2026 ASA Traveling Short Course

“Tree-Based Machine Learning Methods: Prediction, Inference, and Variable Selection with the randomForestSRC Ecosystem”

Instructors:

Dr. Hemant Ishwaran
Professor of Public Health Sciences
Division of Biostatistics, University of Miami

Dr. Min Lu
Research Associate Professor
Division of Biostatistics, University of Miami

Date and time: Saturday, September 26, 2026, from 9:00 a.m. to 1:00 p.m. PDT
Format: Virtual
Registration: Eventbrite link

The registration deadline is Thursday, September 24, 2026, at 11:59 p.m. PDT. The Zoom link will be posted on the Eventbrite event page and emailed to all registered participants one to two days before the workshop.

Registration fees:

• Students: $25 plus processing fee

• Academic participants (non-students): $50 plus processing fee

• Non-academic participants: $75 plus processing fee

 Course Description: 

This half-day virtual workshop is designed for applied statisticians and data scientists who want hands-on R workflows for prediction, inference, and variable selection. It provides a practical, code-centered introduction to tree-based machine learning methods in R. Tree-based methods are useful for nonlinear signals, mixed data types, robustness, and scalable prediction. The workshop focuses on random forest ensembles and centers on the R package randomForestSRC, which implements random forests for regression, classification, and survival. Topics include out-of-bag inference, test-data prediction, and variable selection using permutation VIMP, minimal depth, and rule-based variable priority with varPro. Advanced topics cover class imbalance, imputation, and test-time OOD, random hazard forests (RHF), and super greedy trees (SGT). Related packages in the randomForestSRC ecosystem will also be discussed. 

Workshop Outlines: 

1. Training

 ○ Regression, Classification, Survival 

○ Examples and R code 

○ Worked interpretations 

2. Inference and Prediction 

○ Out-of-bag inference 

○ Test-data prediction 

○ Restore mode and partial plots 

3. Variable Selection 

○ Permutation VIMP 

○ Minimal depth 

○ Variable priority with varPro 

4. Advanced Topics ○ Class imbalance 

○ Imputation and test-time OOD 

○ Random Hazard Forests 

About the Instructors: 

Dr. Hemant Ishwaran is a Professor of Public Health Sciences, Graduate Program Director, and Director of Statistical Methodology, Division of Biostatistics, University of Miami. Hemant Ishwaran develops machine learning methods for complex biomedical and time-to-event data and turns them into practical open-source tools for investigators. He created Random Survival Forests and the R package randomForestSRC. His work has been applied in cardiovascular disease, heart transplantation, cancer, and genomics. 

Dr. Min Lu is a Research Associate Professor in the Division of Biostatistics at the University of Miami. Min Lu works on random forests and trees, causal inference, variable selection, infectious disease modeling, statistical genomics, and meta-analysis. She develops methods and software in the randomForestSRC and varPro ecosystem and collaborates on practical applications in medicine and public health.