Wei Pan

Wei Pan is a Professor in the Division of Biostatistics & Health Data Science at the School of Public Health, University of Minnesota. He is also affiliated with the Graduate Program in Bioinformatics, Biomedical Genomics Center, and Masonic Cancer Center.

• PhD in Statistics (UW-Madison, 1997)
• MS in Computer Science (UW-Madison, 1996)
• BEng & BS in Computer Engineering & Applied Math (Tsinghua University, 1989)

Research Interests

Dr. Pan specializes in statistical genetics, focusing on genetic association testing for GWAS and sequencing data. His work in bioinformatics includes integrative analysis of multi-omics data and microarray gene expression analysis. He develops methods in statistical learning (supervised, unsupervised, semi-supervised), penalized/regularized models, and survival analysis for clinical trials.

Publication Trends

His recent publications (2021-2023) emphasize causal machine learning (Mendelian randomization, IV regression, causal networks) and deep learning (CNNs for genomic data, interpretable neural networks). Earlier work (2008-2014) focused on graphical models and clustering algorithms for biomedical data.

Scientific Recognition

• Fellow, American Statistical Association (2010-- )
• Member, Delta Omega Honorary Society in Public Health
• Editorial Board, Statistics in Medicine (2002-2006)
• Associate Editor, JASA (2003-2006)

Advising & Grants

Dr. Pan advises students in statistical genetics and machine learning, with trainees participating in the NIGMS T32 Biostatistics Training in Genetics and Genomics program (2020-2025). His research involves collaborations with biomedical scientists and clinicians, supported by NIH grants including the T32 training grant.

Labs & Teams

He leads a research group meeting weekly for collaborative projects, with subgroups focusing on network analysis, deep learning, and semi-supervised learning. The group participates in the BD2K Guide to the Fundamentals of Data Science Series and hosts post-doctoral researchers in statistical genetics and neuroimaging.