Gen Li, PhD
- Associate Professor, Biostatistics
Dr. Gen Li is interested in developing cutting-edge statistical methods for complex biomedical data, such as high dimensional data, multi-way tensor array data, and multi-view multi-type data. His work is primarily motivated by research questions arising in genomics, microbiome, and multi-omics studies. His methodological research focuses on dimension reduction, predictive modeling, network analysis, and data integration. Dr. Li's research has been recognized by several awards and supported by multiple NIH grants.
- PhD, University of North Carolina at Chapel Hill, 2015
- BS, Beijing Normal University, 2010
Microbiome data analysis, multi-omics data integration, dimension reduction, network analysis, association analysis, statistical learning.
- Developing differential analysis, network analysis, and association analysis methods for longitudinal omics data.
- Developing network estimation and community detection methods for multi-omics data.
- Developing high-dimensional mediation models for microbiome data.
- Developing nonlinear regression models for microbiome data.
Gen Li, Dan Yang, Andrew B. Nobel, Haipeng Shen (2016). Supervised singular value decomposition and its asymptotic properties. Journal of Multivariate Analysis, 146: 7-17.
Gen Li, Andrey A. Shabalin, Ivan Rusyn, Fred A. Wright, Andrew B. Nobel (2018). An empirical Bayes approach for multiple tissue eQTL analysis. Biostatistics, 19(3): 391-406.
Gen Li, Irina Gaynanova (2018). A general framework for association analysis of heterogeneous data. The Annals of Applied Statistics, 12(3): 1700-1726.
Gen Li, Xiaokang Liu, Kun Chen (2019). Integrative multi-view regression: bridging group-sparse and low-rank models. Biometrics, 75(2): 593-602.
Jihui Lee, Gen Li, James D. Wilson (2020). Varying-coefficient models for dynamic networks. Computational Statistics and Data Analysis, 152: 107052.
Gen Li, Yan Li, Kun Chen (2022+). It's all relative: regression analysis with compositional predictors. Biometrics, to appear.
View full list of publications at https://scholar.google.com/citations?user=0qYcxZIAAAAJandhl=en.