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, 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. In Spring 2023, I will teach STAT 718: High-Dimensional Data.

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

Recent News

  • September 2022: I am offering a special topics class STAT 718: High-Dimensional Data in Spring 2023. Here is the syllabus. Statistics/biostatistics graduate students and other graduate students who meet the class prerequisites may enroll. Highly motivated and well-prepared undergraduate students may also enroll if there is room.

  • 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 deep learning approaches for conditional density estimation 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 “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

Click here for news from past years