Gen Li

Gen Li, PhD, is an Associate Professor in the Department of Biostatistics at the University of Michigan School of Public Health. He holds a PhD from the University of North Carolina at Chapel Hill (2015) and a BS from Beijing Normal University (2010). His research focuses on developing statistical methods for complex biomedical data, including high-dimensional data, tensor arrays, and multi-omics studies. Key interests include dimension reduction, predictive modeling, network analysis, and data integration in genomics, microbiome, and multi-omics contexts. His work has been supported by NIH grants and recognized through awards like the John G. Searle Assistant Professorship (2021).

Education:

• PhD in Statistics, University of North Carolina at Chapel Hill, 2015
• BS in Mathematical Sciences, Beijing Normal University, 2010

Research Interests: Low-rank models, tensor analysis, network analysis, longitudinal omics data, microbiome analysis, and data integration. His projects include developing methods for differential analysis of longitudinal omics data, network estimation for multi-omics, and nonlinear regression for microbiome data.

Awards:

• John G. Searle Assistant Professorship (University of Michigan, 2021)
• Sigma Xi Inductee (2019)
• Sanford Bolton Faculty Scholar (Columbia, 2018)
• Calderone Junior Faculty Award (Columbia, 2016)

Advising & Grants: Dr. Li’s NIH-funded research emphasizes multi-omics integration and microbiome-driven health studies. His grants support collaborations in cancer, chronic disease, and pediatric health. He advises students on statistical methods for biomedical data analysis.

Labs/Teams: Active in the University of Michigan’s Biostatistics Research Group and collaborates with multi-disciplinary teams in genomics and public health.