School Research for MS Stat
Ranking:
综合排名：
UCLA-25
Gatech-35
UCSD-37
Ranking 对比：
USNEWS 的统计专业排名为
1 Stanford University Stanford, CA
University of California--Berkeley Berkeley, CA
3 University of North Carolina--Chapel Hill Chapel Hill, NC
4 Harvard University Cambridge, MA
5 Iowa State University Ames, IA
6 Duke University Durham, NC
University of Chicago Chicago, IL
University of Washington Seattle, WA
9 University of Florida Gainesville, FL，
可以看到三所学校都不在其中。就综合排名来讲似乎 UCLA 更占优势，不过 GaTech 的理工
科在全美有很高的地位，所以猜想他家的 STAT 项目也很不错。
不过对于想回国就业的话，综合排名显然更重要，这三所学校严格意义上都不算 Top Tier，
所以对于国内的公司可能他们也没什么区分性。
Location:
UCSD – San Diego, CA
UCLA – Los Angeles, CA
Gatech – Atlanta, GA
Location 对比：
UCLA、UCSD 均在加州，一个在洛杉矶，一个在圣地亚哥；而 Gatech 则处在亚特兰大。这三
个城市在美国均为相对发达的城市，大公司也都比较多；作为没有特别 focus 的统计专业的
同学，我三个学校的 location 都还算不错。
Admission condition:
UCLA-statistics
Admissions Data
Average Number of Applicants
163
Average Number of Admits
50
Percent of Applicants Admitted
31%
Average Number of New Registrants
19
Percent of Admits Registered
38%
Applications, Admits, and New Registrants data are the averages for Fall 2004 - Fall 2008.
Percent of Applicants Admitted = number of admits divided by number of applications.
Percent of Admits Registered = number of new registrants divided by the number of admits.
Enrollment Data
Average Number of Registrants Fall Term
69
Percent Women
41%
Percent Underrepresented Minorities
6%
Percent International Students
38%
Percent with a Master's Degree Objective
40%
Percent with a Doctoral Degree Objective
59%
Average Number of Registrants Fall Term = Average number enrolled for Fall 2004 - Fall
2008. This is adjusted for those programs that have not been in existence for the entire five
years.
Underrepresented Minorities includes domestic students identified as American
Indian/Native American, African American/Black, Mexican American/Chicano, Latino/Other
Hispanic, and Filipino.
International Students are those on temporary visas. They include all students who are not
US citizens, Permanent Residents, Immigrants, or Refugees.
Combined Master's & Doctoral Degree Objective percentages may appear lower than 100%
if registrants are pursuing other degree objectives including JD, MD, or DDS degrees.
Degrees Awarded
Total Number of Master's Degrees Awarded
95
Average Time to Master's Degree (In Years)
2
Total Number of Doctoral Degrees Awarded
26
Average Time to Doctoral Degree (In Years)
5
Total masters and doctoral degree data are the number of degrees awarded from 2003-04
through 2007-08.
Average time-to-degree is calculated by counting and then aggregating the total number of
elapsed terms for students whom were in graduate standing under the specific program's
major code from point of entry through degree completion.
Faculty researches:
UCSD:
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Algebra
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Algebraic Geometry
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Bioinformatics
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Combinatorics
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Complex Variables
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Differential Equations

Functional Analysis / Operator Theory

Geometric Analysis

Geometry and Topology

Logic and Computational Complexity

Mathematical Physics

Mathematics Education

Number Theory

Numerical Analysis

Numerical PDE / Scientific Computation

Probability Theory

Representation Theory

Statistics
UCLA:
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
GaTech:
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)
Faculty 对比：
在这三所学校的 faculty research focus 里面，最偏重应用的明显是 GaTech；几乎所有教授的
研究方向都与应用统计，以及统计相关工作里最热门的金融、生物统计、数据挖掘、模拟有
关，而 GaTech MS Stat 的 program description 里就有说道他们想培养应用型的统计人才，这
与申请的目的也是一致的；UCLA 也有很多教授在研究应用统计相关，不过我发现比较牛的
professor 还是偏重在统计理论方面的；而 UCSD 比 UCLA 更偏理论一些。
从学习应用统计的角度来讲，GaTech 是比较好的选择。
Job Resources:
GaTech:
Job Opportunity Center for Graduate students of department of Mathematics, GaTech
Employees:
National Security Agency
IBM
Shell Oil Company
United States Olympic Committee
Scottrade, Inc.
Corporate Resource Group of Mid America
Mountbatten Institute
BMW Manufacturing Co., LLC
UCLA:
UCLA 的 Alumni 档案很不全，基本都没有 current employment，猜测可能这部分的 open
information 不够多吧
UCSD:
UCSD Mathematics : Internship Resources
Career Services: The Career Services Center's Internship
Supersite can help with career exploration, resume development, interview skill building and
development of personal statements. They maintain an on-line listing of internships
through Port Triton. Contact CSC at 858/534-3750 for more information.
Academic Internship Program: The Academic Internship Program (AIP) features internships
for which you can earn college credit. AIP will handle paperwork for research projects on
campus. They will also help train you in how to write a successful resume. Call AIP at
534-4355 for more information.
Associated Students Internship Office: The Associated Students Internship Office (ASIO)
handles both volunteer and paid internships. It provides books listing past student internships
and evaluations of the internship sites. They provide mock interviews to prepare the student
for the real interview. ASIO is located in the Price Center and can be contacted at 534-4689.
Job Resources 对比：
从可以获得的信息来看，GaTech 和 UCSD 都有比较好的就业指导，GaTech 还专门为数学学
院的同学开设了一个就业网站，post 在上面的工作也都是很不错的、跟统计专业很挂钩的工
作，包括了咨询、数据分析、金融、财务等等，很适合不同 career goal 的学生。
而 UCLA 的网站关于就业情况信息很少，很多 Alumni 的联系方式都没有，如果没有内部的
一些非公开信息的话，UCLA 在就业资源的方面是比较弱的。
Graduate Courses:
UCSD:
GRADUATE COURSES (Fall 2009)
MATH 200A A00 Small, Lance
Algebra
MATH 201A A00 Zelmanov, Efim
Basic Topics in Algebra
MATH 202A A00 Gill, Philip
Applied Algebra I
MATH 203A A00 Oprea, Dragos
Algebraic Geometry
MATH 205
A00 Stark, Harold
Topics in Algebraic Number Theory
MATH 209
A00 Stark, Harold
Sem / Number Theory
MATH 210A 00
Li, Bo
Mathematical Methods of Physics & Engineering
MATH 220A A00 Ebenfelt, Peter
Complex Analysis
MATH 231A A00 Sterbenz, Jacob
Partial Differential Equations
MATH 240A A00 Okikiolu, Kate
Real Analysis
MATH 248
A00 Baouendi, Salah
Seminar in Real Analysis
MATH 250A A00 Weinkove, Ben
Differential Geometry
MATH 257A 00
Topics in Differerntial Geometry
Ni, Lei
MATH 261A A00 Remmel, Jeff
Probabilistic Combinatorics & Algorithms I
A00 Remmel, Jeff
Seminar in Combinatorics
MATH 270A A00 Bank, Randy
Numerical Linear Algebra
MATH 271A A00 Gill, Philip
Numerical Optimization
MATH 273A A00 Leok, Melvin
Applied Analysis for Computational Science I
MATH 269
MATH 274
A00 Cheng, Li-Tien
Numerical Methods for Physical Modeling
MATH 278A A00 Holst, Mike
Seminar in Computational Mathematics
MATH 278B A00 Holst, Mike
Seminar in Mathematical Physics/PDE
MATH 280A A00 Driver, Bruce
Probability Theory
MATH 281A A00 Xu, Lily
Mathematical Statistics
MATH 282A A00 Politis, Dimitris
Applied Statistics
MATH 286
A00 Schweinsberg, Jason Stochastic Differential Equations
MATH 288
A00 Driver, Bruce
Seminar in Probability & Statistics
B00 Abramson, Ian
MATH 290A A00 Roberts, Justin
Topology
MATH 292
A00 Roberts, Justin
Seminar in Topology
MATH 295
A00 Li, Bo
Special Topics in Mathematics
MATH 296
A00 Tesler, Glenn
Student Colloquium
UCLA:
Graduate Courses
200A. Applied Probability. (4)
200B. Theoretical Statistics. (4)
200C. Large Sample Theory, Including Resampling. (4)
201A. Research Design, Sampling, and Analysis. (4)
201B. Regression Analysis: Model Building, Fitting, and Criticism. (4)
201C. Advanced Modeling and Inference. (4)
202A. Statistics Programming. (4)
202B. Matrix Algebra and Optimization. (4)
202C. Monte Carlo Methods for Optimization. (4)
204. Nonparametric Function Estimation and Modeling. (4)
M211. Analysis of Data with Qualitative and Limited Dependent Variables. (4)
212. Program Evaluation and Policy Analysis. (4)
M213. Applied Event History Analysis. (4)
C216. Social Statistics. (4)
218. Generalized Linear Models. (4)
M221. Time-Series Analysis. (4)
M222. Spatial Statistics. (4)
C225. Experimental Design. (4)
C226. Bootstrap, Jackknife, and Resampling Methods. (4)
M230. Statistical Computing. (4)
M231. Pattern Recognition and Machine Learning. (4)
M232A. Statistical Modeling and Learning in Vision and Science. (4)
M232B. Statistical Computing and Inference in Vision and Image Science. (4)
233. Statistical Methods in Biomedical Imaging. (4)
234. Statistics and Information Theory. (4)
C235. Data Management. (4)
C236. Introduction to Bayesian Statistics. (4)
M237. Data and Media Arts. (4)
238. Vision as Bayesian Inference. (4)
239. Probabilistic Models of Cognition. (4)
240. Multivariate Analysis. (4)
M241. Causal Inference. (4)
M242. Multivariate Analysis with Latent Variables. (4)
M243. Logic, Causation, and Probability. (4)
M244. Statistical Analysis with Latent Variables. (4)
M245. History of Statistics. (4)
M250. Statistical Methods for Epidemiology. (4)
M251. Statistical Methods for Life Sciences. (4)
CM252. Statistical Methods for Physical Sciences. (4)
253. Statistical Methods for Ecology and Population Biology. (4)
M254. Statistical Methods in Computational Biology. (4)
257. Design, Analysis, and Modeling for Embedded Sensing. (4)
C260. Site-Specifics Topics. (4)
C261. Introduction to Pattern Recognition and Machine Learning. (4)
C273. Applied Geostatistics. (4)
C283. Statistical Models in Finance. (4)
285. Seminar: Computing for Statistics. (2 to 4)
M286. Seminar: Statistical Problem Solving for Population Biology. (2)
287. Seminar: Gene Expression and Systems Biology. (2)
290. Current Literature in Statistics. (2)
291. Statistics Consulting Seminar. (4)
292. Graduate Student Statistical Packages Seminar. (1 to 2)
293. Graduate Student Research Seminar. (2)
C294. Scientific Writing. (2)
C295. Fundamentals of Scientific Writing. (2)
296. Participating Seminar: Statistics. (1 to 2)
370. Teaching of Statistics. (4)
375. Teaching Apprentice Practicum. (1 to 4)
495A. Teaching College Statistics. (2)
495B. Teaching College Statistics. (2)
495C. Evaluation of Teaching Assistants. (2)
596. Directed Individual Study or Research. (2 to 8)
598. M.S. Thesis Research. (2 to 12)
599. Ph.D. Dissertation Research. (2 to 12)
GaTech:
Advanced Courses
ISyE 7441 Theory of Linear Models or
MATH 6266 Linear Statistical Models
MATH 6262 Statistical Estimation
MATH 6263 Testing Statistical Hypotheses
MATH 6267 Multivariate Statistical Analysis
Math/ISyE 6761 Stochastic Processes I
Math/ISyE 6762 Stochastic Processes II
Math/ISyE 6781 Reliability Theory
Methods Courses
ISyE 6402 Time Series
ISyE 6404 Nonparametric Data Analysis
ISyE 6405 Response Surfaces
ISyE 7400 Advanced Design of Experiments
ISyE 7401 Advanced Statistical Modeling
ISyE 7405 Multivariate Data Analysis (see also ISyE 6413 and 6805)
Electives (A non-comprehensive and brief list )
ISyE 6650 Probabilistic Models and their Applications
ISyE 6644 Simulation
ISyE 6656 Queueing Theory
Course 对比：
从三所学校的 Advanced Graduate Courses，可以明显看出，GaTech 的课程很偏重应用性，UCLA
提供了很多的课程供不同方向的同学选择，而 UCSD 理论性稍强。联系之前的 Faculty 情况，
GaTech 和 UCLA 应该更适合毕业后就工作的统计 master 同学。
总结：
通过上面关于 Location、Ranking、Faculty Research、Job Resource、Courses 几个方面的对比，
GaTech 在 location/ranking/financial aid 方面没有明显劣势，而在 Faculty/Job Resource/Courses
方面表现出了很适合希望毕业就工作的偏重应用统计的同学的特点，所以排序选择是
GaTech，UCLA，UCSD.