For Prospective Ph.D. Students
Statistics at UCLA has a long history, going back to the 1930’s including the formation of the Department of Biostatistics in Public Health and the Department of Biomathematics within the School of Medicine. In the College of Letters and Sciences, Statistics grew within the Department of Mathematics as the Probability and Statistics group and via the Social Statistics program within the Division of Social Sciences. Through the work of Jan de Leeuw, Don Ylvisaker, Tom Ferguson, Dick Berk, and others, the Department of Statistics was founded in 1998. Aided by the development of the electronic computer, the adoption of statistical methods has grown enormously. Statistical ideas are now the basis for advancing science and commerce through data.
Today the department combines the strengths of mathematical statistics and statistical theory with ideas from domain sciences to form a very modern statistics discipline. In it ideas from the computational sciences, machine learning and data sciences are expressed through our student programs and research.
The best place to get a sense of the program is on the Ph.D. Program page, which gives detailed description of the program. This is good to read before the visitation day. It describes sequentially the steps toward earning your doctorate. The focus of the first year is your core courses. Progressively you focus more on your research and doctoral dissertation, while still taking elective classes. A detailed description of the program is given in Ph.D. Program overview, that will be covered during the visitation day.
Faculty Research Interests
The tenure-track faculty are the research engine of the department. Work through the introduction at the bottom of the page to get a brief description of their research interests and introduction via videos. Please click on the video links and the websites to get to know the faculty and some of their interests. They are very broad both in terms of the application areas and the theoretical, modeling or computational statistical frameworks. Enjoy!
While research is driven by individual research interests the department also has centers combining one or more faculty. These fields of emphasis are: applied multivariate analysis; bioinformatics (Center for Statistical Research in Computational Biology); computational and computer-intensive statistics; computer vision; cognition; artificial intelligence; machine learning (Center for Vision, Cognition, Learning, and Autonomy); social statistics (Center for Social Statistics); experimental design and environmental statistics.
Campus and Living
To get a sense of the campus here at UCLA, take the Virtual Campus Tour. Note that the virtual tour focuses on undergraduate housing. For more information about graduate housing, please visit UCLA Housing and for a virtual tour of graduate housing please visit Graduate Housing.
9:00am to noon, March 18, 2022 fully online.
9:00am – 9:30am: Overview of program (Mark and Hongquan)
9:40am – 10:40am: Get-to-know your faculty. Breakout rooms each headed by a faculty member so you can introduce yourself and get to know them. You choose which rooms you enter and can move to other rooms.
10:45am-11:30am: Panel of current doctoral students (no faculty)
11:30am: Q&A session with Hongquan Xu, Chair of Statistics
1:00pm– Meet with the Graduate Vice-Chair (signup, 1 on 1)
Recording of Overview of Doctoral Program
Recording of 2021 Question and Answer Session
A Brief Introduction to the Faculty
Arash Ali Amini, Assistant Professor
High-dimensional inference, machine learning, optimization, networks
Guang Cheng, Professor
Trustworthy AI, Data-Centric AI, Deep Learning Theory and Statistical Machine Learning
Alyson (Allie) Fletcher, Associate Professor
Machine learning, Statistical inference for high-dimensional data with applications in neuroscience, signal processing, information theory
Robert Gould, Senior Lecturer SOE and Undergraduate Vice Chair
Statistics education and Modeling longitudinal data
Mark S. Handcock, Professor and Graduate Vice Chair
Stochastic modeling of social networks, Environmental and spatial statistics, Demography, Computational statistics, Survey sampling, and Epidemiology.
Chad Hazlett, Assistant Professor
Causal inference, high-dimensional regression and classification, applications in political science
Jingyi Jessica Li, Associate Professor
Applied Statistics and Statistical Modeling, as well as their interface with Statistical Genomics, Bioinformatics, and Computational Biology
Ker-Chau Li, Distinguished Professor
Dimension reduction, data visualization, time series, images, and gene expression
Oscar Madrid Padilla, Assistant Professor
High dimensional statistics, Network estimation problems, Change point detection, Bayesian statistics, Quantile regression, and Graphical models.
Karen McKinnon, Assistant Professor
Spatial and environmental statistics, predictive modeling, applications to climate science
Guido Montufar, Assistant Professor
Deep learning, artificial neural networks, information geometry, algebraic statistics
Rick Schoenberg, Professor and Director of MAS Program
Point processes, Image analysis, Time series, and applications especially in seismology and fire ecology
Qing Zhou, Professor
Computational biology, Statistical learning, Monte Carlo methods, Energy landscapes
Song-Chun Zhu, Professor
Computer Vision, Machine Learning, MCMC computing, Cognition, and Visual Arts
Chenlu Shi, Adjunct Assistant Professor
Experimental design, computer experiment, big data reduction
Alan Vazquez Alcocer, Adjunct Assistant Professor
Experimental design, model selection, metaheuristic optimization
There is information on more faculty in the faculty directory page