Jun Shao

Jun Shao is a Professor of Statistics at the University of Wisconsin-Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on statistical theory and methodology, including resampling methods, high-dimensional data analysis, and medical statistics. He holds prestigious fellowships from the Institute of Mathematical Statistics and the American Statistical Association.

Education:

• B.S., Mathematics, East China Normal University (1982)
• Ph.D., Statistics, University of Wisconsin-Madison (1987)

Research Interests:

Shao's work addresses challenges in statistical inference, such as handling missing data, longitudinal studies, and covariate-adaptive designs in clinical trials. He develops methodologies for resampling techniques, variable selection, and nonignorable nonresponse scenarios. His contributions include influential papers on log-rank tests, robust treatment effect estimation, and integration of external data sources.

Professional Activities:

• Editor of Statistical Theory and Related Fields (2017–present)
• Co-Editor of Journal of Systems Science and Complexity (2014–2019)
• Fellow of the Institute of Mathematical Statistics and American Statistical Association

Awards:

• Fellow, Institute of Mathematical Statistics (Year not specified)
• Fellow, American Statistical Association (Year not specified)

Teaching & Mentorship:

Shao teaches advanced courses like Mathematical Statistics and Variable Selection, emphasizing rigorous theoretical foundations and practical applications. His courses cover topics such as asymptotic theory, likelihood-based methods, and modern variable selection techniques.