Mo Li

Mo Li
  • Faculty
  • Assistant Professor

Biography

Mo joined the Department of Mathematics at the 天美传媒安卓破解版 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 University in 2021. Under Dr. QiQi Lu鈥檚 supervision, his research delved into changepoint detection in correlated categorical time series. He also hold an MS in Operations Research from Virginia Commonwealth University and a dual major BS in Statistics and Biology from the University of New Mexico (jointly awarded by Northwest University, China). After obtaining my 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 students, guiding their research projects through his expertise and our collaborative efforts.

Education

Ph.D. in Systems Modeling and Analysis, 2021
Virginia Commonwealth University

M.S. in Operations Research, 2018
Virginia Commonwealth University

B.S. in Biology and Statistics, 2015
The University of New Mexico

B.S. in Biology, 2013
Northwest University, China

Student 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