UCSD
M.S. in Statistics
All student course programs must be approved by a faculty adviser prior to registering for
classes each quarter, as well as any changes throughout the quarter. (This program is
offered only under the Comprehensive Examination Plan)
48 units of course credit subject to adviser approval are needed. Up to 8 of them can be from
upper-division Mathematics. Up to 8 of them can be graduate courses in other departments.
Mathematics 295 and 500 generally don't count toward those 48 units, and neither do seminar
courses, unless the student's participation is substantial. Fulltime students are generally
required to register for at least 12 units per quarter.
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
recommended.

For an applied orientation, Mathematics 270A-B-C (Numerical Mathematics) is
recommended.
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.
Statistics
Ph.D., University of California, Berkeley
Ian Abramson
Professor
Undergraduate Vice Chair
Applied Probability, Statistics, Machine
Ery Arias-Castro
Learning
Assistant Professor
Ph.D., Stanford University
Time Series Analysis, Bootstrap Methods,
Dimitris Politis
Nonparametrics
Professor
Ph.D., Stanford University
Institute of Mathematical Statistics Fellow
High Dimensional Data Analysis, Random
Ronghui Xu
Effects Models, Survival Analysis, Clinical
Associate Professor
Trials
Associate Professor of Family
Ph.D., University of California, San Diego
and Preventive Medicine
David P. Byar Young Investigator Award,
American Statistical Association
Ery Arias-Castro
Assistant Professor of Mathematics
Academic Interests and Research
Image Processing and Detection of Geometric Objects by Fast Multiscale Methods and
techniques related to Statistical Mechanics.
Ronghui Xu
Associate Professor of Mathematics
Associate Professor of Family and Preventive Medicine
Academic Interests and Research

High dimensional data analysis (as applicable to -omics fields research)

Imaging data analysis

Random effects models (longitudinal and/or clustered data)

Survival analysis

Clinical trials
该专业设在数学系下，师资较少，且与商业关系不大，更偏重数学背景下的理
论研究。
UCLA
UCLA Department of Statistics
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.
The Department of Statistics offers Master of Science (M.S.) and Doctor of Philosophy
(Ph.D.) degrees in Statistics. The graduate program is 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. As with the
undergraduate program, the interest of faculty members in various application areas
weaves itself throughout the graduate offerings.
Courses and workshops for secondary school teachers of statistics are also offered in
order to promote sound statistics pedagogy throughout the curriculum.
Faculty Index
Bentler, Peter — Distinguished Professor,
310-825-2893
Multivariate analysis, with special
Berk, Richard — Professor Emeritus,
Applied statistics, statistical learning,
evaluation of complex computer models
emphasis on latent variable models.
Braverman, Amy — Adjunct Associate
Professor, 818-793-4606
Christou, Nicolas — Lecturer,
310-206-4420
Massive data set analysis, data fusion,
Spatial statistics, Spatial regression
statistical methods for diagnosing
models.
climate models.
Davis, Gretchen G. — Continuing Lecturer,
310-206-6450
Teaching of Statistics
de Leeuw, Jan — Distinguished Professor
and Chair, 310-825-9550
Data analysis, Multivariate analysis,
Computational statistics
Dinov, Ivo — Adjunct Associate Professor,
310-206-4330
Esfandiari, Mahtash — Continuing Senior
Lecturer, 310-825-2732
Mathematical and statistical modeling,
Teaching of statistics, Evaluation of
brain mapping, decision theory, wavelet
interventions, Experimental design,
analysis, computational techniques.
Testing and Measurement
Ferguson, Thomas — Professor Emeritus,
310-825-4898
Statistics, Game theory
Gould, Robert L. — Academic Administrator
and Undergraduate Vice-Chair, 310-206-3381
Education, repeated measures
analysis.
Hansen, Mark — Professor and Graduate
Vice-Chair, 310-206-8375
Jennrich, Robert — Professor Emeritus,
310-825-2207
Information theory, nonparametric
Statistical computing, design, nonlinear
methods, media design.
regression.
Lew, Vivian — Continuing Lecturer,
Li, Ker-Chau — Distinguished Professor,
310-206-6474
310-825-4897
Statistical packages, Government and
Dimension reduction, data visualization,
Business statistics.
time series, images, and gene
expression.
Paik Schoenberg, Rick — Professor and
Vice Chair for External Affairs, 310-794-5193
Port, Sidney — Professor Emeritus,
310-825-2207
Point processes, Image analysis, Time
Probability theory, Limit theorems,
series, and applications especially in
Markov processes, Potential theory,
seismology and fire ecology
Applied Statistics, Applications of
Mathematics to Medicine
Sabatti, Chiara — Professor, 310-206-6450
Bayesian statistics, Markov chains,
Sanchez, Juana — Continuing Lecturer,
310-825-1318
Monte Carlo methods, Statistical
Statistics education, time series,
genetics
analysis of internet traffic and www
data, bayesian statistics
Wu, Yingnian — Professor, 310-794-4860
Statistical modeling and computing
Xu, Hongquan — Associate Professor,
310-206-0035
Experimental design, bioinformatics,
data mining, computer experiments.
Ylvisaker, Donald — Professor Emeritus,
310-825-4819
Design theory, Applied statistics
Zhou, Qing — Assistant Professor,
310-794-7563
Yuille, Alan — Professor, 310-267-5383
Computer Vision, Bayesian Statistics,
Pattern Recognition
Zhu, Song Chun — Professor,
310-206-8693
Computational biology, Monte Carlo
Computer Vision, Bayesian Statistics,
methods, and Bayesian statistics
Pattern Recognition
Research
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
gSCAD/iSCAN
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
UCLA 与 UCSD 相比要更适合一点，至少 UCLA 得核心课程 theoretical statistics, data
analysis, and statistical computing 还与该同学感兴趣的 computational technology,
applied statistics 相关，包括它的核心课程也有 data analysis 的部分。
但是，还是觉得这学校跟商业没什么关系，貌似更偏重计算啊，视觉处理什么的相关研究。
GaTech
一个是数院下开的
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.
In addition to the listing below see also the ISyE listing.
Core:
12 hrs
Advanced:
6 hrs
Methods:
6 hrs
Statistics Electives: 3 hrs
Free Electives:
3 hrs
Total:
30 hrs
All below are 3 hour courses.
Core Courses
Math 4261 Mathematical Statistics I
Math 4262 Mathematical Statistics II
ISyE 6401 Statistical Modeling and Design of Experiments (or ISyE 6414)
ISyE 7400 Advanced Design of Experiments or
ISyE 7401 Advanced Statistical Modeling
Georgia Tech's statistics program emphasizes applications for engineering
and the physical sciences. Current research interests of the faculty are listed
below:
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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
VI)
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
Minnesota)
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)
还是以理论研究为主，不适合该同学的兴趣方向
一个是工程学院下开的
Welcome to the website of The H. Milton Stewart School of Industrial and
Systems Engineering at the Georgia Institute of Technology. With nearly
60 tenure-track faculty, ISyE is able to support not only a broad spectrum
of academic concentrations but, importantly, several that have achieved
world-class rank.
Though the Stewart School functions as a single cohesive unit, some of
our subdisciplines or academic specialties such as operations research,
statistics, manufacturing and logistics, and various ones identified with
the ACO Program, are so large and concentrated and so heavily represented,
they could be legitimately viewed as academic departments in their own
right.
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
TOTAL HOURS REQUIRED 30
Jye-Chyi (JC) Lu, Ph.D.Professor
Dr. Lu is very active in promoting research, education and extension-service programs with focus
on information systems engineering, e-business, e-logistics, e-design and industrial statistics areas.
He serves as an associate editor of IEEE Transactions on Reliability and as session organizer and
chair for conferences such as “Multiscale Methods in Biometry” for IMS/ENAR Spring 2001
meeting and “Reliability and Degradation Studies” for 2001 Spring Research Conference on
Statistics in Industry and Technology. In 1996, Dr. Lu received the David D. Mason award, and in
1998, he received an outstanding extension service award from NCSU.
Xiaoming Huo, Ph.D.Associate Professor
His research interests include statistics and multiscale methodology. He has made numerous
contributions on topics such as sparse representation, wavelets, and statistical problems in
detectability. His papers appeared in top journals, and some of them are highly cited. See his
publication list for details.
Yajun Mei, Ph.D.
Assistant Professor
Dr. Mei’s research interests include change-point problems and sequential analysis in
Mathematical Statistics; sensor networks and information theory in Engineering; as well as
longitudinal data analysis, random effects models, and clinical trials in Biostatistics.
GaTech 比 UCLA 更好一点，统计学系设在工程学院下，相比于前两个偏重实践多一点
中国大陆过去的留学生任教授到不少，申请是不是容易些？
还有一个相关的专业是数学学院和工业工程合开的
The Quantitative and Computational Finance program at
Georgia
Introduction to Quantitative and Computational Finance
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.
这个名目虽然不是统计，但与该同学的兴趣与就业方向最为契合。
总结：
UCSD 的该专业设在数学系下，师资较少，且与商业关系不大，更偏重数学背景下的理论
研究。
UCLA 与 UCSD 相比要更适合一点，至少 UCLA 得核心课程 theoretical statistics, data
analysis, and statistical computing 还与该同学感兴趣的 computational technology,
applied statistics 相关，包括它的核心课程也有 data analysis 的部分。
但是，还是觉得这学校跟商业没什么关系，貌似更偏重计算啊，视觉处理什么的相关研究。
GaTech 的统计分别在数院和工程学院下，前者更注重理论研究，后者相比而言与 data
analysis 之类的核心课程还有点相关。
有个最适合的是数院和工程学院合开的一个项目，Introduction to Quantitative and
Computational Finance
这个名目虽然不是统计，但与该同学的兴趣与就业方向最为契合。从他的专业设置及研究
方向看，与该同学的兴趣与未来职业规划非常契合，正是偏重商界的应用性人才。
所以，最适合的是 GaTech，然后是 UCLA，最后才是 UCSD