Jiwei Zhao

Jiwei Zhao is a statistician who focuses on developing novel methods and empirically applying these methods in real studies. His recent work concentrates on health disparities and health equity. With collaborators, Prof. Zhao uses the All of Us program to figure out analytical challenges, to better understand mechanisms of health disparity, and to propose actionable items for improving health equity.

The Zhao Research Group’s primary focus is to apply its expertise in statistics, encompassing a range of methodologies including semiparametrics, robust methods, unconventional likelihood methods, feature selection, and post-selection inference. Additionally, they specialize in statistical learning techniques such as semi-supervised learning, transfer learning, federated learning, and non-convex optimization. Their objective is to analyze data with complex structures across a diverse array of biomedical studies.

Research Areas: Semiparametric statistics, Missing data analysis, Causal inference, Analysis of electronic health records, Health disparities