently is a Van Vleck Visiting
Assistant Professor/RTG Postdoctoral Fellow Research Assistant in the Mathematics
Department at the University of Wisconsin-Madison.
4. Laura Vazquez, graduated in Spring 2013. Employment after graduation.
5. Chenxue Li, graduated in Fall 2013, is Ph.D. at GSU.
6. Paradis, Rebecca D., graduated in Fall 2013. Employment in Kaiser Permanente.
7. Wei, Xiaoxi, graduated in Fall 2013. Statistician in LexisNexis
8. Yin,
Yanan,
graduated in Fall 2013. Statistician in State Farm.
9. Zhou, Wen, graduated in summer 2013. Consultant, International Food Policy Research
Institute.
10. Huan, Taoying, graduated in summer 2013. Scientific Data Analyst, ICF international.
11. Meng, Xueping, graduated in summer 2013. Statistician in LexisNexis
12. Tong, Di, graduated in summer 2013. Ph.D. student in Business School at Drexel University.
13. Dong, Xing. Graduated in Spring 2013. Statistical Analyst at ICF International.
14. Jackson-Henry, India. Graduated in Spring 2013. Adjunct Mathematics Instructor at Georgia
Perimeter College.
15. Tie, Yunfeng. Graduated in Spring 2013. Statistician at CDC
16. Yin, Yichao.
Graduated in Spring 2013. Market Research Analyst at BBDO.
17. Folarinde, Micheal. Graduated in Fall 2012. Adjunct Faculty at The Art Institute of Atlanta
18. Guo, Shuman. Graduated in Fall 2012. Program Analyst at JPatton - IMG Company.
19. Marsli, Rachid. Graduated in Fall 2012. Ph.D. student at GSU.
20. Patterson, Gerius D. Graduated in Fall 2012. Analyst at US DHHS Office of Inspector
General.
21. Yang, Dong.
Graduated in Fall 2012. Market Research and Support Analyst at
Southern Company.
22. Yang, Fang-di. Graduated in Fall 2012. Biostatistician at QPS Qualitix.
23. Twagirumukiza, Etienne. Graduated in Summer 2012. Analytics Consultant at Wells Fargo
24. Wang, Hongwei. Graduated in Summer 2012. Statistical Programmer DataTek Inc.
25. Liu,
Shuang
. Graduated in Spring 2012. Operations Reporting Analyst at
Verizon Telematics, Inc.
26. Parker, Bobby I. Graduated in Spring 2012. Senior Risk Engineering Consultant, ECommerce Zurich Financial Services
27. Wang, Liqiao. Graduated in Spring 2012. Data Analyst, Atlanta Convention & Visitors
Bureau
28. Yates, Amy N. Graduated in Spring 2012. Ph.D. at GSU.
29. Wang, Kun. Graduated in Summer 2011. Ph.D. in Biostatistics at New York University
30. Wang, Zhibo. Graduated in Summer 2011. Ph.D. at GSU
31. Bouadoumou,Maxime. Graduated in Summer 2011. Ph.D. at GSU
32. Han, Guangming. Graduated in Summer 2011. Health Surveillance Specialist in Nebraska.
33. Jung, Aekyung. Graduated in Summer 2011. CRA at FDMATE, South Korea
34. Ma, Zhengbo. Graduated in Summer 2011. Ph.D. at UGA.
35. Fost, Michael. Graduated in Summer 2011. Statistician in Georgia State Government
36. Hui, Qin. Graduated in Summer 2011. Computer analyst at ValueCalling, Inc.
37. Liu, Yang. Graduated in Summer 2011. Statistician at CDC.
38. Zhu, Zi. Graduated in Summer 2011. Direct Marketing Business Analyst, Hughes Telematics,
Inc.
39. Amadou Sekou. Graduated in Spring 2011. System Specialist at Fulton County Government.
40. An Yueheng
. Graduated in Spring 2011. Ph.D. student at GSU.
41. Jiang Yong. Graduated in Spring 2011. Risk Analyst at AIG Enterprise Risk Management.
42. Liu Huayu. Graduated in Spring 2011. Ph.D. student at University of Colorado Health
Sciences Center.
43. Ntwoku Boris. Graduated in Spring 2011.
Sr Online Marketing Manager at AT&T
Mobility.
44. Reizer Gabriella. Graduated in Spring 2011. Mathematics Instructor at Georgia Gwinnett
College
45. Syed Ali. Graduated in Spring 2011. Portfolio Manager at US TRUST Wealth Management.
46. Yan, Fengxia. Graduated in Fall 2010. Statistician in Clinical Research Center at Morehouse
School of Medicine
47. Wu, Baohua. Graduated in Fall 2010. Data Analyst and Application Developer, Senior,
Emory University.
48. Akolly, Kokou. Graduated in Summer 2010. Ph.D. student at University of California,
Berkeley - Walter A. Haas School of Business.
49. Li, Ji. Graduated in Summer 2010.
Ph.D. at University of Southern California.
50. Li, Junbo. Graduated in Summer 2010. Pricing Analyst at Infinity Insurance.
51. Jeyarajah, Jenny V. Graduated in Spring 2010. Ph.D. student at GSU
52. Wang, Dongmei. Graduated in Spring 2010. Ph.D. student at Clemenson University.
53. Wenyan Zhou, Graduated summer 2013. Employment as a visiting assistant professor at
University of West Georgia.
54. Muslim Baid, Graduated in 2009, Ph.D. at SUNY Buffalo.
.20 Citations by Tenured and Tenure Track Faculty
ARAV, M.
BELYKH, I.
BONDARENKO, V. E.
CHEN, G.
ENESCU, F.
Citations Papers
Avg. Cites per Paper/Additional Source
21
12
1.75
22
15
133
13
10.23
100
19
1234 Citations in Google Scholar
25
11
2.27
1
5
418 Citations (53 papers) in Web of Science
255
13
19.62
336
100
32
11
2.91
77
19
250 Citations on Google Scholar.
HALL, F.
JIANG, Y.
LI, Z.
MILLER, V.
MONTIEL, M.
OSAN, R.
QI, X.
QIN, G.
SMIRNOVA, A.
STEWART, M.
VIDAKOVIC, D.
YAO, Y.
YE, X.
144
36
4.00
207
69
773 citations in Google Scholar
249
1
249.00
8
6
1605 citations (64 papers) in Google Scholar.
166
30
5.53
165
41
24
3
28
6
4
2
1
1
0
3
0.00
14
6
431 Citations in Google Scholar.
0
1
0.00
4
4
134
29
4.62
45
53
394 Citations in Google Scholar
39
12
3.25
103
31
33
1
82
18
4
4
1.00
0
1
704 Citations in Google Scholar
5
6
0.83
46
13
-
-
-
5
6
139 citations in Google Scholar
8.00
2.00
33.00
ZHAO, YICHUAN
ZHAO, YI
VAN DER HOLST, H
137
36
3.81
56
39
324 Citations in Google Scholar
55
27
2.04
55
19
1
2
250
32
0.50
.21 External Research Grants
Belykh, Igor (Principal), "DynSyst_Special_Topics: Time-varying dynamical networks: theory and
applications," Sponsored by NSF, Federal, $206,350.00. (September 1, 2010 - August 31, 2013).
Belykh, Igor (Principal), “REU Supplement to the existing NSF grant,” $12,500 (separate award, July
2011-August 2014).
Chen, Guantao (Principal), "The Chromatic Index and the Circumference of a Graph," Sponsored by
NSA, Federal, $58,385.00. (February 2012 - August 2014).
Chen, Guantao (Principal), Gould, Ronald J (Principal), Yu, Xingxing (Principal), "Atlanta Lecture
Series on Combinatorics and Graph Theory," Sponsored by NSA, Federal, $42,300.00. (September
2010 - August 2013).
Chen, Guantao (Principal), Tao, Guoyu (Co-Principal), "STD transmission Model," Sponsored by The
Centers of Disease Control and Prevention, $24,500.00. (September 2007 - September 2012).
Chen, Guantao (Principal), "Graph Computing on Finding Long Cycles and Small Dense Subgraphs
with Applications," Sponsored by NSF, Federal, $99,998.00. (June 2005 - June 2009).
Enescu, Florian, "SFH: Small: Collaborative Proposal: Efficient Computer Algebra Techniques for
Scalable Verification of Galois Field Arithmetic Circuits," Sponsored by National Science Foundation,
$188,241.00. (August 1, 2013 - July 31, 2016).
Enescu, Florian (Principal), "Contributions to problems on multiplicities and local cohomology in
positive characteristic," Sponsored by National Security Agency, Young Investigators Grant,
$39,914.00. (March 1, 2012 - February 28, 2014).
Enescu, Florian (Principal), "Hilbert-Kunz multiplicities and local cohomology questions in positive
characteristic," Sponsored by National Security Agency, Young Investigators Grant, $29,882.00.
(February 15, 2010 - February 14, 2012).
Yi Jiang (Principal), Alissa Weaver and Patricia Keely NIH/NCI Multiscale modeling of extracellular
matrix-tumor interactions, NIH/NCI, $1,800,000.
Iman Chahine and Yi Jiang (Co-Principal), Scaffolding Mathematics & Science Teachers Role co-PI,
UGA Teacher Quality, $43,631
Iman Chahine and Yi Jiang (Co-Principal), Building High School Teachers Capacity to Teach
Mathematical Modeling Using Technology-Supported Simulations Role UGA Teacher Quality,
$43,748.
R. Ecke, Yi Jiang (Principal), W. Hlavacek, I. Nemenman, and M. Wall Information processing in
cellular signaling and gene regulation, NIH/NIGMS, $150,000.
Yi Jiang (Consortium PI) and Bridget Wilson, MSM Mapping and Modeling ErbB Receptor Membrane
Topography, NIH $1,800,000.
Yi Jiang (Principal) and Luisa Iruela-Arispe, UC-Lab Modeling of Pathological and Developmental
Angiogenesis, UC-Lab, $1,317,646.
Yi Jiang (Co-Principal) and Charles Reichhardt, Modeling Topotaxis: Getting Bacteria and Cells to Do
Work with Microfabricated Topologies, LANL, 1,050,000
Yi Jiang (Principal), W. Hlavacek, I. Nemenman, and M. Wall, Information processing in cellular
signaling and gene regulation, NIH/NIGMS, $180,000.
Yi Jiang (Co-Principal) and Mark Alber, Integrating Multiscale Modeling and in vivo Experiments for
Studying Blood Clot Development, NSF, $864,000.
Montiel, Mariana (Co-Principal), Jiang, Yi (Co-Principal), Chahine, Iman Chafik (Principal), "Building
High School Teachers Capacity to Teach Mathematical Modeling Using Technology—Supported
Simulations;," Sponsored by Title II Higher Education, Federal, $43,748.00. (January 2013 - December
2013).
Matt Wachowiak (PI), Remus Osan (Co-PI), Erik Sherwood (Co-PI), Active Sensing and Glomerular
Odor Processing in the Rodent Olfactory Bulb: Physiological, Behavioral, and Computational
Analyses, Collaborative Research in Computational Neuroscience (CRCNS, funding agency NIH,
2010-2014). $404,326
Qi, Xin (Co-Principal), "New Developments for Analysis of Two-way Structured Functional Data,"
Sponsored by National Science Foundation (Funded), Federal, $88,688.00
Qin, Gengsheng (Supporting), "Design of MRI Contrast Agents for Molecular Imaging of EGFR/HER2
Targeted Therapy," Sponsored by NIH Grant with Dr. Jenny J. Yang, Federal, $2,423,226.00. (2012 2017).
Qin, Gengsheng (Principal), "Empirical Likelihood-based Inferences for Income Distribution,"
Sponsored by NSA Grant, Federal, $57,287.00. (January 2012 - January 2014).
Smirnova, Alexandra B (Principal), "Continuous Regularization for Nonlinear Ill-Posed Problems,"
Sponsored by National Science Foundation (NSF), Georgia State University, $250,000.00. (2011 2014).
Vidakovic, Draga Djordje (Co-Principal), Thomas, Christine D (Principal), "Partnering to Enhance the
Teaching of Coordinate Algebra (PETCA)," Sponsored by Teacher Quality, State, $46,240.00.
(February 15, 2014 - May 31, 2015).
Vidakovic, Draga Djordje (Co-Principal), Thomas, Christine D (Principal), Fournillier, Janice
Bernadine (Co-Principal), Junor Clarke, Pier Angeli (Co-Principal), "Robert Noyce Urban Mathematics
Educator Program Phase II," Sponsored by NSF-DUE, Federal, $149,474.00. (September 1, 2011 August 31, 2014).
Vidakovic, Draga Djordje (Principal), "Collaborative Research: Linear Algebra in New Environments
(LINE)," Sponsored by NSF DUE - CCLI - Phase I: Exploratory Program, Federal, $38,244.00.
(September 1, 2009 - August 31, 2014).
Thomas, C. and Vidakovic, D. (Co-PI). Partnering to Enhance the Teaching of Coordinate Algebra
(PETCA). Improving Teacher Quality State Grants Program, The University of Georgia, Athens, GA,
($46,240.00), (Awarded, February 15, 2014-May 31, 2015). (State funding).
Thomas, C. and Vidakovic, D. (Co-PI). Partnering to Enhance the Teaching of Analytic Geometry
(PETAG). Improving Teacher Quality State Grants Program, (instruction and research) The University
of Georgia, Athens, GA, ($52,239.00), (Awarded, February 1, 2013-May 31, 2014). (State funding).
Willox, L. and Vidakovic, D. (Co-PI). Improving Mathematical Number Sense and Technology
Integration in the Elementary Classroom. Improving Teacher Quality State Grants Program,
(instruction and research) The University of Georgia, Athens, GA, ($59,860.00), (Awarded, February 1,
2013-May 31, 2014). (State funding).
Thomas, C. and Vidakovic, D. (Co-PI). Collaborative for Mathematics and Science Achievement II.
Improving Teacher Quality State Grants Program, (instruction and research) The University of Georgia,
Athens, GA, ($49,257.00), (Awarded, February 2012-April 2013). (State funding).
Willox, L. and Vidakovic, D. (Co-PI). Improving Mathematical Number Sense and Technology
Integration in the Elementary Classroom. Improving Teacher Quality State Grants Program,
(instruction and research) The University of Georgia, Athens, GA, ($44,808.00), (Awarded, February
2012-April 2013). (State funding).
Thomas, C., Vidakovic, D. (Co-PI), Fournillier, J., and Junor Clarke, P. NSF-DUE - Robert Noyce
Urban Mathematics Educator Program Phase II; (research and instruction) $149,474, (Awarded,
September 1, 2011-August 31, 2014).(Federal funding)
Vidakovic, D. (PI), Collaborative Research: Linear Algebra in New Environments (LINE), NSF DUE CCLI-Phase 1: Exploratory Program, (instruction and research) ($38,244). (Awarded, September 1,
2009-August 31, 2014).
Thomas, C., Vidakovic, D. (Co-PI), Fournillier, J., & Junor Clarke, P. Robert Noyce Urban
Mathematics Educator Program (UMEP). (instruction and research) NSF Robert Noyce Scholarship
Program, Supplemental support, $93,805.00. (Awarded, September 2009-March 2011). (Federal
funding)
Thomas, C., Vidakovic, D. (Co-PI), & Junor Clarke, P. Robert Noyce Urban Mathematics Educator
Program (UMEP). (instruction and research) NSF Robert Noyce Scholarship Program, Supplemental
support, $94,000.00. (Awarded, September 2008-2010). (Federal funding)
Yao, Yongwei (Principal), "Tight closure and primary decomposition in commulative Algebra,"
Sponsored by National Science Foundation, Federal, $83,904.00. (June 1, 2007 - May 31, 2012).
Zhao, Yi (Principal), "Young Investigator Grant," Sponsored by National Security Agency, Federal,
$40,000.00. (April 2012 - March 2014).
Zhao, Yi (Principal), "Young Investigators Grant," Sponsored by National Security Agency, Federal,
$30,000.00. (December 2009 - December 2011).
Zhao, Yichuan (Principal), New Development in Empirical Likelihood, National Security Agency,
February 2012 - February 2014, $40,000.
van der Holst, Hendricus (Principal), "On the Graph Parameters of Colin de Verdière," Sponsored by
NSA, Federal, $31,330.00. (January 1, 2014 - December 31, 2014).
.22 Composition of Faculty
Average Fall Term # of Faculty by Rank and Status
FALL 2010
FALL 2011
FALL 2012
3 YR AVG
Ten Prof
3
3
3
3.0
T Asc P
8
10
11
9.7
2
2
1.3
TT Asc P
TT Ast P
13
9
6
9.3
Total TT
24
24
22
23.3
FT NTT
12
13
14
13.0
Total FT
36
37
36
36.3
PTI
1
2
3
2.0
GTA
24
26
30
26.7
ACAD PROF.
2
3
3
2.7
27
31
36
31.3
ACAD ADMIN.*
GEN. ADMIN.**
PARTIAL
CONTR.
ADJUNCT
Total PT
Year
Status
FALL
2010
Female
FALL
2010
Male
Prof Assoc Assoc Asst NTT NTT PTI
(T) (T) (TT) (TT) Perm. Temp
FT
PT
3
4
4
4
4
9
8
1
GTA
Acad. Prof.
Total
10
2
24
14
39
FALL
2010
Total
3
8
13
12
FALL
2010
Asian
2
2
8
3
FALL
2010
Black
1
FALL
2010
Hisp
1
1
24
2
63
13
1
29
5
6
1
FALL Nat.Am.
2010
FALL
2010
Mixed
FALL Unknown
2010
1
1
FALL
2010
White
1
6
3
9
1
5
1
26
FALL
2010
Total
3
8
13
12
1
24
2
63
FALL
2011
Female
1
14
2
28
FALL
2011
Male
FALL
2011
4
1
3
3
3
6
1
6
7
3
1
12
1
40
Total
3
10
2
9
10
3
2
26
3
68
FALL
2011
Asian
2
3
1
6
2
3
13
1
31
FALL
2011
Black
FALL
Hisp
2
1
2
1
2011
FALL Nat.Am.
2011
FALL
2011
Mixed
FALL Unknown
2011
1
1
FALL
2011
White
1
7
1
2
8
2
10
2
33
FALL
2011
Total
3
10
2
9
10
2
26
3
65
FALL
2012
Female
4
1
2
3
2
1
18
2
33
FALL
2012
Male
3
7
1
4
8
1
2
12
1
39
FALL
2012
Total
3
11
2
6
11
3
3
30
3
72
FALL
2012
Asian
2
4
1
3
2
2
13
1
28
FALL
2012
Black
FALL
2012
Hisp
1
1
4
5
1
2
1
1
FALL Nat.Am.
2012
FALL
2012
Mixed
FALL Unknown
2012
FALL
White
1
7
1
2
9
1
2
11
2
36
2012
FALL
2012
Total
3
11
2
6
11
3
3
30
3
72
.23 Interdisciplinary Publications
The following are recent publications of Mathematics and Statistics Faculty outside of mathematics or
statistics journals.
Jalil, S., Belykh, I., Shilnikov, A. (2012). Spikes matter for phase-locked bursting in inhibitory neurons.
Physical Review E, 85, 036214.
I. Belykh, V. Belykh, R. Jeter, and M. Hasler, Multistable randomly switching oscillators: the odds of
meeting a ghost, European Physical Journal Special Topics, V. 222, 2497-2507, 2013.
I. Belykh and A. Shilnikov, When weak inhibition synchronizes strongly desynchronizing networks of
bursting neurons, Physical Review Letters, V. 101, 078102, 2008.
Jalil, S., Belykh, I., Shilnikov, A. (2010). "Fast reciprocal inhibition can synchronize bursting neurons".
Physical Review E, 81(045201).
Belykh, I., Piccardi, C., Rinaldi, S. (2009). "Synchrony in tritrophic food chain metacommunities,".
Journal of Biological Dynamics, 3(5), 497 – 514.
Bondarenko V. E. A compartmentalized mathematical model of the beta1-adrenergic signaling system
in mouse ventricular myocytes. PLoS ONE 9(2): e89113, 2014.
Mullins, P. D., Bondarenko, V. E. (2013). A Mathematical Model of the Mouse Ventricular Myocyte
Contraction. PLoS ONE, 8(5), e63141.
Petkova-Kirova, P. S., London, B., Salama, G., Rasmusson, R. L., Bondarenko, V. E. (2012).
Mathematical modeling mechanisms of arrhythmias in transgenic mouse heart overexpressing TNFalpha. Am J Physiol Heart Circ Physiol, 302, H934-H952.
Bondarenko, V. E., Bett, G.C.L., Dinga-Madou, I., Zhou, Q., Rasmusson, R.L. (2011). A model of the
interaction between N-type and C-type inactivation in Kv1.4 channels. Biophys. J, 100(1), 11-21.
Bondarenko, V. E., DeSimone, C.V., Zarayskiy, V.V., Morales, M.J. (2011). Heteropoda toxin 2
interaction with Kv4.3 and Kv4.1 reveals differences in gating modification. Mol Phapmacol, 80(2),
345-355.
Zhao, K., Chen, G., Gift, T., Tao, G. (2010). Optimization Model and Algorithm Help to Screen and
Treat Sexually Transmitted Diseases. International Journal of Computational Models and Algorithms in
Medicine (IJCMAM), 1(4), 1-18.
Kirkberger, M., Wang, X., Zhao, K., Tang, S., Chen, G., Yang, J. J. (2010). Integration of diverse
research methods to analyze and engineer Ca2+ binding proteins: From prediction to production. Curr
Bioinform, 5(1), 68-80.
Wang, X., Kirkberger, M., Qiu, F., Chen, G., Yang, J. J. (2009). Towards Predicting Ca2+ - binding
Sites with Different Coordination Numbers in Proteins with Atomic Resolution. Proteins, 75(4), 787798.
Wang, X., Zhao, K., Kirkberger, M., Chen, G., Wong, H., Yang, J. J. (2010). Analysis and prediction of
calciumbinding pockets from apo-protein structures exhibiting calcium- induced localized
conformational changes. P, 19(6), 1180-1190.
Lv, J., Kalla, P., Enescu, F. (2013). Efficient Groebner Basis Reductions for Formal Verification of
Galois Arithmetic Field Circuits,. IEEE Transactions on CAD.
Jiang, Y., Qi, X., Chrenek, M. A., Gardner, C., Grossniklaus, H., Nickerson, J. M. (2013). Functional
principal component analysis of RPE sheet morphology give discriminatory categories. Investigative
Ophthalmology and Visual Science, 54(12), 10.
Shirinifard, A., Glazier, J. A., Swat, M., Gens, J. S., Family, F., Jiang, Y., Grossniklaus, H. E. (2012).
Adhesion Failures Determine the Pattern of Choroidal Neovascularization in the Eye: A Computer
Simulation Study. PLoS Computational Biology, 8, e1002440.
Chrenek, M. A., Nickerson, J. M., Dalal, N., Gardner, C., Grossniklaus, H. E., Jiang, Y. (2012).
Analysis of the RPE sheet in the rd10 retinal degeneration model, in Retinal degenerative Diseases.
Advances in Experimental Medicine and Biology, 723, 641-647.
Wu, Y., Jiang, Y., Kaiser, D., Alber, M. (2011). Behavioral Priniciples of Bacterial Swarm: Learning
from Myxobateria. Physical Biology, 8, 055003.
Jones, S. A., Dollet, B., Slosse, N., Jiang, Y., Cox, S. J., Graner, F. (2011). Two-dimensional
constriction flows of foams, Colloids and Surfaces. Colloids and Surfaces A, 382, 18-23.
Jiang, Y., Mazzitello, K., Chrenek, M., Zhang, Q., Nickerson, J., Grossniklaus, H. Drusen Induced
Morphology Dynamics of Retinal Pigment Epithelium. Physical Biology.
Steinkamp, M., Kanigel-Winner, K., Davies, S., Muller, C., Zhang, Y., Hoffman, R. H., Shirinifard, A.,
Moses, M., Jiang, Y., Wilson, B. S. Ovarian tumor attachment, invasion and vascularization reflect
unique microenvironments in the peritoneum: insights from xenograft and mathematical models.
Frontiers in Molecular and Cellular Oncology.
Nemenman, I., Hlavacek, W. S., Jiang, Y., Wall, M. E., Zilm, A. (2010). Editorial: The Third q- bio
Conference on Cellular Information Processing., 331-333.
Bauer, A. L., Jackson, T. L., Jiang, Y., Rohlf, T. (2010). Stochastic network model of receptor cross-talk
predicts anti-angiogenic effects. J Theor. Biol.(264), 838-846.
Tsien, J., Li, M., Osan, R. M., Kuang, H. (2013). On initial Brain Activity Mapping of episodic and
semantic memory code in the hippocampus. Elsevier, 105, 200-10.
Tsien, J., Li, M., Osan, R. M., Kuang, H. (2013). On brain activity mapping: insights and lessons from
Brain Decoding Project to map memory patterns in the hippocampus. Springer, 56(9), 767-79.
Osan, R. M. A mismatch-based model for memory reconsolidation and extinction in attraction
networks. PLoS One, 6(8), 16.
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0023113
Osan, R. M. Differential consolidation and pattern reverberations within episodic cell assemblies in the
mouse hippocampus. PLoS One, 6(2), 21.
Osan, R. M. The interplay between branching and pruning on neuronal target search during
developmental growth: Functional role and implications. PLoS One, 6(10), 16.
Su, E., Xia, J., Osan, M., Zhang, J., Titan, E., Shinbrot, T., Osan, R. M. Analytical methods for
evaluating targeting performances in stochastic models of neuronal arborization. PLOS Computational
Biology.
Jiang, Y., Qi, X., Chrenek, M. A., Gardner, C., Grossniklaus, H., Nickerson, J. M. (2013). Functional
principal component analysis of RPE sheet morphology give discriminatory categories. Investigative
Ophthalmology and Visual Science, 54(12), 10.
Sarajlic, O., Smirnova, A. B. (2013). Numerical Representation of Weirs Using the Concept of Inverse
Problems. International Journal of Hydraulic Engineering, 2(3), 53-58.
.24 Editorial Work by Departmental Faculty
Belykh, Igor: Editor, Associate Editor, IEICE Journal: "Nonlinear Theory and Applications". (2009 Present) ; Editor, Associate Editor, International Journal of Nonlinear Systems and Applications.
(2009- Present) ; Editor, Associate Editor, International Journal "Dynamics of Continuous, Discrete &
Impulsive Systems. Series B: Applications and Algorithms". (2006 – Present) ; Editor, Associate
Editor, International Journal of Bifurcation and Chaos. (January 1, 2012 - January 1, 2015) ; Editor,
Associate Editor, IEEE Transaction on Circuits and Systems I: Research Paper. (January 1, 2012 December 31, 2013) ; Editor, Guest Editor, Physica D: Special Issue on Evolving dynamical networks.
(September 1, 2012 - May 1, 2013) 2012 Special issue. J.Kurths, M.di Bernardo, M. Porfiri ; Editor,
Guest Editor, International Journal of Bifurcation and Chaos. (2010 – 2012) ; Editor, Associate Editor,
IEEE Transaction on Circuits and Systems II: Express Briefs. (January 1, 2008 - December 31, 2011)
Bondarenko, Vladimir E. : Editor, Journal Editor, PLoS ONE. (January 1, 2013 - December 31, 2013)
Editor, Editor, PLoS ONE. (January 1, 2012 - December 31, 2012) ; Editor, Journal Editor, PLoS ONE.
(2011)
Chen, Guantao : Editor, Senior Editor (PRJ), Graphs and Combinatorics (January 2011 – Present)
Enescu, Florian : Editor, Journal Editor, Algebra-Hindawi Publ. (2012 – Present) ; Editorial Review
Board Member, CAA Undergraduate Research Journal. (2011 - Present)
Hall, Frank J. : Editor, Associate Editor, Central European Journal of Mathematics.; Editor, Associate
Editor, Czech Mathematics Journal. ; Editor, Associate Editor, Journal of Advanced Research in Pure
Mathematics. ; Editor, Associate Editor, JP Journal of Algebra, Number Theory and Applications.
Li, Zhongshan : Editorial Review Board Member (PRJ), Algebra, Yes, appointed. (June 2012 –
Present) ; Editorial Review Board Member (PRJ), JP Journal of Algebra, Number Theory and
Applications, Yes, appointed. (2001 - Present)
Qin, Gengsheng : Editorial Review Board Member, Editorial Board of Statistics Research Letters.
(2012 - Present) ; Editorial Review Board Member, Editorial Board of Open Journal of Statistics.
(2011- Present) ; Editorial Review Board Member, Editorial Advisory Board of The Open Statistics &
Probability Journal. (2008 – Present) ; Editor, Guest Editor, Computational and Mathematical Methods
in Medicine. (2013 - 2014)
Zhao, Yichuan : Editor, Journal Editor, Current Advances in Mathematics. (2013 – Present) ; Editorial
Review Board Member, International Journal of Advanced Statistics and Probability. (2013 – Present) ;
Editor, Associate Editor, International Journal of Statistics in Medical Research. (2013- Present) ;
Editorial Review Board Member, Journal of Statistics: Advances in Theory and Applications. (2013-
Present) ; Editorial Review Board Member, International Journal of Statistics and Probability. (2012Present) ; Editor, Associate Editor, International Journal of Statistics in Medical Research. (2012Present) ; Editorial Review Board Member, Statistics Research Letters. (2012 – Present) ; Editorial
Review Board Member, Open Journal of Statistics. (2011 – Present) ; Editor, Guest Editor, Open
Journal of Statistics. (2013) special issue on Medical Studies in Statistics ; Editor, Guest Editor,
Journal of Probability and Statistics. (2012) , Lead guest editor for special issue on Adaptive and
Sequential Methods for Clinical Trials in 2012 .
.25 International Conferences and Talks
.25.a
International Conferences
Dr. Igor Belykh: Program Committee, Nonlinear Dynamics on Complex Networks and International
Symposium “Topical Problems of Nonlinear Wave Physics,” Nizhny Novgorod, Russia, July 17-24,
2014; Program Committee , The 4th International Conference of Complex Systems and Applications,
Normandy, Le Havre, France, June 23-26, 2014; Program Committee, the 6th International Scientific
Conference on Physics and Control, San Luis Potosí, Mexico, August 26-29, 2013.
Dr. Florian Enescu: Co-organizer (with Cristodor Ionescu) of the Special Session in Commutative
Algebra at the Joint International Meeting between the Amer. Math. Soc. and the Romanian Math. Soc.
in Romania, June 26-30 2013.
Dr. Zhongshan Li: Co-Chair of the Organizing Committee of the 2013 International Workshop on
Graphs and Matrices, held at the North University of China, 7/10-7/13/2013.
Dr. Yichuan Zhao: Technical Program Committee, The Third International Conference on Data
Analytics, Rome, Italy, August 24 - 28, 2014. Program Committee Member, The 2013 IEEE
International Conference on Granular Computing, Beijing, Beijing Institute of Technology, P.R. China,
December 13-15, 2013. Technical Program Committee, The Second International Conference on Data
Analytics, Porto, Portugal, September 29 – October 3, 2013. Organize a topic contributed session
“New Frontiers in Survival Analysis and Empirical Likelihood”, Joint Statistical Meeting, Montreal,
Canada, August 3 - 8, 2013. Chair an invited session “Biostatistics”, the 2nd Taihu International
Statistics Forum, Taihu, Jiangsu, China. July 6-8, 2013. Organize an invited session “Non Parametric
Statistics”, Statistics and its Interactions with Other Domains Conference in Hochiminh City,
VietNam, June 5-7, 2013. International Scientific Program Committee Member, Statistics and its
Interactions with Other Domains Conference in Hochiminh City, VietNam, June 5-7, 2013. Technical
Program Committee, The First International Conference on Data Analytics, Barcelona, Spain,
September 23 - 28, 2012. Program Committee Member, International Symposium on Foundations and
Frontiers of Data Mining, Hangzhou, China, August 11 - 13, 2012. Program Committee Member,
Symposium on Cloud Computing and the Web (CW20102), Hangzhou, China August 11-13, 2012.
Program Committee Member, The 2012 IEEE International Conference on Granular Computing,
Hangzhou, Zhejiang University, P.R. China, Aug 11-13, 2012. Co-organize and chair an invited session
“New Advances in Survival Analysis”. 2nd Biostatistics Symposium, Renmin University of China,
Beijing, July 7-9, 2012. Program Committee Member, IEEE International Conference on Granular
Computing, Kaohsiung, Taiwan, November 08—10, 2011. Chair a “Nonparametric Survival Analysis”
session of Nonparametric Statistics Section, Joint Statistical Meetings, Vancouver, BC, Canada, July 31August 5, 2010. Organize a topic-contributed session “Empirical Likelihood Methods”, Joint Statistical
Meetings, Vancouver, BC, Canada, July 31-August 5, 2010.
Program Committee Member, IEEE International Conference on Granular Computing,
Mountain/Nanchang, China, August 17—19, 2009.
.25.b
Lushan
Hosting of Visiting Scholars
Dr. Frank Hall hosted the visitor Mirek Fiedler who visited GSU in fall 2012 for about two weeks
and gave a talk in our department.
Dr. Yi Jiang hosted an international visitor, Dr. Shoubing Dong, 2014
Dr. Zhongshan Li hosted an international postdoctoral visitor, Dr. Lihua Zhang, 2011—2014.
Dr. Mariana Montiel hostedDr. Yichuan Zhao: the following speakers:
1. Dr. Guerino Mazzola, April 2012 University of Zurich.
2. Dr. Thomas Noll, September, 2011, Speaker, Visiting scholar, Catalonia College of Music (Spain)
3. Dr. Emilio Lluis-Puebla, April 2012, Speaker, National Autonomous University of Mexico,
Dr. Draga Vidakovic hosted/supervised the following international visitor (post-doc):
1. Dr. Tangul Kabael, Andadolu University, Turkey, spring semester 2011
2. Dr. Tangul Kabael, Anadolu University, Turkey, one week visit in August 2012
Dr. Yichuan Zhao hosted an international visiting scholar, Dr. Carolina Baguio, Philipine, 2010.
She gave a talk in our dept on July 2, 2010.
Dr. Yichuan Zhao hosted an international visiting scholar, Mr. Ping Lei, Shanghai International
University of Trade and Business, China, 2012-2013.
Dr. Yichuan Zhao supervised an international visiting scholar, Mr. Mohammad Soliman, Alexandra
University, Egypt, 2013-2014.
.25.c
International Talks
Dr. Igor Belykh:
Dynamical networks with on-off stochastic connections, 2013 IEEE International Workshop on
Complex Systems and Networks, Simon Fraser University, Vancouver, Canada, December 11-13,
2013. Invited speaker .
Ghost attractors in randomly switched dynamical systems, International Conference “Dynamics,
Bifurcations, and Strange Attractors,” dedicated to the memory of L.P. Shilnikov, Nizhny Novgorod,
Russia, July 1-5, 2013. Invited speaker .
Mixed couplings and network synchronization: when friends turn enemies, 7th Crimean School and
Workshop on Emergent Dynamics of Oscillatory Networks, Mellas, Crimea, Ukraine, May 20-27
2012. Invited speaker .
Synchronization in asymmetrical networks, Int. Conference on Nonlinear Dynamics: New Directions,
Guanajuato, Mexico, May 11-15, 2010. Invited speaker .
Synchronization in inhibitory networks of bursting neurons, The 3rd International Conference on
Complex Systems and Applications, University of Le Havre, Normandy, France June 29 - July 02,
2009. Invited speaker .
Synchronization and network topology, Int. Conference “Dynamics and Statistics of Spatially Extended
Systems”, January 17-23, 2009, Banff, Canada. Invited speaker .
What matters in synchronization of bursting neurons, Int. Workshop-School “Chaos and dynamics in
biological networks”, May 5-11, 2008, Cargese, Corsica, France. Invited speaker .
Synchronization in complex networks of bursting neurons, Int. Workshop on Bio-inspired Complex
Networks in Science and Technology: From Topology to Structure and Dynamics (BIONET-2008),
Dresden, Germany, May 5- 9, 2008. Invited speaker .
Patterns of synchrony in bursting neuronal networks, Int. Symposium on Synchronization in Complex
Networks (SynCoNet-2007), Leuven, Belgium , July 2-4 2007. Invited speaker .
Induced synchronization of neuronal networks with strongly desynchronizing connections, Italian
Society for Chaos and Complexity meeting, Politecnico di Milano, Italy, May 16 2007. Invited
speaker .
Dr. Florian Enescu
(May 2011), Local cohomology in positive characteristic, Institute of Mathematics of the Romanian
Academy, Romania.
(May 2011), Hilbert-Kunz multiplicities, University of Bucharest, “Nicolae Radu” Algebra Seminar,
Romania.
(May 2011), The Hilbert-Kunz multiplicity, Algebraic Geometry Seminar, Mainz University, Germany
(May 2012), Lectures on Hilbert-Kunz multiplicities, four one hour talks. Institute of Mathematics of
the Romanian Academy.
(July 2013), The Frobenius Complexity of a Local Ring of Prime Characteristic and Frobenius
Operators, Conference on ‘Commutative Algebra and Interactions with Algebraic Geometry’, Luminy,
Centre International de Recontres Mathematiques, France.
Dr. Frank Hall:
gave 5 invited talks during his time as a Fulbright scholar, 4 in Prague at the Academies and 1 in
Budapest at the Hungarian Academy of Science. Mirek and he have published 9 papers together
(which includes 1 paper in press).
Dr. Zhongshan Li:
Minimum ranks of nonnegative sign patterns and polytopes, invited talk at the 2013 International
Workshop on Matrices and Operators, Beijing Normal University, July 2013.
Sign patterns and point-hyperplane configurations, invited talk at the 2013 International Workshop on
Graphs and Matrices, North University of China, Taiyuan, July 2013.
Sign patterns with minimum rank 3, invited talk at the International Workshop on Graph and
Combinatorics, Anhui University, Hefei, June 2013.
Polytopes and nonnegative sign patterns, invited talk at the 7th Cross Strait Conference on Graph
Theory and Combinatorics, Changsha, June 2013.
Sign patterns with minimum rank 2 and upper bounds on the minimum ranks, invited talk at the 2012
Workshop on Matrices and Operators, Harbin, July 2012.
Sign vectors, duality and minimum ranks, The 10th International Conference on Matrix Theory and Its
Applications, Guiyang, July 2012.
Sign patterns that require all distinct eigenvalues, invited talk at the Combinatorial Matrix Theory
Session of Summer 2011 Canadian Mathematical Society Conference, Alberta, June 2011.
4 by 4 irreducible sign patterns that require all distinct eigenvalues, invited talk at 3rd International
Workshop on Matrices and Applications, LinAn, China, July 2009.
Polytopes and minimum ranks of sign patterns, Lanzhou University, Lanzhou, China, August, 2013.
Minimum ranks of nonnegative sign patterns and polytopes, North University of China, June 2013.
Sign patterns with minimum rank 3 and point--line configurations, North University of China, June
2013.
Survey of sign pattern matrices, University of Science and Technology of China, Hefei, June 2013.
Sign patterns with minimum rank 3 and point--line configurations, University of Electronic Science
and Technology of China, Chengdu, June 2013.
Sign patterns with minimum rank 2 and upper bounds on the minimum ranks, Lanzhou University,
Lanzhou, July 26, 2012.
Sign patterns with minimum rank 2 and upper bounds on the minimum ranks, North University of
China, Taiyuan, June 11, 2012.
Sign vectors, duality and minimum ranks, North University of China, Taiyuan, June 14, 2012.
Sign patterns with minimum rank 2 and upper bounds on the minimum ranks, Shanghai Jiaotong
University, Shanghai, June 21, 2012.
Sign patterns with minimum rank 2 and upper bounds on the minimum ranks, East China Normal
University, Shanghai, June 19, 2012.
Sign patterns with minimum rank 2 and upper bounds on the minimum ranks, Tongji University,
Shanghai, June 20, 2012.
Sign patterns with minimum rank 2 and upper bounds on the minimum ranks, University of Electronic
Science and Technology of Chengdu, June 26, 2012.
Spectrally arbitrary sign patterns, Shanghai Jiaotong University, July 26, 2010.
The field of values of matrices, Lanzhou University, Lanzhou, July 24, 2010.
Sign pattern matrices that require all distinct eigenvalues, Lanzhou University, July 21, 2010.
Spectrally arbitrary sign patterns, Lanzhou University, July 20, 2010.
Foundations of sign pattern matrices, Lanzhou University, July 19, 2010.
Sign pattern matrices that require all distinct eigenvalues, University of Electronic Science and
Technology, Chengdu, July 12, 2010.
Spectrally arbitrary sign patterns, University of Electronic Science and Technology, Chengdu, July 13,
2010.
The numerical range of a matrix, University of Electronic Science and Technology, Chengdu, July 13,
2010.
Sign pattern matrices: a branch of combinatorial matrix theory, Center of Combinatorial Mathematics,
Fuzhou University, Fuzhou, July 7, 2010.
Sign pattern matrices that require all distinct eigenvalues, Chinese Academy of Sciences, Beijing, July
2, 2010.
Spectrally arbitrary sign patterns, Beijing University, July 2, 2010.
Boolean matrix factorizations, University of Electronic Science and Technology, Chengdu, January
2010
4 by 4 irreducible sign patterns that require all distinct eigenvalues, Shanghai Jiaotong University, July
2009
Spectrally arbitrary tree sign patterns of order 4, University of Electronic Science and Technology,
Chengdu, July 2009
4 by 4 irreducible sign patterns that require all distinct eigenvalues, University of Electronic Science
and Technology, Chengdu, July 2009
4 by 4 irreducible sign patterns that require all distinct eigenvalues, Lanzhou University, July 2009
Sign pattern matrices, Lanzhou University, July 2009
Dr. Remus Osan
Oral Presentation: Evaluation of Target Search Efficiency for Neurons with Balanced Arborization
During Developmental Growth, Bioengineering Department, Imperial College London, June 7, 2012
Dr. Yichuan Zhao
Smoothed jackknife empirical likelihood method for ROC curves with missing data, The 2nd Taihu
International Statistics Forum, Taihu, Jiangsu, China. July 6-8, 2013.
Smoothed jackknife empirical likelihood method for ROC curves with missing data, School of
Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China,
July 4, 2013.
New empirical likelihood inference for linear transformation models, The IMS-China International
Conference on Statistics and Probability, June 30-July 4, 2013, Chengdu, China.
Jackknife empirical likelihood method for ROC curves with missing data, Department of Statistics,
School of Mathematics, Southwest Jiaotong University, Chengdu, China, June 27, 2013
Jackknife empirical likelihood method for ROC curves with missing data, Business Information
Management School, Shanghai University of International Business and Economics, Shanghai, China,
June 21, 2013.
Smoothed empirical likelihood for ROC curves with censored data, The 2nd Biostatistics Symposium,
School of Statistics, Renmin University of China, Beijing, China, July 7-9, 2012.
Smoothed jackknife empirical likelihood method for ROC curves with missing data,
Mathematical Sciences, Tsinghua University, Beijing, China, July 9, 2012.
Department of
Empirical likelihood inference for the Cox model with time-dependent coefficients, Center for Statistic
Science, Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese
Academy of Sciences, Beijing, China, July 6, 2012.
Smoothed jackknife empirical likelihood method for ROC curves with missing data, Center for Statistical
Science, Peking University, Beijing, China, July 5, 2012.
Empirical likelihood inference for the Cox model with time-dependent coefficients, School of
Mathematical Sciences, Suzhou University, Suzhou, Jiangsu Province, China, June 25, 2012.
Analysis of microarray gene expression data via correlation principal component regression, School of
Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong,
China, December 24, 2010.
Empirical likelihood intervals for the difference of two quantile functions with right censoring, The
Eighth ICSA International Conference: Frontiers of Interdisciplinary and Methodological Statistical
Research, Guangzhou, China, December 19-22, 2010.
Empirical likelihood inference for the Cox model with time-dependent coefficients via local partial
likelihood, Guanghua Lecture, Statistics School, Southwestern University of Finance and Economics,
Chengdu, Sichuan, China, December 14, 2010.
Semiparametric inference for transformation models via empirical likelihood, Annual Meeting of the
American Statistical Association, Vancouver, BC, Canada, July 31-August 5, 2010.
Empirical likelihood inference for the difference of quantiles with right censoring, 2009 International
Conference on Financial Statistics and Financial Econometrics, Chengdu, China, July 8—10, 2009.
Dr. Mariana Montiel
Nov. 25-29, 2013, Professor abroad, Course on Mathematical Music Theory at the East Chinese
University of Science and Technology;
Dr. Remus Osan
Lecturer for the Transylvanian Experimental Neuroscience Summer School (TENSS), June 1-17, 2013
Short Course in Mathematical Methods in Neurosciences, Lecturer: Dr. Remus Oşan, Georgia State
University, USA, May 22-25, 2012, Universitatea Tehnica Cluj, Cluj-Napoca, Romania
Short Course in Mathematical Methods in Biology, Lecturer: Dr. Remus Oşan, Boston University,
USA, May 23-26, 2011, Universitatea Tehnica Cluj, Cluj-Napoca, Romania
Dr. Hein van der Holst
A Colin de Verdiere-type invariant and odd-K4 and odd-K32-free signed graphs, 2012 Haifa Matrix
theory conference, Haifa. Israel, November 14, 2012.
.26 Study Abroad Programs
May 2014. Study Abroad with Peers. Mathematical Music Theory: Shanghai Experience, China is
directed by Dr. Mariana Montiel.
May 22 – June 14
Remus Osan.
May 11 - 31
2014. Mathematical Biology in Transylvania, Cluj, Romania is directed by Dr.
2014. Mathematical Experience in Nepal, Nepal is directed by Dr. Tirtha Timsina.
Fall 2012 and Fall 2013. Math 7821 Study Abroad in Africa (6 students each Fall), Dr. Margo
Alexander.
.27 Student Faculty Ratios
.28 Credit Hour Generation
Average Annual Credit
Hours by
Level
UG Core
UG Lower
UG Upper
Grad
FY 2011
32,823
2,061
4,083
4,506
FY 2012
31,473
2,201
4,539
4,748
FY 2013
33,239
2,508
4,737
4,988
3 YR
AVG
32,511.7
2,256.7
4,452.8
4,747.3
FALL 2010
FALL 2011
FALL 2012
3 YR
AVG
242.7
442.8
685.6
369.0
281.0
670.5
184.3
463.2
647.5
145.5
240.7
711.7
154.2
373.4
527.6
115.0
319.3
696.3
Average Fall
Credit Hours
by Faculty
Type
TT
FT NTT
TOTAL FT
PTI
GTA
ACAD PROF.
ACAD ADMIN.*
GEN. ADMIN.**
PARTIAL CONTR.
193.7
426.5
620.2
209.8
280.3
692.8
ADJUNCT
TOTAL PT
1,320.5
1,097.9
1,130.6
1183.0
.29 Assessment Reports for the Mathematics Major
Below are the official assessment reports for the major. The assessment was based on the performance
of students in the capstone course Math 4991 Senior Seminar.