Current Members
Lab Director
Graduate Vice Chair
Prof of Statistics and Data Science
Prof of Biostatistics (Adjunct)
Univ of California, Los Angeles
.
Postdocs
Dr. Namjoon SuhDr. Chi-Hua Wang
Dr. Shirong Xu (co-advised by Prof. Wei Sun)
PhD Students
Joshua WardXiaofeng Lin: LLM, Foundation model, Synthetic data
Hengzhi He: Applied probability, Theoretical statistics and Theoretical computer science.
Peiyu Yu (co-advised by Prof. Yian Nian Wu)
Lan Tao
Yidong Ouyang: Diffusion model and its applications in images, language, videos, and 3D generation
Minrui Gui
Thomas Kwok: Adaptation of Foundation Models for Autonomous Agent Applications
Master Students
Junpeng Ren (Master of Statistics)Chenheng Xu (MASDS)
Jiaxin Yang (Master in Statistics)
Undergrads
Din-Yin Hsieh (Data Theory)Henry Liu (CS)
Jiacheng Wang (CS)
PhD & Postdoc Alumni
Ritabrata Dutta [Bayesian and Frequentist Model Selection] (PhD, 2008-2012, co-advised by Prof. Jayanta Ghosh)Tenure Track Assistant Professor, Dept of Statistics, Univ. of Warwick
Zuofeng Shang [Smoothing Spline Inference & Nonparametric Bayes] (VAP, 2013-2015)
Tenure Track Assistant Professor, Department of Mathematical Sciences, NJIT
Wei Sun [High Dimensional Tensor & Statistical Stability of Classification] (Ph.D, 2011-2015)
Tenure Track Assistant Professor, Krannert School of Management, Purdue University
Zhuqing Yu [High Dimensional Semiparametric Estimation and Inference] (Ph.D, 2011-2016)
Data Scientist in AbbVie
Meimei Liu [Statistical-and-Computational Trade-off in Big Data] (Ph.D, 2013-2018, co-advised by Prof. Zuofeng Shang)
Assistant Prof. of Statistics, Virginia Tech
Shih-Kang Chao [Distributed and Online Statistical Inference] (Postdoc, 2015-2018)
Machine Learning Scientist at HEALTH[at]SCALE
Yao Zheng [Non-Asymptotic Statistical Inference for Dependent Data] (Postdoc, 2017-2019)
Assistant Professor, Dept of Statistics, University of Connecticut
Ching-Wei Cheng [Statistical Studies of Genetic Algorithm] (PhD, 2014-2019)
Data Scientist in Lowes
Botao Hao [Statistical Guarantees in Non-Convex Optimization] (PhD, 2014-2019)
Research Scientist in OpenAI
Qing Yang [Random Tensor Theory & Its Applications] (Postdoc, 2017-2020)
Assistant Professor in the School of Management, University of Science and Technology of China
Jincheng Bai [Sparse Deep Neural Networks] (PhD, 2014-2020, co-advised by Prof. Qifan Song)
Quantitative Research Associate at J.P. Morgan
Tianyang Hu [Nonparametric Perspective of Deep Learning] (PhD, 2016-2020)
Senior Researcher at Noah's Ark Lab
Jiexin Duan [Distributed Nearest Neighbor Classification] (PhD, 2015-2021)
Sr. Financial Modeler at Moody's Analytics
Yang Yu [Distributed Bootstrap for Massive Data] (PhD, 2016-2021)
Data Scientist, Infrastructure Strategy at Meta
Chi-Hua Wang [Statistical Design of Sequential Decision Making] (PhD, 2017-2022)
Postdoc, UCLA
Pratik Ramprasad [Policy Evaluation in Statistical Reinforcement Learning] (PhD from 2017-2022, co-advised by Prof. Wei Sun)
Research scientist, Sentilink
Yue Xing [Statistical Theory for Adversarial Robustness in Machine Learning] (PhD from 2018-2022, co-advised by Prof. Qifan Song)
Assistant Prof. of Statistics and Computer Science & Engineering at Michigan State University
Zhanyu Wang [Statistical Inferences with Differential Privacy Guarantee] (PhD from 2018-2023, co-advised by Prof. Jordan Awan)
Research Scientist, Meta
Marie Maros [Decentralized High-dimensional Statistical Learning] (Postdoc from 2019-2021, co-advised by Prof. Gesualdo Scutari)
Assistant Prof of Industrial Engineering, Texas A&M Univ.
Yuantong Li [Statistical Matching Model in Centralized Two-sided Markets] (PhD from 2019-2024, co-advised by Prof. Wei Sun and Prof. Xiaowu Dai) Research Scientist, Meta
Chendi Wang [Generative Data with Privacy Guarantees] (Postdoc from 2021-2024, co-advised by Prof. Weijie Su) Professor, Xiamen Univ, China
Xianli Zeng [Statistical Foundation of Fairness] (Postdoc from 2020-2024, co-advised by Prof. Edgar Dobriban) Professor, Xiamen Univ, China.
Undergrad & Master Alumni
Jacob Swoveland [The Impact of Generative and Real Training Data on Model Vulnerability] (Master from 2021-2023, co-advised by Dr. Chi-hua Wang)Data Analyst in Children's Wisconsin
Ryan O'Dell [Estimating Privacy Leakage of Machine Learning Models] (Master from 2021-2023, co-advised by Dr. Chi-hua Wang)
Data Analyst in Mercury Insurance
Nicklaus Kim [Privacy Auditing of Generative Tabular Data Generators Using Membership Inference Attacks] (Master from 2021-2023, co-advised by Dr. Chi-hua Wang)
Trevor Beer [Generative Data Science: Applications for Early Life Cycle Cost Estimation in the Aerospace Industry] (Master from 2022-2024)