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

My Publications in Public Archives

Selected Papers and R Packages

• Z-residual: Computing Z-residual for survival models and generalized linear models. (under development) A demonstration for checking covariate functional forms, Github page.
• HTLR: Fitting Bayesian Logistic Regression with Heavy-tailed (Hyper-Lasso) Priors, [CRAN page], [Github page].
• NRSP: Computing NRSP residuals (Z-residuals) for survreg and coxph objects, the page for downloading
• Wu, T., Feng, C., Li, L. (2024). Cross-validatory Z-Residual for Diagnosing Shared Frailty Models. The American Statistician 0, 1–17. [PDF]; [Free Reprints]; [slides]; [Z-residual on Github]; [Demo]
• Wu, T., Li, L., Feng, C. (2024). Z-residual diagnostic tool for assessing covariate functional form in shared frailty models. Journal of Applied Statistics 0, 1–31. [PDF]; [Z-residual on Github]; [Demo] [slides]
• Li, L., Wu, T., Feng, C. (2021). Model diagnostics for censored regression via randomized survival probabilities. Statistics in Medicine 40, 1482–1497. [PDF]; [R Functions and Demonstration]; [slides];
• Feng, C., Li, L., Sadeghpour, A. (2020). A comparison of residual diagnosis tools for diagnosing regression models for count data. BMC Medical Research Methodology. 20(1):175.
• Dong, M., Li, L., Chen, M., Kusalik, A., Xu, W. (2020). Predictive analysis methods for human microbiome data with application to Parkinson's disease. PLOS ONE 15(8):e0237779.
• Jiang, L., Greenwood, CMT, Yao, W., Li, L. (2020). Bayesian Hyper-LASSO Classification for Feature Selection with Application to Endometrial Cancer RNA-seq Data. Scientific Reports 10(1):9747.
• Li, L., Qiu, S., Zhang, B., and Feng, C.X. (2016). Approximating Cross-validatory Predictive Evaluation in Bayesian Latent Variables Models with Integrated IS and WAIC, Statistics and Computing, 26(4), 881-897. [PDF]; [slides].
• Yao, W. and Li, L. (2014). A New Regression Model: Modal Linear Regression. Scandinavian Journal of Statistics, 41(3), 656-671. [PDF].
• Li, L. (2012). Bias-corrected Hierarchical Bayesian Classification with a Selected Subset of High-dimensional Features. Journal of American Statistical Association, 107:497, 120-134. [PDF]; [software]; [slides].

Summary

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

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

Prof. Li’s teaching is featured by incorporating cutting-edge computational techniques and real-world examples into classes. He introduces his students to computational tools such as R, R Markdown, 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 classes on introductory and applied statistics and provided the source code for students to learn these tools. He employs computational simulation and animation tools to elucidate statistical theory and computer algorithms. He is also committed to using real-world examples in his classes. He actively involves undergraduate students in his research. For instance, he used data on SK’s COVID-19 vaccination and hospitalization rates to demonstrate the power of Bayes' rule for understanding 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 build R packages for machine learning. 

Selected Courses

• STAT 245: Introduction to Statistical Methods
• STAT 342: Mathematical Statistics
• STAT 345: Design and Analysis of Experiments
• STAT 348: Sampling Techniques
• STAT 812/420: Computational Statistics
• STAT 851/443: Linear Statistical Models

brain disorder disease microbiome data model diagnostics statistical machine learning

Summary

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.

Many agencies including NSERC, CFI, CFREF, and MITACS have supported his research. To date, he has supervised the research of 3 postdoctoral fellows, 23 graduate students, and 14 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 available on CRAN, GitHub, and his website.

Externally Funded Research Projects

• Statistical Methodologies and Computational Tools to Identify Microbial Correlates of Canadian Bee Gut Health, Collaborative Research Team Projects – Project 29, Co-PI, 2025–2028.
• Geospatial Artificial Intelligence Algorithms for Automating Manual Observation Associated with Wheat Production, MITACS Accelerate Grant, PI. 2022-2025.
• Develop a web-based geospatial artificial intelligence framework to track, visualize, analyze, model, and predict infectious disease spread in real-time, MITACS Accelerate Grant, PI, 2020-2021.
• Predictive Methods for Analyzing High-throughput and Spatial-temporal Data, NSERC Individual Discovery Grant, 2019-2024, PI.
• Genotype & Environment to Phenotype, sub-project from Canada First Research Excellence Fund (CFREF) Project "Designing Crops for Global Food Security",  2016-2019, Co-Investigator (PI: Prof. Kusalik).
• Applications of Neural Network Curve Fitting Methods for Least-squares Monte Carlo Simulations in Financial Risk Management, MITACS Accelerate Internship Fund, 2016, PI.
• Bayesian Methods for High-dimensional and Correlated Data, NSERC Individual Discovery Grant, 2014 - 2019, PI.
• Efficient Bayesian Analysis for Complex Models, NSERC Individual Discovery Grant, 2009 - 2014, PI.
• A Computer Cluster for Research on Efficient Bayesian Statistical Methods, CFI Leaders Opportunity Fund, 2009, PI.
• Clustering Analysis for Detecting the Types of Vehicles, MITACS Accelerate Grant, 2008, Co-PI with Prof. Laverty.