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, supervised by Yong Chen and Mary Boland. I earned my PhD in Statistics from the University of Florida under the supervision of Malay Ghosh, my MS in Applied Mathematics from the University of Massachusetts Amherst, and my BA in Economics and Government 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 focuses on Bayesian statistics, high-dimensional modeling, and scalable machine learning algorithms for large and complex data sets. 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, microbiome data analysis, and analysis of electronic health records. We develop methods and theory, as well as algorithms for harnessing the full potential of high-performance computing.

In Fall 2022, I am teaching STAT 517: Advanced Statistical Models and STAT 714: Linear Statistical Models.

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

Recent News

  • May 2022: PhD student Shijie Wang has joined my research group. We are studying scalable approaches to uncertainty quantification using Bayesian bootstrap. I am also now working with PhD student Qingyang Liu and his advisor Xianzheng Huang on new approaches to Gaussian process regression.

  • 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 “On the proof of posterior contraction for sparse generalized linear models with multivariate responses” (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

  • December 2021: A chapter on spike-and-slab lasso methods (with Veronika Ročková and Edward I. George) has been published in the Handbook of Bayesian Variable Selection. Link

  • October 2021: New preprint “A Bayesian selection model for correcting outcome reporting bias with application to a meta-analysis on heart failure interventions” (with Xiaokang Liu, Lifeng Lin, Yulun Liu, Stephen E. Kimmel, Haitao Chu, and Yong Chen). arXiv

  • May 2021: New preprint “A Bayesian hierarchical modeling framework for geospatial analysis of adverse pregnancy outcomes” (with Cecilia Balocchi, Jessica Liu, Silvia P. Canelón, Edward I. George, Yong Chen, and Mary R. Boland). arXiv

  • May 2021: “Individual- and neighborhood-level risk factors for severe maternal morbidity” (with Jessica R. Meeker, Silvia P. Canelón, Lisa D. Levine, and Mary R. Boland) has been published in Obstetrics & Gynecology. Link

  • April 2021: “On the beta prime prior for scale parameters in high-dimensional Bayesian regression models” (with Malay Ghosh) has been published in Statistica Sinica. Link