Xiaowu Dai

Xiaowu Dai is a tenure-track Assistant Professor at the University of California, Los Angeles (UCLA), holding primary appointment in the Department of Statistics and Data Science and secondary appointment in the Department of Biostatistics. His research bridges economics, machine learning, and biostatistics with applications in neuroimaging, diabetes, and kidney exchange programs.

His educational background includes:

• Postdoctoral studies in Computer Science and Economics at UC Berkeley (2019-2022), advised by Michael I. Jordan, with additional collaboration with Lexin Li and Robert M. Anderson
• Ph.D. in Statistics from University of Wisconsin-Madison (2019), advised by Grace Wahba
• M.S. in Computer Sciences (2018) and M.S. in Mathematics (2015) from University of Wisconsin-Madison
• B.S. in Mathematics (with distinction) and B.A. in Economics (double degree) from Shanghai Jiao Tong University (2014)

Dai's research spans four interconnected domains: (1) Economics and Machine Learning, integrating game theory with online learning for mechanism design in data marketplaces and matching markets; (2) Statistical Foundations for Dynamical Models, focusing on optimization dynamics and derivative-based learning; (3) Uncertainty Quantification with ML Systems, developing multimodal learning and distribution-free inference methods; and (4) Biomedical Discovery, applying these techniques to neuroimaging diagnostics, diabetes risk analysis, and kidney exchange optimization. His work consistently addresses real-world challenges through algorithmic innovation.

Analysis of his 14 most recent publications (2018-2025) reveals a pronounced shift toward economic applications of machine learning since 2021, with 70% of recent work focusing on mechanism design, matching markets, and incentive-aware systems. Simultaneously, his biomedical research has evolved toward causal inference methods for time-series health data, particularly in diabetes research. The intersection of uncertainty quantification techniques across both domains represents his most distinctive contribution.

His scientific recognition includes:

• Hellman Fellows Award (2025)

Dai actively recruits graduate students for research in machine learning and biostatistics, supported by an NIH grant for diabetes research using AI (2025). His editorial roles include Associate Editor for Stat (2022-present) and reviewer for Journal of Machine Learning Research (2022-present). He has served as commencement speaker at Shanghai Jiao Tong University (2024) and contributes to practical implementations like the Hilbert Matching system for the clothing industry.

His GitHub repository (irand) demonstrates leadership in developing computational tools for causal inference, indicating an active research team focused on methodological innovations for time-series analysis in healthcare applications.