Department of Mathematics and Statistics
University of Saskatchewan
106 Wiggins Road, MCLN 219
Saskatoon, SK, CANADA, S7N5E6
Email: longhai.li@usask.ca
Homepage: https://longhaisk.github.io
Official Profile: https://artsandscience.usask.ca/profile/LLi
Short Bio
Longhai Li is a professor at the University of Saskatchewan in Canada. He earned his B.Sc. honours in statistics from the University of Science and Technology of China and his Ph.D. in statistics from the University of Toronto under the supervision of Prof. Radford M. Neal, a core member of the U of T machine learning group. Building on this foundation, Prof. Li develops computationally intensive tools for bioinformatics and epidemiology to solve complex health science problems. His focus on Predictive Methods for Model Validation bridges a critical gap by providing novel residual diagnostic tools to evaluate intricate Bayesian and non-Bayesian structures for highly correlated data. Additionally, his work in Statistical Machine Learning improves phenotype modeling by designing robust methods to accurately identify and measure truly predictive features, ultimately applying these advanced techniques to uncover the underlying molecular mechanisms driving conditions like Alzheimer’s and Parkinson’s diseases. Supported by funding from NSERC, CANSSI, CFI, CFREF, and MITACS, his research papers have appeared in highly reputed journals, specifically the Journal of the American Statistical Association, Bayesian Analysis, Statistics and Computing, and the Canadian Journal of Statistics, among many others. He also served on the NSERC IDG EG 1508 committee (Mathematics and Statistics) from 2022 to 2025. More details are in his full CV.
Research Interests
statistical learning, cross-validation, hierarchical modelling, survival modelling, model checking, residual diagnostics, model comparison, zero-inflated models, high-throughput data, microbiome data