Research Expertise and Interest

causal inference, data integration, federated learning and transfer learning, electronic health records, clinical trials, High Dimensional Data Analysis

Research Description

Xiudi Li received a B.S. in Mathematics and Economics from the Hong Kong University of Science and Technology in 2017, and a Ph.D. in Biostatistics from the University of Washington in 2022. He then worked as a Postdoctoral Research Fellow at Harvard T.H. Chan School of Public Health, before joining UC Berkeley in 2024. His research interests include multi-source data integration, especially for causal inference and federated and transfer learning, as well as high dimensional complex data analysis.  He has also been working with electronic health record data to develop risk prediction algorithms.