US News & World Report: ISyE Remains Number One

Action: School Research by 杜博超
1. 你是个想申请 Statistics Master 的同学。 你的目标是想在硕士阶段学些更偏应用的统计
学知识(例如: computational technology, applied statistics), 然后将来进入商界做些工作。
例如 data analysis , marketing analysis.......
请按照你的目标将下列三个学校进行优先级排序,并给出原因,完成一份 report.
(提示:教授 focus, 课程设置,院系风格,就业…)
Why Statistics?
The world is becoming more and more quantitative. Many professions depend on
numerical measurements to make decisions in the face of uncertainty. Statisticians use quantitative
abilities, statistical knowledge, and communication skills to work on many challenging problems.
Want to Learn More?
The Department of Statistics at UCLA coordinates undergraduate and graduate statistics teaching and
research within the College of Letters and Sciences. We teach a large number of undergraduates and we
have a substantial graduate program. Our research and teaching have a strong emphasis on
computational and applied statistics. We have an active consulting center for both on-campus and
off-campus clients.
You can find the Department’s catalog on-line. We offer nearly one hundred courses; an undergraduate
B.S. degree and Minor; and M.S. and Ph.D. degrees in Statistics.
Statistics Scope and Objectives
With the advent of fast computing and the subsequent flood of data detailing almost every aspect of our
daily lives comes an urgent need for scientists trained in modern statistical methodologies.
Both the undergraduate and graduate programs are structured around three core course sequences
that introduce students to the science of data: theoretical statistics, data analysis, and statistical
computing. This balance reflects the scale and complexity of problems that statisticians are now
routinely called to address. Additional course offerings reflect the work of faculty members in
bioinformatics, sensor networks, environmental studies, finance, and computer vision.
Reflecting diverse research interests, the Statistics Department is organized around several centers
that collectively provide undergraduate and graduate students rich opportunities for specialized study.
These include the Center for Environmental Statistics, Center for Image and Vision Sciences, Center
for Statistical Computing, Center for Statistical Research in Computational Biology, and Center for the
Teaching of Statistics.
Center for Statistical Computing
The modern advent of enormous repositories of digital information presents us with
interesting new challenges. How can we represent and interpret such complex data? What are the best
algorithms and computing strategies to address important scientific and social questions?
Statistical Consulting Center
The Statistical Consulting Center of the UCLA Department of Statistics has the
dual purpose of: 1) training graduate students in consulting work and real-life data analysis; and 2)
providing free statistical consulting and data analysis services to the Campus and the Community.
In addition we offer electronic consulting, and a large number of mini-courses in various topics in
statistics and statistical software.
Albert Gifi's Homepage
Celebrating the work of Albert Gifi and his many co-workers.
Jan de Leeuw
Center for Image and Vision Science
Statistical and computational theory underlying visual perception and learning.
Song Chun Zhu and Alan Yuille
Center for the Teaching of Statistics
Research an projects related to the teaching of statistics at all levels.
Robert Gould
Center for Statistical Computing
Research in computationally intensive statistical problems.
Mark Hansen
Fire Hazard Estimation
Fire hazard estimation using point process methods.
Rick Paik Schoenberg
Gradient Projection Algorithms
Algorithms for rotation in Factor Analysis.
Coen Bernaards and Robert Jennrich
Cluster computing with Mac OS X on the PowerPC architecture.
Jan de Leeuw
High Performance Cluster Computing
Multivariate analyses with large datasets.
Vanessa Beddo and Coen Bernaards
Hyperemesis Gravidarum Survey and Information
Website devoted to hyperemesis gravidarum.
Rick Paik Schoenberg
Mathematical Principles for Visual Computation
Probability modeling and stochastic computing in vision.
Ying-Nian Wu
Statistical Analysis of Earthquake Occurrance Data
Statistical evaluation of earthquake occurrance data using point process techniques
Rick Paik Schoenberg
Studio of Bio-data Refining and Dimension Reduction
Bio-data Refining and dimension reduction research
Ker-chau Li
看过后,总感觉,UCLA 的统计学和视觉处理、地理、生物、以及统计学算法本身等联系比较密切,和商业
U.S. News & World Report: ISyE Remains Number One
"We are pleased to report that the H. Milton Stewart School of Industrial and Systems Engineering (ISyE)
is ranked first in industrial and manufacturing engineering in the annual U.S. News & World Report Best
Colleges issue and U.S. News & World Report Best Graduate School guide," said Chelsea C. White III,
ISyE School Chair. "Although we are aware of the issues surrounding such rankings, we take pride in the
fact that our hard work and dedication to excellence in education and research continues to be
recognized by our peers."
The Master of Science in Statistics program is administered by the Schools of Mathematics (Math) and
Industrial and Systems Engineering (ISyE). The main body of courses for the M.S. in Statistics degree are taken
in Math and in ISyE. Choices of the remaining courses in the program are quite flexible; students in the
program can concentrate their studies on a specific area of application such as Operations Research,
Psychology, Mechanical Engineering, etc., or, in preparation for the Ph.D., can take more mathematical courses.
The M.S. degree in Statistics is awarded upon successful completion of the courses in the program as described
below according to the stipulations of the Institute catalog. Electives are to be chosen in consultation with a
faculty member. A plan of offerings and catalog descriptions of courses in mathematics is available as are more
details, including catalog descriptions, about all courses offered the coming semester.
Core (12 hrs.) + Advanced (6 hrs.) + Methods (6 hrs.) + Statistics Electives (3 hrs.) + Free Electives (3 hrs.) = 30
hours total
Note: All the courses listed below are 3-hour courses.
Master of Science in Statistics (MS Stat)
CORE (12 hrs required)
Math 4261 Mathematical Statistics I
Math 4262 Mathematical Statistics II
ISyE 6414 Statistical Modeling and Regression Analysis
ISyE 7400 Advanced Design of Experiments or ISyE 7401 Advanced Statistical Modeling
THEORY/ADVANCED (select 6 hrs)
ISyE 7441 Theory of Linear Models
Math 6262 Statistical Estimation
Math 6263 Testing Statistical Hypotheses
Math 4317 Real Analysis
ISyE 7405 Multivariate Data Analysis
ISyE 6761 Stochastic Processes I
ISyE 6762 Stochastic Processes II
ISyE 6781 Reliability Theory
METHODS (select 6 hrs)
ISyE 6404 Nonparametric Data Analysis
ISyE 6405 Statistical Methods for Manufacturing Design and Improvement
ISyE 6805 Reliability Engineering
ISyE 6402 Time-Series Analysis
ISyE 6413 Design and Analysis of Experiments
ISyE 6650 Probabilistic Models and Their Applications
ISyE 6644 Simulation
ISyE 6656 Queueing Theory and Applications
The Industrial and Technology Statistics Center was formed in 1994 to aid in providing statistical expertise to
the Georgia Tech community in the areas of education, research, and operations. The Center has assisted
graduate students and faculty from various schools in the College of Engineering, Ivan Allen College, and the
DuPree College of Management with data analysis, design of experiments, and statistical model building.
Application areas are extremely diverse. The Center has also responded to requests from Georgia Tech
administrators for assistance in the analysis of data and in the development of applied statistics programs to
meet the needs of various engineering disciplines. In addition, aid in developing systems to monitor
research reporting performance and developing information technology services are provided
The Industrial and Technology Statistics Center also maintains a library of research texts, reference books,
periodicals (including Technometrics, Journal of the American Statistical Association, Annals of Statistics, and
Journal of Quality Technology), and statistical software.
2. 设在数学学院旗下的统计硕士:
MS Statistics Affiliated Faculty
Georgia Tech's statistics program emphasizes applications for engineering and the physical sciences.
Current research interests of the faculty are listed below:
Dave Goldsman - Comparisons via stochastic simulation; Statistical ranking and
selection (Professor, Ph. D., Cornell University)
Serge Guillas - Functional data analysis; Nonparametric statistics; Time series;
Environmental statistics; Spatial statistics (Assistant Professor, Ph. D., University Paris
Tony Hayter - Multiple comparison and selection procedures; Engineering
statistics (Associate Professor, Ph.D., Cornell University)
Russ Heikes - Statistical control procedures; Design of experiments; Statistical
model building (Professor Emeritus, Ph.D., Texas Technological University)
Christian Houdré - Nonparametric statistics; Statistical methods in finance and
bioinformatics (Professor, Ph.D., McGill University)
Xiaoming Huo - Multiscale statistical methods, Data mining(Assistant Professor,
Ph.D., Stanford University)
Vladimir Koltchinskii - Probability theory; mathematical statistics (Professor, Ph.D.,
Kiev University)
Paul Kvam - Reliability; Applied engineering statistics; Nonparametric estimation
(Associate Professor, Ph.D., University of California at Davis)
J. C. Lu - Statistics for manufacturing; Reliability; Degradation modeling (Professor,
Ph.D., University of Wisconsin)
Liang Peng - Limit theorems; Extreme value theory and its applications; Boundary
estimation; Heavy tailed and long-range dependent time series; Smoothed distribution
and quantile estimations; Edgeworth expansions; Empirical likelihood methods
(Assistant Professor, Ph.D., Erasmus University)
Alex Shapiro - Mathematical programming and statistics; Sensitivity analysis
(Professor, Ph.D., Ben-Gurion University of Negev)
Carl Spruill - Mathematical statistics and probability (Professor Emeritus, Ph.D.,
Purdue University)
Yung Tong - Mathematical statistics; Multivariate statistical analysis; Reliability
theory; Stochastic inequalities; Operations research and engineering statistics; Multiple
decision problems; Statistical computing (Professor Emeritus, Ph.D., University of
Brani Vidakovic - Multiscale methods; Statistical methods in geophysics;
Turbulence; Bayesian decision theory (Professor, Ph.D. Purdue University)
Jeff Wu - Design and analysis of experiments; Quality engineering;
Product/process improvement; Bioinformatics (Ph.D. University of California - Berkeley)
从教授的研究方向可以看到 GATECH 数学系的统计还是以生产中的统计学以及统计学理论
MS in Quantitative and Computational Finance
The main objective of the Master of Science degree program in Quantitative and Computational Finance
at Georgia Tech is to provide students with the practical skills and theoretical understanding they need to
be leaders in the formulation, implementation and evaluation of the models used by the financial sector to
structure transactions, manage risk and construct investment strategies. QCF Link
Introduction to Quantitative and Computational
Quantitative and Computational Finance (QCF) is a field with enormous impact, excellent employment
opportunities, and tremendous growth. This field forms an ever-expanding part of the financial sector,
present in numerous ways today.
Today, the principles of finance are being combined with advanced mathematical structures to form
useful financial products, strategies and models that are tested and implemented with the use of
advanced quantitative techniques. Use of computer technology is pervasive throughout the entire
process. These financial products, strategies and models are an integral part of the overall financial
activity in several areas; in addition to the basic modeling and forecasting of the underlying financial
markets are the areas involving
financial instrument development and usage: corporations and financial institutions use
an expanding variety of standard and complex financial instruments to structure their
transactions in ways that manage risk and assure performance, to increase their total
and net earnings, and to generate capital for growth and development;
investment: fund managers and investment analysts use sophisticated strategies and
techniques to ensure desired income streams and to increase returns; and
risk analysis: financial institutions seek to manage risks associated with extreme events,
defaults, liquidity constraints and operational factors.
Quantitative and Computational Finance is an area referred to under a variety of names, for example,
'computational finance', 'financial engineering', 'mathematical finance' and 'financial mathematics'. But in
all cases there is an effort that involves 'financial', 'mathematical', 'quantitative' and 'computational'
thinking to build, test and implement models that are at the center of these financial activities.
MS in Statistics
The School of Mathematics in the College of Sciences at Georgia Institute of Technology and the School
of Industrial and Systems Engineering in the College of Engineering offer graduate work leading to the
Master of Science in Statistics. The emphasis in this cooperative program is on statistics as a science
applicable in a technological environment. Although this program can lead to further work toward a
doctorate in applied statistics, mathematics (specialization in statistics), and/or bioinformatics, it is
designed to provide the background for success in a professional career in statistics.
Career fields for graduates of this program may be found in all areas of research, industry, and
government. The program, which can be completed in twelve months, is designed to provide the
graduate with competence in the collection, analysis, and interpretation of data and a sound
understanding of statistical principles. Students work with faculty actively engaged in research and
prepared to teach the latest developments in statistics. Those interested in statistics holding or
anticipating an undergraduate degree in engineering, mathematics, science, or some other field that
indicates a likelihood of successful completion of the program are encouraged to apply.
Affiliated Faculty
The Master of Science in Statistics program is administered by the Schools of Mathematics (Math) and
Industrial and Systems Engineering (ISyE). The main body of courses for the M.S. in Statistics degree
are taken in Math and in ISyE. Choices of the remaining courses in the program are quite flexible;
students in the program can concentrate their studies on a specific area of application such as
Operations Research, Psychology, Mechanical Engineering, etc., or, in preparation for the Ph.D., can
take more mathematical courses. The M. S. degree in Statistics is awarded upon successful completion
of the courses in the program as described below according to the stipulations of the Institute catalog.
Electives are to be chosen in consultation with a faculty member. Plans of graduate and undergraduate
offerings are available as are more details, including catalog descriptions, about all courses offered the
coming semester.
UCSD 没有单独的统计系,是在数学学院旗下的。
The M.S. in Statistics is designed to provide recipients with a strong mathematical background
and experience in statistical computing with various applications. Out of the 48 units of credit
needed, required core courses comprise 28 units, including:
Mathematics 281A-B-C (Mathematical Statistics)
Mathematics 282A-B (Applied Statistics)
and any two topics comprising eight (8) units chosen freely from Mathematics 287A-B-C-D and
289A-B-C (see course descriptions for topics).
The following guidelines should be followed when selecting courses to complete the remaining
20 units:
For a theoretical emphasis, Mathematics 280A-B-C (Probability Theory) is
For an applied orientation, Mathematics 270A-B-C (Numerical Mathematics) is
Upon special approval of the faculty adviser, the rule above, limiting graduate units from other
departments to 8, may be relaxed in making up these 20 non-core units.
Comprehensive Examinations: Two written comprehensive examinations must be passed at
the Master's level in related course work (approved by a faculty adviser). Instructors of the
relevant courses should be consulted for exam dates as they vary on a yearly basis.
Any Master's student can submit for consideration a written request to transfer into the Ph.D.
program when the qualifying exam requirements for the Ph.D. program have been met and a
dissertation adviser is found (refer to "Ph.D. in Mathematics" for complete description of
the departmental examinations). Approval by the Qualifying Exam and Appeals Committee
(QEAC) is not automatic, however.
Research Area: Statistics
Ph.D., University of California, Berkeley
Ian Abramson
Undergraduate Vice Chair
Ery Arias-Castro
Assistant Professor
Ph.D., Stanford University
Time Series Analysis, Bootstrap Methods,
Dimitris Politis
Ph.D., Stanford University
Institute of Mathematical Statistics Fellow
High Dimensional Data Analysis, Random
Ronghui Xu
Effects Models, Survival Analysis, Clinical
Associate Professor
Associate Professor of Family Ph.D., University of California, San Diego
and Preventive Medicine
American Statistical Association
UCSD 的统计方向着实感觉内容比较少。。。
自己的 school research 结果及点评:
综合以上要素,我的学校排序是:GIT> UCLA > UCSD。
GIT 除了数学系的 QCF 项目对于立志从事金融行业的人来说是很好的项目,对于想从事实体行
业的统计学学生来说,与工业工程系合开的那个统计硕士项目含金量也是很高的,毕竟 GIT 的
工业工程一直排名全美第一。而且选择 GIT 的一大原因是 GIT 的学术盛名,作为几乎与 MIT,
Caltech 齐名的理工科牛校,来这里应该是首选。而且补充一点,地理位置上看,尽管其在东部
UCLA 的统计学总体与商业的关系不是很大,但是由于院系规模比 UCSD 要稍大,且排名与名
声都要强于 UCSD,所以把它放在第二名。UCSD 涉及统计的教授和研究人员过于少,导致确