Prof. Longhai Li received his Ph.D. degree in statistics from the University of Toronto. Before that, Dr. Li received a B.Sc honours degree in statistics from the University of Science and Technology of China. His research activities focus on developing and applying statistical learning methods for high-throughput data and complex-structured data.  For more information about Dr. Li, click his website hosted on Github: https://longhaisk.github.io

Find my pulications from: zotero profile, google scholar, researchgate, web of science, semantic scholar, arXiv. 

Prof. Li has taught a diverse array of statistics courses at the University of Saskatchewan, organized into three main areas:

1. Introductory and Applied Statistics: STAT 244, STAT 245, STAT 345, STAT 348, STAT 848
2. Probability and Statistical Theory: STAT 241, STAT 242, STAT 342, STAT 442, STAT 443/STAT 851, STAT 841
3. Computational Statistics: STAT 812/STAT 420

Integrating advanced computational techniques and real-world examples features Prof. Li’s teaching. He has introduced tools such as R, R Markdown, GitHub, and cloud storage for analyzing real datasets, developing statistical packages, and sharing analysis results. He has created web pages using R Markdown for all his introductory and applied statistics classes, providing the source code for students to learn these tools.  He employs computational simulations and animation tools to elucidate statistical and probability theory and numerical algorithms. He is also committed to using real-life examples and datasets in his class. For example, he used data on SK’s COVID-19 vaccination and hospitalization rates to demonstrate the power of Bayes' rule and the efficacy of vaccination. 

He actively involves undergraduate students in his research. In 2021, he led a team supported by MITACS to develop a public website that provides real-time reproduction rates for Canada’s national and provincial jurisdictions. He has also supervised undergraduate students to develop R packages for machine learning. 

brain disorder disease microbiome data model diagnostics statistical machine learning

Prof. Li's research focuses on developing and applying statistical machine-learning methods to analyze high-throughput and complex-structured data. His primary research areas include (1) Residual Diagnostic Methods: Developing innovative techniques to assess the adequacy of statistical models. (2) Predictive Modeling and Feature Selection: Creating new tools to identify truly predictive features, and build more precise predictive models for linking human diseases and high-dimensional bioinformatic signatures.

His research has been supported by many agencies including NSERC, CFI, CFREF, and MITACS. To date, he has supervised the research of 3 postdoctoral fellows, 20 graduate students, and 8 undergraduate students.  His research findings have been published in prestigious journals such as the Journal of the American Statistical Association, Bayesian Analysis, Statistics in Medicine, Statistics and Computing, the American Statistician, the Journal of Applied Statistics, Scientific Reports, and BMC Bioinformatics. He has also developed several R packages and functions, which are available on CRAN, GitHub, and his website.