STATE COUNCIL OF HIGHER EDUCATION FOR VIRGINIA
Program Proposal
Cover Sheet
1. Name of institution: George Mason University
2. Title of proposed program: COMPUTATIONAL SCIENCE
3. Degree title:
M.S. in COMPUTATIONAL SCIENCE
4. CIP number: 30.0801
5. Term and year in initiation: FALL 2002
6. Term and year of first graduates:
SPRING 2004
7. For community colleges: date of
approval by local board
8. Date of approval by Board of Visitors of
State Board for CC:
9. If collaborative program, name of other institution(s):
10. Location of program within institution (please complete for every level, as
appropriate). If any of these organizational units would be new, please so indicate and
attach a revised organizational chart.
School(s) or college(s) of: SCHOOL OF COMPUTATIONAL SCIENCES
GEORGE MASON UNIVERSITY
11. Name, title, telephone number, and email address of person(s) other than the institution’s chief
academic officer who may be contacted by or may be expected to contact Council staff about the proposal.
PETER A. BECKER
ASSISTANT DEAN FOR GRADUATE STUDIES
GEORGE MASON UNIVERSITY
703-993-3619
pbecker@gmu.edu
TABLE OF CONTENTS
BACKGROUND
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DESCRIPTION OF PROPOSED PROGRAM
Program Mission
Program Objectives
Admission Criteria
Program Structure
Curriculum Requirements
Sample Course Schedules
Relation to Other GMU Programs
Comparison with Other Programs in Virginia
Comparison with Other Programs in Washington, D.C. Region
Relevance of Proposed Degree for Northern VA
Faculty
M.S. Faculty Oversight Committee
Evaluation of Program Effectiveness
3
3
4
5
5
6
7
9
10
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10
11
11
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JUSTIFICATION FOR PROPOSED PROGRAM
Student Demand
Demand for Graduates
12
12
13
CONCLUSION
13
APPENDIX
Catalog Descriptions of New Courses
Catalog Descriptions of Existing Courses
15
15
16
2
BACKGROUND
In 1992, George Mason University’s School of Computational Sciences (formerly the
Institute for Computational Sciences and Informatics) established the Ph.D. degree in
Computational Sciences and Informatics (CSI). Since that time, roughly 50 students have
obtained their doctoral degrees in CSI. The program is nationally recognized as the first
and the most forward-looking interdisciplinary Ph.D. degree of its kind in the nation.
In recent years it has become clear that the CSI Ph.D. does not satisfy the needs of many
students in the Washington, D.C. area who seek a Master’s degree in Computational
Science as a terminal degree or as a step towards the Ph.D. That need is recognized by
the new degree proposed here, which is innovative and interdisciplinary, and builds upon
the strength of the CSI Ph.D. program. Justification for the proposed degree is provided
not only by evidence of significant regional and national demand, but also by the success
of the CSI doctoral program itself. Additional evidence is provided by detailed studies
conducted by the Department of Energy, the National Academy of Sciences, the National
Research Council, and the National Bureau of Labor Statistics, which all strongly
indicate that there is growing demand nationally for graduates with the M.S. in
Computational Science. The proposed degree will be unique in the Commonwealth, and
quite distinct from existing M.S. programs at George Mason in disciplines such as
Computer Science, Physics, Chemistry, Mathematics, etc. Therefore it is anticipated that
the new M.S. degree in Computational Science will significantly enhance overall
graduate enrollment at George Mason.
DESCRIPTION OF PROPOSED PROGRAM
Program Mission
The innovative, interdisciplinary M.S. in Computational Science proposed here addresses
the growing national and regional demand for trained computational scientists. Local
corporations expected to hire graduates of the program include Mitre, Hughes, AOL, as
well as many other consulting, contracting, and telecommunications firms. The proposed
degree combines a solid foundation in information technology skills with computational
courses in a variety of scientific areas. The flexibility of the degree structure permits
students to custom-design their curriculum under an advisor’s guidance, making the M.S.
in Computational Science especially relevant for students employed in today’s diverse
Northern Virginia high-technology workplace. The proposed M.S. degree is intended for:
 Students seeking advancement in their current career, or planning to enter a new field.
 Students en route to the CSI Ph.D. degree.
 Students who do not pass the Candidacy Exam in the CSI Ph.D. program and who
would therefore benefit from the availability of a terminal Master’s degree.
All courses are offered in the late afternoon or early evening to accommodate students
with full-time employment outside the university. Persons employed at area hightechnology organizations may take up to 6 credits (out of 30) for work done on the job
3
under the guidance of a faculty member. This work-related project may be applied either
as an optional 3-credit research project or as an optional 6-credit master’s thesis.
Within SCS, we have already developed the central components of a thriving,
interdisciplinary doctoral program in Computational Sciences and Informatics. The
proposed new degree is based upon a similar set of academic principles. The new M.S.
program includes a flexible core of computational classes as well as a number of
additional applications courses associated with various disciplines in the natural sciences,
mathematics, and statistics. The majority of these classes are CSI courses offered by SCS.
The new degree utilizes an extensive base of existing coursework associated with the CSI
Ph.D. program.
Program Objectives
Computational Science is an interdisciplinary area that draws upon the traditionally
distinct areas of computer science, applied mathematics and statistics, and one or more of
the natural sciences. It is more than any of its component fields, and therefore it does not
fit within any of the traditional subject areas. An example of a research endeavor that
would be considered part of Computational Science is the development of a large-scale
computer simulation code to investigate a scientific question that goes beyond the use of
canned (commercial or freely available) software. The scientific component in this case
may be fluid dynamics, astrophysics, physics, or chemistry. Another example is the
development of a large-scale Earth Observing data archive and query system that
facilitates the analysis and validation of global climate models. This requires not only an
understanding of the science, but also insight into a number of specialized aspects of the
simulation, such as computer architecture or algorithms, that would ordinarily be
considered to lie inside a “black box” from the viewpoint of the scientific user. This often
results in much higher performance, efficiency, or scalability than a generic “brute force”
technique is likely to achieve, resulting in an unprecedented degree of fidelity in the
results of the simulation.
At the time of completion, students should be able to:

Work easily with concepts such as numerical methods, computer architectures,
parallel algorithms, and the necessary elements of applied mathematics.

Apply critical thinking skills to the development of computer simulations and
models

Evaluate quantitatively the performance of algorithms and codes.

Analyze, visualize, and interpret data and data systems.

Apply computational techniques to solve problems in a scientific application area.

Work collaboratively in interdisciplinary groups.
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Admission Criteria
Successful applicants to the M.S. program in Computational Science must satisfy the
following entrance requirements:
B.S. degree in computer science, mathematics, statistics, natural science, or
engineering.
GPA of 3.00 or higher in their undergraduate degree.
Satisfactory performance on GRE-GEN exam.
TOEFL exam is required for all foreign applicants.
Three letters of recommendation.
Fluency in a computer programming language.
Mathematics up to and including ordinary differential equations.
payment of application fee.
Applicants failing to satisfy one or more of the above requirements for full admission
may be considered for provisional admission on a case-by-case basis. Students lacking
certain necessary elements of information technology may be advised to take one or more
of the 600-level Computational Tools Courses in the CSI catalog.
Satisfaction of the entrance criteria will be determined by the SCS Graduate Program
Coordinator in consultation with the M.S. Faculty Oversight Committee described below.
Program Structure
The general academic requirements for completion of the M.S. in Computational Science
are organized into several areas:
Computational Courses -- intermediate and advanced concepts including numerical
analysis; algorithm development; simulation; computational techniques.
Computational Science Electives -- computer-intensive courses in a scientific area.
Seminar/Colloquium -- 1 credit hour.
Thesis -- optional.
Work-related project -- optional.
The proposed M.S. in Computational Science is centered on a strong computational
component, which comprises 22 hours of coursework. The remaining 9 hours represents
the scientific component, and centers on specific scientific areas such as mathematics,
physics, chemistry, biology, statistics, etc. However, in keeping with the interdisciplinary
nature of the proposed degree and the limited scope of the scientific component, we do
not include explicit areas of concentration within the degree structure. This provides
students with a flexible set of options that can be used to create their own customized
curriculum under the guidance of a faculty advisor. We believe that the flexibility of this
type of degree structure is particularly appropriate for a rapidly emerging field such as
computational science. Furthermore, the proposed design elements are consistent with
5
guidelines for computational science graduate programs suggested by the Society for
Industrial and Applied Mathematics.
The computational science electives are courses in which actual, hands-on simulations
and data-intensive projects are performed that utilize the mathematics, algorithms, and
information technology skills obtained in the previous courses. As such, they represent
the “capstone” synthesis of the coursework sequence. However, students are also
encouraged to undertake an optional master’s thesis or research project that allows them
to gain more extensive experience in the development of large-scale simulations or other
aspects of computational science. The research project is one of the innovative features of
the proposed degree program that is likely to be attractive to many students in the local
high-technology workplace. We provide a more detailed summary of the curriculum
requirements for the proposed degree below.
Curriculum Requirements
Candidates for the M.S. degree in Computational Science must successfully complete 31
credit hours as follows:
1. 9 credit hours of Computational Core Courses as follows. All students must take
 CSI 700 Numerical Methods (3:3:0)
plus two of the following courses:





CSI 701 Foundations of Computational Science (formerly CSI 801) (3:3:0)
CSI 702 High-Performance Computing (new course) (3:3:0)
CSI 703 Scientific and Statistical Visualization (formerly CSI 803) (3:3:0)
CSI 710 Scientific Databases (formerly CSI 810) (3:3:0)
2. 12 credit hours of Computational Techniques courses from the following list:














MATH 686 Numerical Solutions of Differential Equations (3:3:0)
INFS 614 Database Management (3:3:0)
CS 635 Foundations of Parallel Computation (3:3:0)
CSI 654 Data and Data Systems in the Physical Sciences (new course) (3:3:0)
CSI 701 Foundations of Computational Science (formerly CSI 801) (3:3:0)
CSI 702 High-Performance Computing (new course) (3:3:0)
CSI 703 Scientific and Statistical Visualization (formerly CSI 803) (3:3:0)
CSI 709 Topics in Computational Sciences and Informatics (3:3:0)
CSI 710 Scientific Databases (formerly CSI 810) (3:3:0)
CSI 721 Computational Fluid Dynamics I (3:3:0)
CSI 740/MATH 625 Numerical Linear Algebra (3:3:0)
CSI 744 Linear and Nonlinear Modeling in the Natural Sciences (3:3:0)
CSI 771/STAT 751 Computational Statistics (3:3:0)
CSI 773/STAT 663 Statistical Graphics and Data Exploration (3:3:0)
6

3. 9 credit hours of Computational Science Electives from the following list, or
additional courses as approved by advisor:

 CSI 722 Computational Fluid Dynamics II (3:3:0)
 CSI 730 Biological Sequence Analysis (3:3:0)
 CSI 731 Protein Structure Analysis (3:3:0)
 CSI 732 Genomics (3:3:0)
 CSI 734 Computational Neurobiology (3:3:0)
 CSI 735 Computational Neuroscience Systems (3:3:0)
 CSI 746 Wavelet Theory (3:3:0)
 CSI 748/MATH 629 Symbolic Computation (3:3:0)
 CSI 754 Earth Observing/Remote Sensing Data and Data Systems (3:3:0)
 CSI 758 Visualization and Modeling of Complex Systems (3:3:0)
 CSI 761/ASTR 761 N-Body Methods and Particle Simulations (3:3:0)
 CSI 780/PHYS 613 Computational Physics and Applications (3:3:0)
 CSI 786 Molecular Dynamics Modeling (3:3:0)
 CSI 787 Computational Materials Science (3:3:0)
 CSI 788/PHYS 728 Simulation of Large-Scale Physical Systems (3:3:0)
 OR 635 Discrete System Simulation (3:3:0)
 OR 644 Nonlinear Programming (3:3:0)
4. 1 credit hour of Seminar or Colloquium from any scientific area within SCS:

 CSI 898 Research Colloquium in Computational Sciences and Informatics (1:1:0)
 CSI 899 Colloquium in Computational Sciences and Informatics (1:1:0)
 CSI 991 Seminar in Scientific Computing (1:1:0)
5. Optional 3 credits of CSI 798 Research Project -or- optional 6 credits of CSI 799
Master’s Thesis. Note: utilization of one of these options results in a
corresponding reduction in the requirement for the Computational Science
Electives (category 3 above).
Sample Course Schedules
Students enrolled in the interdisciplinary M.S. program in Computational Science will be
presented with many options as they work with their advisor to design an appropriate
curriculum, based on their background and interests. We present below a few possible
sample course schedules based on various student interests.
Example 1: Student Interested in Computational Fluid Dynamics
(Thesis Option)
Fall Year 1:

 CSI 700 Numerical Methods
 MATH 686 Numerical Solutions of Differential Equations
 CSI 780/PHYS 613 Computational Physics and Applications

7
3 credits
3 credits
3 credits

Spring Year 1:

 CSI 701 Foundations of Computational Science
 CSI 740/MATH 625 Numerical Linear Algebra
 CSI 721 Computational Fluid Dynamics I
3 credits
3 credits
3 credits

Fall Year 2:

 CSI 702 High-Performance Computing
 CSI 722 Computational Fluid Dynamics II
 CSI 991 Computational Fluid Dynamics Seminar

3 credits
3 credits
1 credit
Spring Year 2:

 CSI 799 Master’s Thesis

6 credits
Total:
31 credits

Example 2: Student Interested in Large-Scale Physical Simulation
(Coursework Option)
Fall Year 1:

 CSI 700 Numerical Methods
 CSI 710 Scientific Databases
 CSI 740/MATH 625 Numerical Linear Algebra

3 credits
3 credits
3 credits
Spring Year 1:

 CSI 701 Foundations of Computational Science
 CSI 761/ASTR 761 N-Body Methods and Particle Simulations
 MATH 686 Numerical Solutions of Differential Equations

3 credits
3 credits
3 credits
Fall Year 2:

 CSI 702 High-Performance Computing
 CSI 758 Visualization and Modeling of Complex Systems
 CSI 991 Computational Physics Seminar

3 credits
3 credits
1 credit
Spring Year 2:

 CSI 786 Molecular Dynamics Modeling
 CSI 788/PHYS 728 Simulation of Large-Scale Physical Systems
Total:
3 credits
3 credits
31 credits

Example 3: Student Interested in Earth Observing Data Systems
(Project Option)
Fall Year 1:

 CSI 700 Numerical Methods
 CSI 654 Data and Data Systems in the Physical Sciences
8
3 credits
3 credits
 INFS 614 Database Management

3 credits
Spring Year 1:

 CSI 701 Foundations of Computational Science
 CSI 710 Scientific Databases
 CSI 773/STAT 663 Statistical Graphics and Data Exploration

3 credits
3 credits
3 credits
Fall Year 2:

 CSI 703 Scientific and Statistical Visualization
 CSI 754 Earth Observing/Remote Sensing Data and Data Systems
 CSI 991 Remote Sensing/Space Sciences Seminar
3 credits
3 credits
1 credit
Spring Year 2:

 CSI 758 Visualization and Modeling of Complex Systems
 CSI 798 Research Project
3 credits
3 credits
Total:
31 credits
Relation to Other GMU Programs
No degree program comparable to the proposed M.S. in Computational Science is
currently offered by GMU. The substantial difference in emphasis between the existing
M.S. programs at GMU and the proposed M.S. in Computational Science is highlighted
by comparing the curriculum requirements for the various programs. The disciplinebased M.S. degrees offered by the various CAS departments such as Mathematics,
Statistics, Physics, Biology, Geography, and Chemistry are generally more sciencefocused and much less centered on the utilization of information technology components
in the conduct of scientific research. On the other hand, the M.S. degree offered by the
Department of Computer Science in the School of Information Technology and
Engineering is strongly focused on “traditional” information technology concepts such as
hardware and software issues. Since it does not contain any applied computational
science component, it is not comparable to the proposed degree. Hence the
interdisciplinary nature of the proposed degree makes it unique among the academic
programs offered by GMU.
Due to the marked difference in emphasis between the proposed M.S. in Computational
Science and the existing M.S. degrees in Computer Science, Physics, Mathematics,
Chemistry, Statistics, etc. we believe that the new degree will prove uniquely attractive to
prospective students. Consequently the new degree is not expected to compete directly
with the existing degrees. Rather, it is anticipated that the new degree will result in the
recruitment of regional students who would otherwise have sought degrees in
Computational Science at different schools, most notably the University of Maryland.
Moreover, the presence of a significant number of new students in the proposed M.S.
program will help to increase the class enrollments of many academic units at George
Mason, including those outside the School of Computational Science.
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Comparison with Other Programs in Virginia
No other Virginia institution of higher learning currently offers the M.S. degree in
Computational Science. The most closely related other programs are briefly discussed
below.
Old Dominion University offers a certificate program in Computational Science &
Engineering that graduate students can seek as part of their M.S. or Ph.D. in the
Mathematics & Statistics Department.
William & Mary offers M.A., M.S., and Ph.D. degrees awarded by the affiliated
department or school with the citation "With a Specialization in Computational Science."
Comparison with Other Programs in Washington, D.C. Region
The most closely-related existing degree available in the Washington, D.C. area is the
Master's degree in Applied Mathematics and Scientific Computation with a concentration
in Scientific Computation offered by the University of Maryland. The general structure is
similar to that proposed here. The requirements for the Master’s degree offered by UMD
are summarized below:
Master (thesis)
Master (no thesis)
Scientific Computing I & II
Computer Org. & Prog. for Sci. Comp.
Advanced Scientific Comp. I & II
Core Science Courses
Applications Courses
Electives
Thesis
Total
6 hrs
3 hrs
0
6 hrs
3 hrs
6 hrs
6 hrs
30 hrs
6 hrs
3 hrs
6 hrs
6 hrs
3 hrs
6 hrs
0 hrs
30 hrs
The proposed M.S. degree in Computational Science differs from the UMD degree
described above in several respects. In particular, the proposed degree includes:
 more emphasis on computational methods and techniques and less emphasis on
traditional theoretical science.
 an optional 3 credit hour work-related project.
 a requirement of 1 credit hour of seminar or colloquium.
We believe that these differences make the proposed degree more desirable for students
seeking a competitive advantage in the local high-technology economy.
Relevance of Proposed Degree for Northern VA
Since there are few competing degrees in the Washington, D.C. metropolitan area, and
none in Virginia, it is anticipated that the proposed Computational Science M.S. degree
will become the preeminent such interdisciplinary degree in the surrounding region. This
is especially relevant for the Northern Virginia area that GMU serves most directly,
10
because the availability of a unique interdisciplinary degree involving science and
computing will be of strong interest to area employers such as Mitre, Hughes, SAIC,
CSC, SRA, AOL, etc., and also to area government laboratories such as NRL, NIH,
NASA, NIST, etc.
Due to the strong national and international reputation of the CSI Ph.D. program, SCS is
extremely well positioned to market the new Computational Science M.S. degree both
locally and nationally.
Faculty
Currently, there are 37 SCS faculty members who teach in the CSI doctoral program, in
addition to another 6 faculty from other academic units. It is anticipated that initially the
faculty already associated with SCS will be adequate to address the needs of the new
M.S. students.
M.S. Faculty Oversight Committee
A five-member M.S. Faculty Oversight Committee will review the progress of students
towards the M.S. degree in Computational Science. They will also assist the Graduate
Coordinator in the review of applicants to the program. The membership of the
committee will reflect the diverse scientific interests of the SCS faculty. An individual
master’s degree advisor will be assigned to each student at the time of acceptance into the
program based on the scientific interest the student indicates on the application.
Evaluation of Program Effectiveness
Student achievement in the existing Ph.D. program in Computational Sciences and
Informatics is measured in a number of ways, as is the overall quality of the program.
Many of the same assessment tools would be used in relation to the proposed M.S. degree
in Computational Science. Existing assessment measures, along with their frequency of
implementation and relevant benchmarks, include:

Annual reviews of students’ academic progress. This includes both coursework
relative to requirements and review of thesis if the student has selected that
option.

Annual review of graduates’ academic outcomes.

Annual exit interviews with graduates to assess satisfaction with the program.
This will be analyzed in conjunction with data collected during entrance
interviews to gain additional insight into student satisfaction relative to
expectations.

Annual monitoring of rate of acceptance to Ph.D. programs.
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
Annual data collection regarding success in obtaining or enhancing employment.

Annual alumni satisfaction surveys (at least 85% satisfied or very satisfied).

Meeting enrollment targets (maintaining minimum of 80 students, beginning in
the third year).
The CSI Ph.D. program routinely reviews feedback from its program assessment tools
and revises policies, curriculum, and recruitment efforts accordingly. This practice would
continue with the M.S. degree in Computational Science. All data would be made
available for incorporation into departmental and university-wide assessment documents.
JUSTIFICATION FOR PROPOSED PROGRAM
Student Demand
We are projecting an initial enrollment of approximately 30 students in the program’s
first year (2002-2003), 55 students in the second year, and 80 students in the third and
subsequent years. This estimate is based on a number of factors, including the benchmark
provided by the rate of enrollment of new students into the CSI Ph.D. program
(approximately 24 per year), and the pent-up regional demand for the new master’s
degree as indicated by the survey results discussed below. It is also anticipated that many
students currently enrolled in the Computational Techniques and Applications certificate
program offered by SCS will find the new M.S. degree an attractive academic goal to
pursue upon completion of the certificate. The proposed degree would also serve as a
terminal degree for CSI doctoral students who are unable to pass the doctoral candidacy
exam (note, however, that this may require the student to take some additional courses).
There is evidence indicating strong student demand for the proposed degree. In Summer
2001, a survey was conducted of current SCS students in all programs regarding their
level of interest in a possible M.S. degree in Computational Science. Asked if they would
be interested in applying for admission into the new degree program, 48% said they were
very interested, 32% said they were somewhat interested, and 20% indicated that they
were not currently interested. The students surveyed also indicated that they know of
approximately 75 prospective students (not currently in any SCS program) who would be
interested in the new degree if it is offered. Furthermore, the SCS Graduate Coordinator
receives a steady stream (several per semester) of requests for information from
prospective students seeking the M.S. degree in Computational Science.
Many of the new students (as opposed to current SCS students) enrolling in the M.S.
program will be members of the local workforce who are interested in career
advancement, but are perhaps not inclined to make the commitment to a Ph.D. program at
this time. There is anecdotal evidence to support this expectation provided by a steady
stream (several per year) of information requests from prospective students seeking the
M.S. degree in Computational Science.
12
New GMU graduates with bachelor’s degrees in undergraduate majors such as physics,
mathematics, computer science, statistics, and biology may also be potentially interested
in the degree proposed here due to its interdisciplinary nature. We expect a significant
number of these students to apply to the new program.
Demand for Graduates
Graduates with the M.S. degree in Computational Science will be qualified to work in
private industry and also in government laboratories and bureaus in fields such as
computational statistics, mathematics, physics, astronomy, biology, climate dynamics,
and Earth observing/remote sensing. The Bureau of Labor Statistics Occupational
Outlook Handbook, 2000-1 Edition provides some useful insights into the expected
demand for alumni of computational science graduate programs. It states that:
“Employment of computing professionals is expected to increase much faster than
average (increase by 36% or more between 1998 and 2008) as technology becomes more
sophisticated and organizations continue to adopt and integrate these
technologies...Master’s degree holders with extensive training in mathematics and a
related discipline, such as computer science, economics, engineering, or operations
research, should have good employment opportunities in related occupations.”
Because employers continue to seek computer professionals who can combine strong
technical skills with good interpersonal and business skills, graduates with non-computer
science degrees, who have had courses in computer programming, systems analysis, and
other information technology areas, should also continue to find jobs as computer
professionals.
Additional insight is provided by the Society for Industrial and Applied Mathematics
Working Group on Computational Science and Engineering (CSE) Education, which
states that: “Research in CSE involves the development of state of the art computer
science, mathematical and computational tools directed at the effective solution of realworld problems from science and engineering…We believe that CSE will play an
important if not dominating role for the future of the scientific discovery process and
engineering design…There is a strong feeling that the current climate is highly favorable
toward interdisciplinary work in science and engineering.” Hence it is anticipated that the
demand for holders of the proposed M.S. degree in Computational Science will be quite
strong.
CONCLUSION
Establishment of the proposed M.S. degree program would be advantageous to George
Mason University, the Commonwealth of Virginia, and to the Washington, D.C. region.
George Mason has the resources in place to support this innovative, interdisciplinary
degree, including faculty, library facilities, computer labs, networking, and general
technology support. The proposed degree program is unique in Virginia and would
complement rather than compete with the existing Master’s programs at GMU, including
13
computer science, mathematics, physics, biology, chemistry, and statistics.
It is anticipated that the M.S. in Computational Science will also enhance enrollments in
the already highly successful CSI Ph.D. program by providing students with a significant
intermediate step below the doctoral level. There is substantial evidence suggesting
strong student demand for this new degree program. Sources of applicants will include
graduates of Mason’s undergraduate programs in computer science, mathematics,
physics, chemistry, biology, and statistics. Graduates from other area schools with one of
these majors may also find the M.S. degree in Computational Science an attractive
alternative in a region dominated by information technology-related industries and
government facilities. Graduates from the proposed M.S. program will have many
opportunities to seek or continue employment in high technology fields in the Northern
Virginia region. As the only school in the state to offer this innovative degree, George
Mason is well positioned to enjoy significant student demand and in so doing to prepare
students for challenging and exciting careers.
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APPENDIX
Catalog Descriptions of New Courses
CSI 654 Data and Data Systems in the Physical Sciences (3:3:0). Prerequisites:
Competency in programming at the level of CSI 601-607 or permission of instructor.
This course introduces the student to data issues associated with modern physical
sciences. Specifically, it examines data access, formats, browsing, analysis, visualization
and data information systems in federated environments. Illustrative examples are used
from the physical sciences, including astronomy and space sciences; Earth sciences;
Earth observing and other fields of physics; as well as model output data and associated
special issues. The student is introduced to some mathematical techniques that are
particularly important for large databases, including principal component analysis,
dimensional reduction, etc.
CSI 701 Foundations of Computational Science (3:3:0). Note: This is a renumbering
of CSI 801 so that M.S. students have access. Course level and content is unchanged.
Prerequisites: Competency in UNIX and programming at the level of CSI 601-604, CSI
700, or permission of instructor. Covers the mapping of mathematical models to
computer software, including all aspects of the development of scientific software, such
as architecture, data structures, advanced numerical algorithms, languages,
documentation, optimization, validation, verification, and software reuse. Examples in
bioinformatics, computational biology, computational physics, and global change
demonstrate scientific advances enabled by computation. Class projects involve working
in teams to develop software that implements mathematical models, using the software to
address important scientific questions, and conducting computational experiments with it.

CSI 702 High-Performance Computing (3:3:0). Prerequisites: CSI 700 and CSI 701, or
permission of instructor. Hardware and software associated with high-performance
scientific computing. Computer architectures, processor design, programming paradigms,
parallel and vector algorithms. Emphasis on the importance of software scalability in
science problems.

CSI 703 Scientific and Statistical Visualization (3:3:0). This is a renumbering of CSI
803 so that M.S. students have access. Course level and content is unchanged.
Prerequisite: STAT 554 or CS 651 or permission of instructor. Covers visualization
methods used to provide new insights and intuition concerning measurements of natural
phenomena and scientific and mathematical models. Presents case study examples from a
variety of disciplines to illustrate what can be done. Topics include human perception and
cognition, an introduction to the graphics laboratory, elements of graphing data,
representation of space-time and vector variables, representation of 3-D and higher
dimensional data, dynamic graphical methods, and virtual reality. Students are required to
work on a visualization project. Software tools on the Silicon Graphics workstation are
emphasized, but other workstations and software may be used for the project.
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
CSI 710 Scientific Databases (3:3:0). This is a renumbering of CSI 810 so that M.S.
students have access. Course level and content is unchanged. Prerequisite: INFS 614 or
equivalent, or permission of instructor. Study of database support for scientific data
management. Covers requirements and properties of scientific databases, data models for
statistical and scientific databases, semantic and object-oriented modeling of application
domains, statistical database query languages and query optimization, advanced logic
query languages, and case studies such as the human genome project and Earth-orbiting
satellite.

CSI 798 Research Project (3:0:0). Prerequisites: Twelve graduate credits and
permission of instructor. Project chosen and completed under the guidance of a graduate
faculty member, which results in an acceptable technical report.
CSI 799 Master’s Thesis (1-6:0:0). Prerequisites: Twelve graduate credits and
permission of instructor. Project chosen and completed under the guidance of a graduate
faculty member, which results in an acceptable technical report (master’s thesis) and oral
defense. Graded S/IP.
Catalog Descriptions of Existing Courses
(The following course descriptions refer to existing courses mentioned in this proposal.
The list does not include all courses offered by SCS.)
700/MATH 685 Numerical Methods (3:3:0). Prerequisites: MATH 214, 203, and some
programming experience. Covers computational techniques for the solution of problems
arising in science and engineering. Algorithms are developed for the treatment of typical
problems in applications, with special emphasis on the type of data encountered in
practice. The course covers theoretical development, as well as implementation,
efficiency, and accuracy issues in using algorithms and interpreting the results. When
applicable, computer graphical techniques are used to enhance interpretation of results.
709 Topics in Computational Sciences and Informatics (3:3:0). Prerequisites:
Admission to Ph.D. program and permission of instructor. Covers selected topics in
computational sciences and informatics not covered in fixed-content computational
sciences and informatics courses. May be repeated for credit as needed.
711/CHEM 633 Chemical Thermodynamics and Kinetics (3:3:0). Prerequisites:
CHEM 331 and 332. Advanced study of thermodynamics and kinetics. Covers
application of kinetics to the elucidation of reaction mechanisms, and application of
statistical thermodynamics to the theory of elementary reaction rates.
721 Computational Fluid Dynamics I (3:3:0). Prerequisites: Course in partial
differential equations such as MATH 678 or equivalent, knowledge of linear algebra at
the level of MATH 603 or CSI 740/MATH 625, coding experience in FORTRAN or C;
or permission of instructor. Covers fundamentals of computational fluid dynamics,
including spatial and temporal approximation techniques for partial differential equations,
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solution of large systems of equations, data structures, solvers of the Laplace/full
potential equation, and simple Euler solvers. Two major projects are included: a Laplace
solver and a 2-D Euler solver on unstructured grids. Students are expected to write their
own codes.
722 Computational Fluid Dynamics II (3:3:0). Prerequisite: CSI 721 or permission of
instructor. Covers some of the more advanced topics in computational fluid dynamics,
including high-resolution schemes for hyperbolic PDEs, advanced Euler solvers, NavierStokes solvers, grid generation, adaptive mesh refinement, efficient use of
supercomputing hardware, and future trends. Projects include topics in grid generation
and adaptive refinement. Students are expected to write their own codes.
730 Biological Sequence Analysis (3:3:0). Prerequisites: Competency in programming
at the level of CSI 601-607, familiarity with molecular biology and cell biology at the
level of CSI 631, or permission of instructor. Covers fundamental methods for the
analysis of nucleic acid and protein sequences, including pairwise alignment, multiple
alignment, database search methods, profile searches, and phylogenetic inference. Also
covers probabilistic tools, including hidden Markov models and their associated
optimization algorithms. Provides survey and analysis of current software tools.
731 Protein Structure Analysis (3:3:0). Prerequisites: Course work in molecular
biology, biochemistry, and introductory computer programming, or permission of
instructor. A survey of the computational methods for the analysis, classification, and
prediction of three-dimensional protein structures. Covers theoretical approaches,
techniques, and computational tools for protein structure analysis. Topics include protein
geometry and topology, three-dimensional structure databases, protein modeling, and
engineering.
732 Genomics (3:3:0). Prerequisites: General biology, programming experience, CSI
700 or equivalent, CSI 730, or permission of instructor. A survey of computational tools
and techniques used to study whole genomes. Explores the biological basis of genome
analysis algorithms. Lecture topics include genome mapping, comparative genomics, and
functional genomics.
734 Computational Neurobiology (3:3:0). Prerequisites: CSI 651 or equivalent and
ordinary differential equations, or permission of instructor. Intense review of
neurobiology for graduate students interested in studying how nerve cells integrate and
transmit signals, and how behavior emerges from the integrated actions of populations or
circuits of nerve cells. Covers electrical and biochemical properties of single neurons, and
electrical and chemical communication between neurons. Emphasis is on mathematical
descriptions and computational techniques used to study and understand neurons and
networks of neurons.
735 Computational Neuroscience Systems (3:3:0). Prerequisites: CSI 734 (previously
or concurrently), CSI 650, CSI 651, or permission of instructor. Overview of the nervous
system and biological neural networks. Includes learning and memory, sensory systems,
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and motor systems. Stresses design and application of computational models. Students
are required to propose and design a computational model that addresses some open issue
in neuroscience.
740/MATH 625 Numerical Linear Algebra (3:3:0). Prerequisites: MATH 203 and
some programming experience. Covers computational methods for matrix systems;
theory and development of numerical algorithms for the solution of linear systems of
equations, including direct and iterative methods; analysis of sensitivity of system to
computer round off; and solution of least squares problems using orthogonal matrices.
Also covers computation of eigenvalues and eigenvectors, singular value decomposition,
and applications.
744 Linear and Nonlinear Modeling in the Natural Sciences (3:3:0). Prerequisite:
Permission of instructor. Develops the tools of mathematical modeling while carrying out
numerical simulations of the models. Examples from across the sciences are considered
throughout the course. Topics include basic issues (models, simplification, linearity, and
nonlinearity), dimensionless parameters, dimensional analysis, models involving
differential equations, examples from population growth and chemical kinetics, models
involving partial differential equations, diffusion, transport, nonlinearity and shocks,
probabilistic modeling, perturbation methods, extrapolation, and an introduction to
stability.
746 Wavelet Theory (3:3:0). Prerequisites: Knowledge of convolution and Fourier
transforms of sequences; some familiarity with Hilbert space theory helpful but not
required; knowledge of a scientific programming language. Study of the theory and
computational aspects of wavelets and the wavelet transform. Emphasizes computational
aspects of wavelets, defining the Fast Wavelet Transform in one and two dimensions and
developing the appropriate numerical algorithms, then develops the theory of wavelet
bases on the real line, discussing multi-resolution analysis, splines, time-frequency
localization, and wavelet packets.
748/MATH 629 Symbolic Computation (3:3:0). Prerequisites: Undergraduate degree in
a scientific discipline, and a course in abstract algebra. Provides the mathematical and
computational background for computational algebraic geometry and its applications.
Includes notions of algebra, geometry, algorithms, the concept of Groebner bases,
automatic theorem proving, and serial and parallel algorithms and their complexity.
Topics are related to applications in engineering and computer science. Students are
expected to complete projects.
754 Earth Observing/Remote Sensing Data and Data Systems (3:3:0). Prerequisites:
CSI 753 or permission of instructor. Covers how to access and apply Earth
observations/remote sensing data for Earth system science research and applications.
Major topics are data formats, analysis and visualization tools, and data applications. The
course covers combining innovative information technology techniques and Earth science
data to set up online data centers for web users to be able to access data through the web.
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758 Visualization and Modeling of Complex Systems (3:3:0). Prerequisite: Permission
of instructor. Covers elements of modeling and analysis of Earth and space sciences data
and systems. Concentrates on both sample projects and student-initiated projects as a
means of using visualization and graphical analysis techniques as they apply to the
modeling of complex data sets and systems. Several different analysis and visualization
packages are used. Spacecraft data sets from the Naval Research Laboratory (NRL)
Backgrounds Data Center and other NRL data sets are available for course projects. A
perusal of data sets from the World Wide Web is also possible. Modeling and analysis are
accompanied by appropriate readings from the current literature.
761/ASTR 761 N-Body Methods and Particle Simulations (3:3:0). Prerequisites:
PHYS 613/CSI 780 and CSI 700 or permission of instructor. Covers particle methods as
a tool in solving a variety of physical systems. Emphasizes the study and development of
the numerical results and visualization of these results in complex physical systems.
Applications and projects include stellar and galaxy dynamics, smoothed particle
hydrodynamics, plasma simulations, and semiconductor device theory algorithms on
parallel and vectorized systems.
771/STAT 751 Computational Statistics (3:3:0). Prerequisites: STAT 544, 554, and
652. Covers the basic computationally intensive statistical methods and related methods,
which would not be feasible without modern computational resources. Covers
nonparametric density estimation including kernel methods, orthogonal series methods
and multivariate methods, recursive methods, cross-validation, nonparametric regression,
penalized smoothing splines, the jackknife and bootstrapping, computational aspects of
exploratory methods including the grand tour, projection pursuit, alternating conditional
expectations, and inverse regression methods.
773/STAT 663 Statistical Graphics and Data Exploration (3:3:0). Prerequisite: 300level course in statistics; STAT 554 strongly recommended. Exploratory data analysis
provides a reliable alternative to classical statistical techniques, which are designed to be
the best possible when stringent assumptions apply. Topics include graphical techniques
such as scatter plots, box plots, parallel coordinate plots, and other graphical devices; reexpression and transformation of data; influence and leverage; and dimensionality
reduction methods such as projection pursuit.
780/PHYS 613 Computational Physics and Applications (3:3:0). Prerequisites: PHYS
510; FORTRAN, C, or C++ programming; or permission of instructor. PHYS 502 or
equivalent recommended. Study of diverse physical systems with emphasis on modeling
and simulation. Development of numerical algorithms and application of numerical
methods to gain understanding of the mechanisms and processes taking place in the
physical system. Several projects are undertaken, which are drawn from such areas as
atomic and molecular interactions, molecular dynamics, quantum systems, chaos,
percolation, random walks, and aggregation mechanisms.
786 Molecular Dynamics Modeling (3:3:0). Prerequisite: PHYS 613/CSI 780 or CHEM
633/CSI 711, or permission of instructor. Introduction to simulation methods used in the
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physical chemistry sciences. Covers computational approaches to modeling molecular
and condensed matter systems, including interatomic and molecular potentials, molecular
dynamics, time averages, ensemble distributions, numerical sampling, thermodynamic
functions, response theory, transport coefficients, and dynamic structure. Includes
stochastic simulations such as Brownian motion, Langevin dynamics, Monte Carlo
methods and random walks, and an introduction to cellular automata.
787 Computational Materials Science (3:3:0). Prerequisites: PHYS 512/CSI 687 and
PHYS 736/CSI 783, or permission of instructor. Covers selected topics in the
computational aspects of condensed matter, such as methods of electronic structure
calculations, surface science, molecular clusters, lattice dynamics, nanomaterials,
semiconductors, superconductivity, quantum Hall effect, magnetism, Hubbard model,
mesoscopic systems, and liquids.
788/PHYS 728 Simulation of Large-Scale Physical Systems (3:3:0). Prerequisites:
PHYS 613/CSI 780 and CSI 700, or permission of instructor. Study of diverse large-scale
physical systems, with emphasis on the modeling and simulation of these multifaceted
systems. Several projects are undertaken, which are drawn from such areas as many-body
dynamics, atmospheric structure and dynamics, high-temperature plasmas, stellar
structure, hydrodynamical systems, galactic structure and interactions, and cosmology.
801 Foundations of Computational Science (3:3:0). Note: This course will be
renumbered to CSI 701. Prerequisites: Competency in UNIX and programming at the
level of CSI 601-604, CSI 700, or permission of instructor. Covers the mapping of
mathematical models to computer software, including all aspects of the development of
scientific software, such as architecture, data structures, advanced numerical algorithms,
languages, documentation, optimization, validation, verification, and software reuse.
Examples in bioinformatics, computational biology, computational physics, and global
change demonstrate scientific advances enabled by computation. Class projects involve
working in teams to develop software that implements mathematical models, using the
software to address important scientific questions, and conducting computational
experiments with it.
803/IT 875 Scientific and Statistical Visualization (3:3:0). Note: This course will be
renumbered to CSI 703. Prerequisite: STAT 554 or CS 651 or permission of instructor.
Covers visualization methods used to provide new insights and intuition concerning
measurements of natural phenomena and scientific and mathematical models. Presents
case study examples from a variety of disciplines to illustrate what can be done. Topics
include human perception and cognition, an introduction to the graphics laboratory,
elements of graphing data, representation of space-time and vector variables,
representation of 3-D and higher dimensional data, dynamic graphical methods, and
virtual reality. Students are required to work on a visualization project. Software tools on
the Silicon Graphics workstation are emphasized, but other workstations and software
may be used for the project.
810 Scientific Databases (3:3:0). Note: This course will be renumbered to CSI 710.
20
Prerequisite: INFS 614 or equivalent, or permission of instructor. Study of database
support for scientific data management. Covers requirements and properties of scientific
databases, data models for statistical and scientific databases, semantic and objectoriented modeling of application domains, statistical database query languages and query
optimization, advanced logic query languages, and case studies such as the human
genome project and Earth-orbiting satellite.
898 Research Colloquium in Computational Sciences and Informatics (1:1:0).
Presentations in specific research areas in computational sciences and informatics by
School of Computational faculty and staff members, and professional visitors. May be
repeated for credit; however, a maximum of three credits of CSI 898, 899, and 991 may
be applied toward the Ph.D.
899 Colloquium in Computational Sciences and Informatics (1:1:0). Presentations in
a variety of areas of computational sciences and informatics by School of Computational
Sciences faculty and staff members, and professional visitors. May be repeated for credit;
however, a maximum of three credits of CSI 898, 899, and 991 may be applied toward
the Ph.D.
991 Seminar in Scientific Computing (1:1:0). Considers selected topics in a specific
area of computational sciences and informatics either not covered in fixed-content
courses or as an extension of fixed-content courses. Format for presentation is that of a
seminar with student participation. May be repeated for credit; however, a maximum of
three credits of CSI 898, 899, and 991 may be applied toward the Ph.D.
MATH 686 Numerical Solutions of Differential Equations (3:3:0). Prerequisites:
MATH 446 or 685 and an elementary differential equations course. Finite difference
methods for initial value problems, two-point boundary value problems, Poisson
equation, heat equation, and first-order partial differential equations.
INFS 614 Database Management (3:3:0). Prerequisites: INFS 501, 515 and 590; or
equivalent. Introduction to database systems, emphasing the study of database models
and languages and the practice of database design and programming. Topics include the
Entity-Relationship model, the relational model and its formal query languages, SQL, the
theory of relational database design, and object-oriented and logic-based databases.
Computing lab is required.
CS 635 Foundations of Parallel Computation (3:3:0). Prerequisites: CS 583 and 540 or
571, or equivalent. Survey of the field of parallel computation. Three major parallel
computing paradigms (MIMD computation, SIMD computation, and data flow
computation) are covered. Emphasis is placed on the interfaces between algorithm design
and implementation, architecture, and software. Parallel algorithms and parallel
programming languages are examined relative to the architecture of particular parallel
computers.
*****
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