Wen-Xin Zhou


Department of Mathematics
University of California, San Diego
9500 Gilman Dr.
La Jolla, CA 92093

Phone: 858-534-2640
E-mail: wez243@ucsd.edu
Office: AP&M 6131




I am an Assistant Professor in the Department of Mathematics at the University of California, San Diego [Google Scholar].

My research uses tools and ideas from probability theory (concentration phenomenon, empirical process theory), functional and geometric analysis, and numerical optimization to understand high-dimensional and/or large-scale estimation and inference problems as well as complex machine learning tasks. The driving force of my research is in addressing several core challenges in statistics and data science, such as robustness, heterogeneity, model uncertainty, and statistical and computational trade-offs. Questions of this sort include: (a) Can we develop statistical methods that are robust to violations of the classical yet stringent assumptions, such as normality and homogeneity? (b) Given a complex statistical problem, how much data is required (sample size versus model complexity) to guarantee an effective solution? (c) For a given statistical problem, can we develop a statistically optimal method that can be solved via computationally efficient algorithms?



In a democracy it is important to discriminate influence from authority.
-- Charles W. Eliot

Teaching

MATH 281A: Mathematical Statistics (classical)
[syllabus] [website]

MATH 281C: Mathematical Statistics (modern)
[syllabus]

MATH 287D: Statistical Learning
[syllabus]

MATH 185: Introduction to Computational Statistics
[syllabus]

MATH 189: Exploratory Data Analysis and Inference
[syllabus]

MATH 181A: Introduction to Mathematical Statistics I

MATH 181B: Introduction to Mathematical Statistics II
[syllabus]

Preprints

FarmTest: An R package for factor-adjusted robust multiple testing
with Koushiki Bose, Jianqing Fan, Yuan Ke and Xiaoou Pan
Preprint, 2019
[pdf] [software]

On the asymptotic distribution of the scan statistic for empirical distributions
with Andrew Ying
Preprint, 2019
[arXiv]

Nonconvex regularized robust regression with oracle properties in polynomial time
with Xiaoou Pan and Qiang Sun
Preprint, 2019
[arXiv] [software]

A new principle for tuning-free Huber regression
with Lili Wang, Chao Zheng and Wen Zhou
Preprint, 2018
[pdf] [supplement] [software] [slides]

Publications

Multiplier bootstrap for quantile regression: Non-asymptotic theory under random design
with Xiaoou Pan
Information and Inference: A Journal of the IMA, to appear, 2020+
[pdf] [software]

Robust inference via multiplier bootstrap
with Xi Chen
The Annals of Statistics, to appear, 2020+
[pdf] [supplement] [Matlab code]

Adaptive Huber regression
with Qiang Sun and Jianqing Fan
Journal of the American Statistical Association, 115, 254-265, 2020
[DOI] [arXiv] [software] [slides]

FarmTest: Factor-adjusted robust multiple testing with approximate false discovery control
with Jianqing Fan, Yuan Ke and Qiang Sun
Journal of the American Statistical Association, 114, 1880-1893, 2019
[DOI] [software]

User-friendly covariance estimation for heavy-tailed distributions
with Yuan Ke, Stanislav Minsker, Zhao Ren and Qiang Sun
Statistical Science, 34, 454-471, 2019
[DOI]

Principal component analysis for big data
with Jianqing Fan, Qiang Sun and Ziwei Zhu
Wiley StatsRef: Statistics Reference Online, 2018
[DOI] [arXiv]

A new perspective on robust M-estimation: Finite sample theory and applications to dependence-adjusted multiple testing
with Koushiki Bose, Jianqing Fan and Han Liu
The Annals of Statistics, 46, 1904-1931, 2018
[DOI] [arXiv]

Are discoveries spurious? Distributions of maximum spurious correlations and their applications
with Jianqing Fan and Qi-Man Shao
The Annals of Statistics, 46, 989-1017, 2018
[DOI] [arXiv] [slides]

Max-norm optimization for robust matrix recovery
with Ethan X. Fang, Han Liu and Kim-Chuan Toh
Mathematical Programming, Series B, 167, 5-35, 2018
[DOI] [arXiv]

On Gaussian comparison inequality and its application to spectral analysis of large random matrices
with Fang Han and Sheng Xu
Bernoulli, 24, 1787-1833, 2018
[DOI] [arXiv]

Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity
with Jinyuan Chang, Chao Zheng and Wen Zhou
Biometrics, 73, 1300-1310, 2017
[DOI] [arXiv] [slides]

Self-normalization: Taming a wild population in a heavy-tailed world
with Qi-Man Shao
Applied Mathematics - A Journal of Chinese Universities, 32, 253-269, 2017
[DOI]

Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering
with Jinyuan Chang, Wen Zhou and Lan Wang
Biometrics, 73, 31-41, 2017
[DOI] [arXiv]

Two-sample smooth tests for the equality of distributions
with Chao Zheng and Zhen Zhang
Bernoulli, 23, 951-989, 2017
[DOI] [arXiv]

Guarding against spurious discoveries in high dimensions
with Jianqing Fan
Journal of Machine Learning Research, 17(203), 1-34, 2016
[DOI] [arXiv] [slides]

Nonparametric covariate-adjusted regression
with Aurore Delaigle and Peter Hall
The Annals of Statistics, 44, 2190-2220, 2016
[DOI]

Cramér-type moderate deviations for Studentized two-sample U-statistics with applications
with Jinyuan Chang and Qi-Man Shao
The Annals of Statistics, 44, 1931-1956, 2016
[DOI] [slides]

Matrix completion via max-norm constrained optimization
with Tony T. Cai
Electronic Journal of Statistics, 10, 1493-1525, 2016
[DOI]

Cramér type moderate deviation theorems for self-normalized processes
with Qi-Man Shao
Bernoulli, 22, 2029-2079, 2016
[DOI]

Stein’s method for nonlinear statistics: A brief survey and recent progress
with Qi-Man Shao and Kan Zhang
Journal of Statistical Planning and Inference, 168, 68-89, 2016
[DOI]

Nonparametric and parametric estimators of prevalence from group testing data with aggregated covariates
with Aurore Delaigle
Journal of the American Statistical Association, 110, 1785-1796, 2015
[DOI]

Necessary and sufficient conditions for the asymptotic distributions of coherence of ultra-high dimensional random matrices
with Qi-Man Shao
The Annals of Probability, 42, 623-648, 2014
[DOI] [arXiv] [slides]

A max-norm constrained minimization approach to 1-bit matrix completion
with Tony T. Cai
Journal of Machine Learning Research, 14, 3619-3647, 2013
[DOI]

Bio

2017-Present: Assistant Professor, Department of Mathematics, University of California, San Diego
2015-17: Postdoctoral Research Associate, Department of Operations Research and Financial Engineering, Princeton University
2013-15: Research Fellow, School of Mathematics and Statistics, University of Melbourne
2009-13: Phd Student, Department of Mathematics, Hong Kong University of Science and Technology