Department of Mathematics and Statistics
University of Saskatchewan
106 Wiggins Road, Saskatoon, SK, CANADA
longhai.li@usask.ca
https://longhaisk.github.io
https://artsandscience.usask.ca/profile/LLi
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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. Building on this foundation, Prof. Li develops computationally intensive tools for bioinformatics and epidemiology to solve complex problems in the health sciences. 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 in highly correlated data. Additionally, his work in Statistical Machine Learning aims to improve phenotype modeling through the design of robust methods that accurately identify and measure truly predictive features, with a particular interest in understanding the etiology of Alzheimer’s and Parkinson’s diseases. Supported by funding from NSERC, CANSSI, CFI, CFREF, and Mitacs, his research has been published in highly regarded journals, including 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. Further details can be found 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