Rate My Professor Wei Pan

Wei Pan is a Professor in the Division of Biostatistics and Health Data Science at the School of Public Health, University of Minnesota Twin Cities. He earned a PhD in Statistics from the University of Wisconsin-Madison in 1997, MS degrees in Statistics (1995) and Computer Science (1996) from the same institution, an MSEng in Computer Engineering from the Chinese Academy of Sciences in 1991, and a BEng in Computer Engineering and BS in Applied Mathematics from Tsinghua University in 1989. Pan's research interests encompass statistical genetics, bioinformatics and computational biology, and data mining. He develops statistical and computational methods for analyzing single nucleotide polymorphisms and next-generation sequencing data, integrating multiple types of genomic, proteomic, and other omics data, with applications including Alzheimer's disease genetics, causal inference, machine learning for neuroimaging, proteome-wide association studies, and trait imputation.

Pan began his academic career at the University of Minnesota as an Assistant Professor in 1997, advancing to Associate Professor in 2003 and Professor in 2007; he served as Interim Division Head from 2017 to 2018. His contributions have been honored with Fellowships from the Institute of Mathematical Statistics (2020) and the American Statistical Association (2011), induction into the University of Minnesota Academy for Excellence in Health Research (2020), and membership in the Delta Omega Honorary Society in Public Health. He holds editorial roles including Associate Editor for Statistics in Biosciences (2015-present) and previously for the Journal of the American Statistical Association (2003-2006). Key publications include 'Co-expression-wide association studies link genetically regulated interactions with complex traits' (Nature Communications, 2025), 'Enhancing nonlinear transcriptome- and proteome-wide association studies via trait imputation with applications to Alzheimer's disease' (PLoS Genetics, 2025), 'Multivariate proteome-wide association study to identify causal proteins for Alzheimer disease' (American Journal of Human Genetics, 2025), 'A robust cis-Mendelian randomization method with application to drug target discovery' (Nature Communications, 2024), and 'Nonlinear causal discovery with confounders' (Journal of the American Statistical Association, 2024). As principal or contact investigator, he has led multiple NIH-funded projects on biostatistics training, graphical models for biological networks, and Alzheimer's disease research.