I am Professor at the Department of Statistical Science with a secondary appointment at the Department of Biostatistics and Bioinformatics, Duke University. I got a BSc in Mathematics from Peking University, PhD in Biostatistics from Johns Hopkins University, and did my postdoctoral fellowship at Harvard University Department of Health Care Policy.
My main research interest is causal inference - designs and analysis for evaluating treatments and interventions in randomized experiments and observational studies, and their applications to health studies (also known as comparative effectiveness research) and social sciences. I also work on the interface between causal inference and machine learning. I have developed methods for propensity score, heterogenous treatment effects, posttreatment selection, difference-in-differences, regression discontinuity designs, representation learning, and Bayesian methods. I also have done some work in Bayesian modeling for big and complex data (such as genomics and neuroimaging data). In addition, I have some expertise on missing data.