Mengbing Li

PhD Candidate (Biostat)

University of Michigan
Google Scholar


2017-19, M.S. in Biostatistics, University of Michigan, Ann Arbor

2013-17, B.S.P.H., Biostatistics and Mathematics. Minor: Statistics, University of North Carolina at Chapel Hill. Graduated with Highest Honor and Distinction


  • 2023, ENAR Distinguished Student Paper Award, Baltimore 2024
  • 2023, Outstanding Graduate Student Instructor (Biostatistics)
  • 2023, Institute of Mathematical Statistics Hannan Graduate Student Travel Award
  • 2023, Winner of Research Poster Competition, International Biometric Society, Eastern North American Region, Nashville, TN
  • 2019, Best Doctoral Qualifying Exam Award
  • 2018, Outstanding Graduate Student Instructor (Biostatistics)

Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy

ddtlcm: An R package for overcoming weak separation in Bayesian latent class analysis via tree-regularization

Tree-Regularized Bayesian Latent Class Analysis for Improving Weakly Separated Dietary Pattern Subtyping in Small-Sized Subpopulations

Using source-associated mobile genetic elements to identify zoonotic extraintestinal E. coli infections

Doubly Inhomogeneous Reinforcement Learning

Reinforcement Learning in Possibly Nonstationary Environment

Factors associated with inferior vena cava placement and retrieval for patients with cancer associated thrombosis

Integrating Sample Similarities into Latent Class Analysis: A Tree-Structured Shrinkage Approach

Clinical and Sociodemographic Factors Associated with Anticoagulant Use for Cancer Associated Thrombosis.

Anticoagulant Medication Adherence for Cancer Associated Thrombosis: A Comparison of LMWH to DOACs