Dalla Lana School of Public Health

Links

• Tree-Based Machine Learning Methods Workshop

The Southern Ontario Toronto Chapter of the American Statistical Association and the Dalla Lana School of Public Health at the University of Toronto are pleased to host an upcoming ASA Traveling Course, “Tree-Based Machine Learning Methods: Prediction, Inference, and Variable Selection with the randomForestSRC Ecosystem.” This half-day, four-hour virtual course will take place on Friday, September 11, 2026, from 9:00 a.m. to 1:00 p.m. and is designed for applied statisticians, data scientists, researchers, and students interested in practical applications of tree-based machine learning methods.

The course will provide a hands-on, code-centered introduction using R, with a particular focus on random forests and the randomForestSRC ecosystem. Topics will include regression, classification, and survival analysis; out-of-bag inference and prediction; variable selection using permutation VIMP and minimal depth; and more advanced topics such as class imbalance, imputation, random hazard forests, and super greedy trees.

The course will be taught by Dr. Hemant Ishwaran, Professor of Public Health Sciences and Director of Statistical Methodology in the Division of Biostatistics at the University of Miami, and Dr. Min Lu, Research Associate Professor in the Division of Biostatistics at the University of Miami. Dr. Ishwaran is the creator of Random Survival Forests and the randomForestSRC R package, while Dr. Lu develops methodology and software involving random forests, variable selection, causal inference, and related applications in medicine and public health.

This should be an excellent opportunity for those interested in expanding their knowledge of modern machine learning methods and seeing how these approaches can be implemented in practical statistical applications.

Please note that ticket sales will end at 4:00 p.m. on September 10. The Zoom link will be emailed to the registrants by 8:00 a.m. on the day of the workshop. Registrants will be able to access the workshop recordings for three weeks following the event.

I encourage faculty and students with an interest in machine learning, biostatistics, data science, prediction, or survival analysis to consider attending.