Wenxin 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 Associate Professor in the Department of Mathematics at the University of California, San Diego [Google Scholar & CV].

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?

Complete Publications (arXiv)

Selected Publications

Smoothed quantile regression with large-scale inference
with Xuming He, Xiaoou Pan and Kean Ming Tan
Journal of Econometrics, to appear, 2022+
[DOI] [R package] [Python code] [slides]

High-dimensional quantile regression: convolution smoothing and concave regularization
with Kean Ming Tan and Lan Wang
Journal of the Royal Statistical Society: Series B, 84(1): 205-233,2022
[DOI] [preprint] [R package] [Python code]

Multiplier bootstrap for quantile regression: Non-asymptotic theory under random design
with Xiaoou Pan
Information and Inference: A Journal of the IMA, 10, 813-861, 2021
[DOI] [R package]

A new principle for tuning-free Huber regression
with Lili Wang, Chao Zheng and Wen Zhou
Statistica Sinica, 31, 2153-2177, 2021
[DOI] [supplement] [R package]

Iteratively reweighted l1-penalized robust regression
with Xiaoou Pan and Qiang Sun
Electronic Journal of Statistics, 15, 3287-3348, 2021
[DOI] [R package] [Python code]

Robust inference via multiplier bootstrap
with Xi Chen
The Annals of Statistics, 48, 1665-1691, 2020
[DOI] [supplement] [Matlab code]

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

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

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

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]

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]

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]

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

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

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

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

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

Editorial Service

2022-Present: Associate Editor, Annals of Statistics
2022-Present: Associate Editor, Annals of Applied Probability
2022-Present: Associate Editor, JRSSB
2020-Present: Associate Editor, Statistics: A Jnl of Theor. & Appl. Stat


2021-Present: Associate Professor, Department of Mathematics, University of California, San Diego
2017-21: 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