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 received my PhD in Statistics from the University of Florida in 2018 under the supervision of Malay Ghosh.
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, data integration, and analysis of electronic health records.
In Fall 2021, I am teaching STAT 714: Linear Statistical Models.
Recent News and Updates
July 2021: A chapter on spike-and-slab lasso methods (with Veronika Ročková and Edward I. George) will appear in the Handbook of Bayesian Variable Selection.
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) on 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.
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.
