Xiaowu Dai
Dr. Xiaowu Dai is an Assistant Professor in the Department of Biostatistics, UCLA Fielding School of Public Health. His specific appointments are in the Department of Statistics and Data Science (primary) and the Department of Biostatistics (secondary) at University of California, Los Angeles (UCLA). He is also affiliated with California Center for Population Research and Consortium of Data Analytics in Risk.
Before joining UCLA in 2022, Xiaowu was a postdoc at UC Berkeley from 2019-2022, advised by Prof. Michael I. Jordan, and also worked with Prof. Lexin Li and Prof. Robert M. Anderson. Xiaowu obtained his Ph.D. degree in Statistics from University of Wisconsin, Madison, where he worked with Prof. Grace Wahba, and received his B.S. (summa cum laude) from the Department of Mathematics at Shanghai Jiao Tong University, China, in 2014. Xiaowu and his students focus on developing statistical and machine learning methods motivated by important questions in microeconomics and biomedical sciences. On the statistical methodology side, his research interests include machine learning and economics, learning in dynamical systems, and high-dimensional nonparametric statistics. On the biomedical application side, his research interests include Alzheimer’s disease, cardiovascular disease, neuroimaging, and kidney exchanges.
Education
- PhD, Statistics, University of Wisconsin, Madison, WI, 2019
- MS, Computer Sciences, University of Wisconsin, Madison, WI, 2018
- MS, Mathematics, University of Wisconsin, Madison, WI, 2015
- BS, Mathematics, Shanghai Jiao Tong University, Shanghai, China, 2014
Areas of Interest
Dr. Dai’s research is on statistical theory and machine learning. His primary research interest lies in developing new statistical methods that integrate game theory, high-dimensional statistics, and kernel methods, with applications to economics and biomedical research. The specific topics he has studied include:
Machine learning and economics:
- Development of machine learning models for matching markets and mechanism design
- Using game theory for decision-making under uncertainty
- Understanding relationships between incentives, fairness, and strategic behaviors
Learning in dynamical systems:
- Statistical inferences in ordinary differential equations
- Geometry of gradient-based optimization methods
- Continuous-in-time analysis for multi-agent decision systems
High-dimensional nonparametric statistics:
- Inferences under high-dimensional confounding variables
- False discovery rate control for nonparametric selection methods
- Transparency analysis for mis-specified models
Biomedical research:
- Pathological mechanisms for Alzheimer’s disease
- Causal inferences for cardiovascular disease
- Fairness and efficiency in kidney exchanges and organ transplantations