Wenxin Zhou
Associate Professor
Department of Information and Decision Sciences
Pronouns: He/Him/His
Contact
Building & Room:
UH 2423
Address:
601 S. Morgan St., Chicago, IL 60607
Office Phone:
Email:
CV Link:
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About
My general research interests range from parametric to nonparametric regression problems, including high-dimensional regression under sparsity, low-rank matrix recovery/estimation, kernel methods, and deep neural networks for nonparametric regression. More recently, I have focused on tail-centric regressions, such as quantile and expected shortfall regression. I am also interested in developing privacy-preserving statistical methods and tail-robust techniques for regression and factor models.
Selected Grants
Australian Research Council, Discovery Projects DP230100147 (2023-2026), Mitigating bias in statistical analyses of data collected over time, Partner Investigator
U. S. National Science Foundation, DMS-2401268 (2023-2025), Collaborative Research: Inference and Decentralized Computing for Quantile Regression and Other Non-Smooth Methods, Principal Investigator
U. S. National Science Foundation, DMS-2113409 (2021-2023), Collaborative Research: Inference and Decentralized Computing for Quantile Regression and Other Non-Smooth Methods, Principal Investigator
U. S. National Science Foundation, DMS-1811376 (2018-2021), A Non-Asymptotic Theory of Robustness, Principal Investigator
Selected Publications
- S. Zhang, X. He, K. M. Tan and W.-X. Zhou. (2025). High-dimensional expected shortfall regression. Journal of the American Statistical Association, in press. DOI:10.1080/01621459.2024.2448860.
- M. Yu, Y. Wang, S. Xie, K. M. Tan and W.-X. Zhou. (2025). Estimation and inference for nonparametric expected shortfall regression over RKHS. Journal of the American Statistical Association, in press. DOI:10.1080/01621459.2024.2441657.
- Y. He, L. Li, D. Liu and W.-X. Zhou. (2025). Huber Principal Component Analysis for large-dimensional factor models. Journal of Econometrics, 249 Part B, 105993.
- T. Zhao, W.-X. Zhou and L. Wang. (2025). Private optimal inventory policy learning for feature-based newsvendor with unknown demand. Management Science, 71(7): 6092-6111.
- J. Fan, Y. Gu and W.-X. Zhou. (2024). How do noise tails impact on deep ReLU networks? The Annals of Statistics, 52(4): 1845-1871.
- X. He, K. M. Tan and W.-X. Zhou. (2023). Robust estimation and inference for expected shortfall regression with many regressors. Journal of the Royal Statistical Society, Series B, 85(4):1223-1246.
- X. He, X. Pan, K. M. Tan and W.-X. Zhou. (2023). Smoothed quantile regression with large-scale inference. Journal of Econometrics, 232(2): 367-388.
- K. M. Tan, L. Wang and W.-X. Zhou. (2022). High-dimensional quantile regression: Convolution smoothing and concave regularization. Journal of the Royal Statistical Society, Series B, 84(1): 205-233.
- X. He, X. Pan, K. M. Tan and W.-X. Zhou. (2022). Scalable estimation and inference for censored quantile regression process. The Annals of Statistics, 50(5): 2899-2924.
- X. Chen and W.-X. Zhou. (2020). Robust inference via multiplier bootstrap. The Annals of Statistics, 48(3): 1665-1691.
- Q. Sun, W.-X. Zhou and J. Fan. (2020). Adaptive Huber regression. Journal of the American Statistical Association, 115(529): 254-265.
Publication Aggregators
Service to Community
- Associate Editor, The Annals of Statistics (2022-2024)
- Associate Editor, Statistics: A Journal of Theoretical and Applied Statistics (2020–2023)
- Reviewers for OR, AOS, AOP, JRSS-B, JASA, Biometrika, JMLR, JOE, JBES, IEEE-TIT, IAI, AIHP, Statistical Science, JCGS, Bernoulli, EJS, ET, etc
Professional Leadership
Associate Editor, The Annals of Applied Probability (01/2022–Present)
Associate Editor, Journal of the Royal Statistical Society: Series B (01/2022–Present)
Associate Editor, Statistica Sinica (07/2025-Present)
Notable Honors
2020-21, Hellman Fellow, University of California, San Diego
Education
Ph.D. in Mathematics (Specializing in Statistics) – Hong Kong University of Science and Technology, 2013
B.Sc. in Mathematics – Shanghai Jiao Tong University, 2008
Professional Memberships
Member of the American Statistical Association
Research Currently in Progress
Empowering Climate Science through Effective Tail Learning Methodology and Theory
Intellectual Property
Python package 'quantes':
Joint Quantile and Expected Shortfall Regression (https://pypi.org/project/quantes/)
R package 'conquer':
Convolution-Smoothed Quantile Regression (https://cran.r-project.org/web/packages/conquer/index.html)