Wen Zhou

Associate Professor of Biostatistics

Professional overview

Wen Zhou is an Associate Professor in the Department of Biostatistics at the School of Global Public Health. He received his Ph.D.s in Statistics and Applied Mathematics from the Iowa State University. His research focuses on developing theories and methods for network data analysis, high-dimensional statistics, multiple testing problems, machine learning, and causal inference. He is particularly interested in applications within genomics, genetics, bioinformatics, protein structure modeling, social science, epidemiology, and health policy. Wen serves on the editorial boards of the Statistica Sinica, Journal of Multivariate Analysis, Biometrics, as well as serves as the Editor-in-Chief of Journal of Biopharmaceutical Statistics. He is an elected member of the International Statistical Institute and has been elected as the WNAR program coordinator in 2024.

Before joining NYU, he was an Associate Professor in Statistics at Colorado State University and held a joint appointment in the Department of Biostatistics and Informatics at the Colorado School of Public Health as well as affiliated to the Molecular, Cellular and Integrative Neurosciences (MCIN) Program.

Visit Dr. Zhou's website to learn more about his professional history and accomplishments.

Education

BS, Computational Mathematics, Chongqing University

PhD, Applied Mathematics, Iowa State University

PhD, Statistics, Iowa State University

Honors and awards

Elected Member of International Statistical Institute (ISI) (2020)

NSF junior faculty travel award (2016)

Los Alamos Statistical Sciences Conference Grant (2014)

5th place finish in the 14th Data Mining Cup Competition (Team leader of the ISU team) (2013)

Chair’s Award on Academic Excellence (2012)

Graduate Teaching Excellence Award (2010)

Robert J. Lambert Award for Excellence in Applied Mathematics (2008)

Professional Advancement Grants (2007)

Publications

Publications

With random regressors, least squares inference is robust to correlated errors with unknown correlation structure

LARGE-SCALE INFERENCE OF MULTIVARIATE REGRESSION FOR HEAVY-TAILED AND ASYMMETRIC DATA

Ten simple rules for managing laboratory information

Non-asymptotic properties of spectral decomposition of large Gram-type matrices and applications

Zhang, L., Zhou, W., & Wang, H. (n.d.).

Publication year

2022

Journal title

Bernoulli

Volume

28

Issue

2

Page(s)

1224-1249

10.3150/21-BEJ1384

Abstract

Abstract

Gram-type matrices and their spectral decomposition are of central importance for numerous problems in statistics, applied mathematics, physics, and machine learning. In this paper, we carefully study the non-asymptotic properties of spectral decomposition of large Gram-type matrices when data are not necessarily independent. Specifically, we derive the exponential tail bounds for the deviation between eigenvectors of the right Gram matrix to their population counterparts as well as the Berry-Esseen type bound for these deviations. We also obtain the non-asymptotic tail bound of the ratio between eigenvalues of the left Gram matrix, namely the sample covariance matrix, and their population counterparts regardless of the size of the data matrix. The documented non-asymptotic properties are further demonstrated in a suite of applications, including the non-asymptotic characterization of the estimated number of latent factors in factor models and relate machine learning problems, the estimation and forecasting of high-dimensional time series, the spectral properties of large sample covariance matrix such as perturbation bounds and inference on the spectral projectors, and low-rank matrix denoising using dependent data.

Tracking the transmission dynamics of COVID-19 with a time-varying coefficient state-space model

A semiparametric latent factor model for large scale temporal data with heteroscedasticity

Large-scale multiple inference of collective dependence with applications to protein function

Nonparallel transcriptional divergence during parallel adaptation

Large scale maximum average power multiple inference on time-course count data with application to RNA-seq analysis

Equilibrium Distributions of Populations of Biological Species on Networks of Social Sites

A single dose polyanhydride-based vaccine platform promotes and maintains anti-GnRH antibody titers

Transcriptome-wide comparison of selenium hyperaccumulator and nonaccumulator Stanleya species provides new insight into key processes mediating the hyperaccumulation syndrome

Transcriptomic analysis of differentially expressed genes in the molting gland (Y-organ) of the blackback land crab, Gecarcinus lateralis, during molt-cycle stage transitions

Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering

On the preservation of cooperation in two-strategy games with nonlocal interactions

Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity

Testing for high-dimensional white noise using maximum cross-correlations

More powerful tests for sparse high-dimensional covariances matrices

Optimality and stability of symmetric evolutionary games with applications in genetic selection

Comparative analyses of C4 and C3 photosynthesis in developing leaves of maize and rice

Quantification of egg yolk contamination in egg white using UV/Vis spectroscopy : Prediction model development and analysis

A computational analysis framework for molecular cell dynamics : Case-study of exocytosis

Modeling protein interaction network and mechanisms in exocytosis