1.
Background:
UCLA有独立的统计学院，不论是综合排名还是专业排名都很靠前，主要方向是应用统计
学。
GaTech没有独立的统计学院，项目由 Schools of Mathematics (Math) 和 Industrial and Systems
Engineering (ISyE) 共同管理，学生选择灵活，既可以选择偏向应用性质的课程，也可以选择
数学系的课程为 PHD 做准备。但其无论是综合排名还是专业排名都不如 UCLA 。
UCSD统计学设在数学系下，分为 Mathematical Statistics 和 Applied Statistics 两个方向，但
两个方向都有具体的数学要求，所以感觉在理论方面要求还是比较高的。
从各学校项目的背景来看， UCLA 是较理想的选择。
2.
Location:
UCLA-Los Angeles
GaTech-Atlanta
UCSD-San Diego
很明显， UCLA 的地理位置相对优越，能为就业提供比较理想的环境。
3.
师资力量
UCLA

Bentler, Peter — Distinguished Professor, 310-825-2893
 Multivariate analysis, with special emphasis on latent variable models.

Berk, Richard — Professor Emeritus,Applied statistics, statistical learning, evaluation of complex computer models

Braverman, Amy — Adjunct Associate Professor, 818-793-4606Massive data set analysis, data fusion, statistical methods for diagnosing climate models.
 Christou, Nicolas — Lecturer, 310-206-4420 Spatial statistics, Spatial regression 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 Mathematical and statistical modeling, brain mapping, decision theory, wavelet analysis, computational techniques.

Esfandiari, Mahtash — Continuing Senior Lecturer, 310-825-2732 Teaching of statistics,
Evaluation of interventions, Experimental design, 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 Information theory, nonparametric methods, media design.

Jennrich, Robert — Professor Emeritus, 310-825-2207 Statistical computing, design, nonlinear regression.
 Lew, Vivian — Continuing Lecturer, 310-206-6474 Statistical packages, Government and
Business statistics.
 Li, Ker-Chau — Distinguished Professor, 310-825-4897 Dimension reduction, data visualization, time series, images, and gene expression.
 Paik Schoenberg, Rick — Professor and Vice Chair for External Affairs, 310-794-5193

Point processes, Image analysis, Time series, and applications especially in seismology and fire ecology
 Port, Sidney — Professor Emeritus, 310-825-2207 Probability theory, Limit theorems,
Markov processes, Potential theory, Applied Statistics, Applications of Mathematics to
Medicine

Sabatti, Chiara — Professor, 310-206-6450 Bayesian statistics, Markov chains, Monte Carlo methods, Statistical genetics
 Sanchez, Juana — Continuing Lecturer, 310-825-1318 Statistics education, time series, 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

Yuille, Alan — Professor, 310-267-5383 Computer Vision, Bayesian Statistics, Pattern
Recognition

Zhou, Qing — Assistant Professor, 310-794-7563 Computational biology, Monte Carlo methods, and Bayesian statistics

Zhu, Song Chun — Professor, 310-206-8693 Computer Vision, Bayesian Statistics, Pattern
Recognition
GaTech
 Kobi Abayomi - Statistics/Measures of Association and Dependence, (Fixed Marginal)
Multivariate Distributions, Environmental Statistics, Environmental Economics.

Nagi Gebraeel - Sensor-Based Prognostics, Degradation Modeling, and Engineering
Reliability

Dave Goldsman - Comparisons via stochastic simulation; Statistical ranking and selection
 Xiaoming Huo - Multiscale statistical methods, data mining.
 Paul Kvam - Reliability; Applied engineering statistics; Nonparametric estimation

JC Lu - Statistics for manufacturing; Reliability; Degradation modeling
 Yajun Mei - Change-Point problems, sequential analysis, sensor networks, biostatistics

Nicoleta Serban - Analysis of multiple curves and analysis of multiple peaks
 Alex Shapiro - Mathematical programming and statistics; Sensitivity analysis
 Kwok Tsui - Statistical quality control including SPC, design of experiments, Taguchi
Method
 Roshan Joseph Vengazhiyil - robust parameter design, process control

Brani Vidakovic - Multiscale methods, statistical methods in geophysics, turbulence, bayesian decision theory
 Jeff Wu - Design and analysis of experiments, quality engineering, product/process improvement, bioinformatics
 MingYuan - Statistical learning, bioinformatics, methods of regularization
UCSD
 Ian Abramson ， Professor ， Undergraduate Vice Chair Ph.D., University of California,
Berkeley
 Ery Arias-Castro ， Assistant Professor ， Applied Probability, Statistics, Machine Learning ，

Ph.D., Stanford University
 Dimitris Politis ， ， ，
Stanford University ， Institute of Mathematical Statistics Fellow

Ronghui Xu ， Associate ProfessorAssociate Professor of Family and Preventive Medicine
High Dimensional Data Analysis, Random Effects Models, Survival Analysis, Clinical
Trials ， Ph.D., University of California, San Diego ， David P. Byar Young Investigator Award,
American Statistical Association
UCLA 师资雄厚，教师们的学术背景和所交内容很贴近商业领域的应用统计学。 GaTech 的
老师们来自两个不同的系，都侧重各自的专业特点，在统计方面的教学可能不会像 UCLA
那么 specific 。 UCSD 由于项目在数学系下，统计专业的老师不多，并且最牛的那几个老师
还是做 biostatistics 的，因此不大符合那位同学的要求。
4.
课程设置
UCLA
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 Optimizationan (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)
C248. Applied Sampling (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)
GaTech
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
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
UCSD
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.
从课程要求来看， UCLA 设有 Statistical Models in Finance 等这样极贴近商业运作的统计学
课程，而其他两所则没有这样比较职业导向的课程设置，另外， UCSD 对数学要求比较高。
综上所述， UCLA 以其良好的声誉，优越的地理位置以及比较务实的专业定位和课程设置，
应该能够成为该同学的 target school 。