I am an Assistant Professor of Statistics at the University of South Carolina (USC). Before joining USC in 2020, I was a postdoc at the University of Pennsylvania for two years. 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 career in academia, I worked in industry for five years as an engineer and as a financial software analyst. Here are the links to my CV and Google Scholar. If my work interests you, you are also welcome to connect with me on LinkedIn and BlueSky.

My research group conducts research in the following areas of statistics and machine learning:
  • deep learning and deep generative models
  • methods and scalable algorithms for high-dimensional data
  • Bayesian and empirical Bayes methodology and computation
  • matrix completion and recommender systems
  • survival analysis and causal inference
In Spring 2025, I am teaching STAT 515: Statistical Methods I.

Prospective Students

My group is looking for highly motivated students to work in one or more of the following areas: deep learning, Bayesian inference, causal inference, and scalable algorithms for big data. To work with me, you must be currently enrolled as a student at USC. PhD students interested in my supervision must also first pass the PhD Qualifying Exam (offered every August). Information for prospective students

Recent News

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