Jiwei Zhao, PhD


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Jiwei Zhao

Jiwei Zhao

Associate Professor
Department of Statistics (School of Computer, Data & Information Sciences)
Department of Biostatistics & Medical Informatics (School of Medicine & Public Health)
University of Wisconsin-Madison

Affiliated Faculty
Data Science Institute
Institute on Aging
Center for Demography of Health and Aging
Center for Demography and Ecology
Center for Health Disparities Research
Institute for Diversity Science

Office: 5687 Morgridge Hall, 1205 University Ave, Madison WI 53706
E-mail: jiwei DOT zhao AT wisc DOT edu; jiwei2012zhao AT gmail DOT com
[Google Scholar] [OpenReview.net] [GitHub]


Short Bio

Jiwei Zhao is currently an Associate Professor at the University of Wisconsin-Madison, affiliated with the Departments of Statistics and of Biostatistics & Medical Informatics.

His research interests include semiparametric statistics, the tradeoff between efficiency and robustness, domain adaptation and transfer learning, missing data analysis and causal inference. He also focuses on domains such as patient-reported outcome, clinical trial, real-world evidence and real-world data, survey data, aging, mental health, and cancer. His work has been published in top-tier statistical journals as well as in leading machine learning conferences. His research has been consistently supported by the US National Science Foundation and the National Institutes of Health.

Jiwei is now Associate Editor for Annals of Applied Statistics, JRSS Series A: Statistics in Society, Scandinavian Journal of Statistics, and Action Editor for Transactions on Machine Learning Research (TMLR). He is also Area Chair for ICML in 2026, and AISTATS in 2024, 2025, 2026.


Research Interests

Jiwei Zhao's research focuses on applying semiparametric techniques to understand the complex structure of modern biomedical data sets, usually in the pursuit of efficiency, robustness, and the tradeoff between the two. Specifically, his work has concentrated in the fields of:

  • distribution shift, domain adaptation and transfer learning,

  • weakly supervised machine learning (semi-supervised, label noise, etc.),

  • missing data analysis and causal inference.

He also conducts research on developing trustworthy statistical inference methods for AI-predicted data and, more broadly, for synthetic data, with a focus on reliability, robustness, and principled uncertainty quantification.

Together with Dr. David Francis, Jiwei co-leads the NIH R01 project NIH/NIDCD/R01DC021431 (2023-2028) entitled CoPE II: Individualizing Patient-Reported Outcomes in Patient Care for Vocal Fold Paralysis in the Clinic and in Research. Currently patients are being recruited across a 37-site national collaborative of high-volume voice centers.

Jiwei's research is also supported by the NSF through the DMS/NIGMS program under award numbers 1953526 and 2122074 and the Statistics program under award number 2310942 , as well as by the Data Science Institute of UW-Madison.


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