Overview of Longhai Li's Research Activities

Prof. Li's research focuses on developing and applying statistical machine-learning methods to analyze bioinformatics and epidemiology data. His research activities are computationally intensive, aiming to develop new data analysis software and solve real problems in life and health sciences. 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. His research activities can be summarized with the following themes/keywords:

• Model Diagnostics and Comparison: He aims to develop new tools for evaluating Bayesian/non-Bayesian models with complex structures. Today, increasingly complicated models are being proposed for a variety of correlated data such as temporal, spatial, and repeated measurement data. There is a gap between developing new modelling methods and model validation methods. He is working on developing new residual diagnostic methods for checking the adequacy of statistical models.
• Statistical Machine Learning: He aims to develop new tools for honestly measuring the predictivity (such as error rate, AUC) of selected features, and new tools for identifying truly predictive features and for building sharper predictive models for phenotypes. He is particularly interested in uncovering the molecular mechanisms behind Alzheimer’s and Parkinson’s diseases.
• Keys Words Describing His Research Interests: statistical learning, cross-validation, hierarchical modelling, survival modelling, model checking, residual diagnostics, model comparison, zero-inflated models, high-throughput data, microbiome data

Externally Funded Research Projects

Many granting agencies including NSERC, CFI, CANSSI, CFREF, and MITACS have supported his research:

• 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.