Mo Li

Mo Li
  • Faculty
  • Assistant Professor

Biography

Mo joined the Department of Mathematics at the 亚洲自慰视频 of 亚洲自慰视频 at Lafayette as an assistant professor in August 2023. He earned his PhD in the System Modeling and Analysis Program with a concentration in statistics and data science from Virginia Commonwealth 亚洲自慰视频 in 2021. Under Dr. QiQi Lu鈥檚 supervision, his research delved into changepoint detection in correlated categorical time series. He also holds an MS in Operations Research from Virginia Commonwealth 亚洲自慰视频 and a dual major BS in Statistics and Biology from the 亚洲自慰视频 of New Mexico (jointly awarded by Northwest 亚洲自慰视频, China). After obtaining his PhD, Mo conducted postdoctoral research under Dr. Ni Zhao at Johns Hopkins Bloomberg School of Public Health.

Interdisciplinary collaborations with researchers from various fields form another essential part of his research program. He is also dedicated to mentoring graduate and undergraduate 亚洲自慰视频s, guiding their research projects through his expertise and their collaborative efforts.

Education

Ph.D. in Systems Modeling and Analysis, 2021
Virginia Commonwealth 亚洲自慰视频

M.S. in Operations Research, 2018
Virginia Commonwealth 亚洲自慰视频

B.S. in Biology and Statistics, 2015
The 亚洲自慰视频 of New Mexico

B.S. in Biology, 2013
Northwest 亚洲自慰视频, China

亚洲自慰视频 Research/Collaboration

  • Categorical time series,
  • Changepoint detection
  • Microbiome and T-cell receptor sequencing data analysis
  • Statistical applications in the environmental and biological sciences

Publications

  • Li, Runzhe, Li, Mo, & Zhao, Ni, A Mixed鈥怑ffect Kernel Machine Regression Model for Integrative Analysis of Alpha Diversity in Microbiome Studies,
    Genetic Epidemiology, 49(1) (2025), e22596 (first published: 30 September 2024).
  • Qinwen Deng, Yangwen Zhang, Mo Li, Songyang Zhang, and Zhi Ding, Efficient Eigen-Decomposition for Low-Rank Symmetric Matrices in Graph Signal Processing: An Incremental Approach, IEEE Transactions on Signal Processing, 72 (2024), 4918-4934.
  • Li, Mo, Hua, X., Li, S., Wu, M. C., & Zhao, N., A multi-bin rarefying method for evaluating alpha diversities in TCR sequencing data, Bioinformatics, 40(7) (2024), btae431 (published 1 July 2024).
  • Li, Mo, Robert E. Tyx, Angel J. Rivera, Ni Zhao, and Glen A. Satten. What Can We Learn about the Bias of Microbiome Studies from Analyzing Data from Mock Communities?, Genes, 13 (2022) no. 10, 1758
  • Li, Mo and QiQi Lu. Changepoint detection in autocorrelated ordinal categorical time series,Environmetrics, 33 (2022) no. 7, e2752