About

I am an Assistant Professor of Statistics at George Mason University (GMU). My current research focuses mainly on Bayesian statistics, deep learning, and causal inference. I am especially interested in developing new methodologies and scalable algorithms for high-dimensional data where the number of parameters and/or the sample size is large.

Before joining GMU in 2025, I was an Assistant Professor at the University of South Carolina (2020-2025), and before that, I was a postdoc at the University of Pennsylvania (2018-2020). I got my PhD in Statistics from the University of Florida, my MS in Applied Mathematics from the University of Massachusetts Amherst, and my BA from Cornell University. Prior to my academic career, I worked in industry for five years as an engineer and as a financial software analyst.

Here is my CV and some other identifiers: Google Scholar | GitHub | ORCID | Bluesky

Recent News

  • September 2025: My single-authored paper “Bayesian group regularization in generalized linear models with a continuous spike-and-slab prior” has been accepted by Annals of the Institute of Statistical Mathematics. Paper

  • September 2025: “Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression” (with Anja Zgodic, Jiajia Zhang, Yuan Wang, Christopher Rorden, and Alexander McLain) has been accepted by Computational Statistics. Paper

  • August 2025: New preprint “Parameter expanded variational Bayes for well-calibrated high-dimensional linear regression with spike-and-slab priors” (with Peter Olejua, Enakshi Saha, Rahul Ghosal, and Alexander McLain). Preprint

  • August 2025: “Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm” (with Anja Zgodic, Jiajia Zhang, Peter Olejua, and Alexander McLain) has been published in Statistics and Computing. Paper

  • July 2025: “A unified three-state model framework for analysis of treatment crossover in survival trials” (with Zile Zhao, Ye Li, and Xiaodong Luo) has been published in Statistics in Biopharmaceutical Research. Paper

  • July 2025: Congratulations to my summer REU students Mark Ritchie, Emily Sitnik, and Evan Funderburg for completing their summer research on spatial causal inference for areal data!

  • May 2025: New preprint “A robust monotonic single-index model for skewed and heavy-tailed data: A deep neural network approach applied to periodontal studies” (with Qingyang Liu, Shijie Wang, and Dipankar Bandyopadhyay). arXiv

  • January 2025: My student Zile Zhao has defended his PhD dissertation “Methods and Applications for Bayesian Semiparametric Survival Analysis” and will join the Moffitt Cancer Center as a postdoctoral fellow. Congratulations, Zile!

  • January 2025: “Two-step mixed-type multivariate Bayesian sparse variable selection with shrinkage priors” (with Shao-Hsuan Wang and Hsin-Hsiung Huang) has been published in Electronic Journal of Statistics. Paper

  • November 2024: “Generative quantile regression with variability penalty” (with Shijie Wang and Minsuk Shin) has been published in Journal of Computational and Graphical Statistics. Paper

  • November 2024: “Bayesian modal regression based on mixture distributions” (with Qingyang Liu and Xianzheng Huang) has been published in Computational Statistics & Data Analysis. Paper

  • September 2024: “VCBART: Bayesian trees for varying coefficients” (with Sameer Deshpande, Cecilia Balocchi, Jennifer Starling, and Jordan Weiss) has been accepted by Bayesian Analysis. Paper

  • June 2024: New preprint “Neural-g: A deep learning framework for mixing density estimation” (with Shijie Wang, Saptarshi Chakraborty, and Qian Qin). arXiv

  • May 2024: My student Shijie Wang has defended his PhD dissertation “New Deep Learning Approaches to Classical Statistical Problems” and will join Gauss Labs as an Applied Scientist. Congratulations, Shijie!