I joined the Department of Statistics at the University of South Carolina as an Assistant Professor in 2020. From 2018 to 2020, I was a postdoc at the University of Pennsylvania. I earned 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 also worked in industry for five years as an engineer and as a financial software analyst.

My research group conducts research in the following areas of statistics and machine learning:
  • high-dimensional data (big n and/or big p)
  • Bayesian methodology and computation
  • deep learning and generative models
  • scalable optimization and computer-driven sampling algorithms.
Our research is primarily motivated by addressing “big data” challenges in contemporary biomedical and public health problems, including genome-wide association studies, computational drug repositioning, and analysis of electronic health records. We develop methods and theory, as well as algorithms for harnessing the full potential of high-performance computing.

My CV is attached here. You can also find me on Twitter, LinkedIn, and Google Scholar.

Recent News

  • January 2023: New preprint “Generative Quantile Regression with Variability Penalty” (with Shijie Wang and Minsuk Shin). arXiv

  • January 2023: I have been selected as a McClausland Faculty Fellow. This selective fellowship supports early-career USC College of Arts and Sciences faculty who are rising stars in their academic disciplines and committed, creative teachers.

  • November 2022: New preprint “Bayesian Modal Regression based on Mixture Distributions” (with Qingyang Liu and Xianzheng Huang). arXiv

  • June 2022: I am now the Principal Investigator for NSF grant DMS-2015528. This grant is being used to develop new approaches to deep-learning-based generative models, scalable uncertainty quantification, and Bayesian variable selection.

  • May 2022: PhD student Shijie Wang has joined my research group. We are studying scalable deep learning approaches for joint quantile regression and mixture models.

  • April 2022: I received an ASPIRE-I grant to develop new methods and algorithms for scalable Bayesian survival analysis.

  • March 2022: “Spike-and-slab group lassos for grouped regression and sparse generalized additive models” (with Gemma E. Moran, Joseph L. Antonelli, Yong Chen, and Mary R. Boland) has been published in Journal of the American Statistical Association. Link

  • February 2022: New preprint “Ultrahigh dimensional variable selection for mixed‐type multivariate response Bayesian generalized linear models” (with Shao-Hsuan Wang and Hsin-Hsiung Huang). arXiv

  • January 2022: “Neighborhood deprivation increases the risk of post-induction cesarean delivery” (with Jessica R. Meeker, Heather H. Burris, Lisa D. Levine, and Mary R. Boland) has been published in Journal of the American Medical Informatics Association. Link

Click here for news from past years