Learning Models that Predict Objective, Actionable Labels: Vector Institute AI/ML Talk
Join the Vector Institute for an AI/ML Talk!
Dr. Russell Greiner
Alberta Machine Intelligence Institute
Professor in Computing Science
University of Alberta
Talk title: “Learning Models that Predict Objective, Actionable Labels”
Agenda
- 2:00 - 3:00 pm - Talk
- 3:00 - 4:00 pm - Meet & greet
Where and when
Thursday, November 13, 2025
2 - 4 pm
Hybrid
Presentation Abstract
Many medical researchers want a tool that “does what a top medical clinician does, but does it better”. This presentation explores this goal. This requires first defining what “better” means, leading to the quest for outcomes that are “objective” and then to ones that are “actionable”, with a meaningful evaluation measure. We will discuss some of the subtle issues in this exploration – what does “objective” mean, the role of the (perhaps personalized) evaluation function, multi-step actions, counterfactual issues, distributional evaluations, etc. Collectively, this analysis argues we should learn models whose outcome labels are objective and actionable, as that will lead to tools that are useful and cost-effective.
About the speaker: Dr. Russell Greiner
After earning a PhD from Stanford, Russ Greiner worked in both academic and industrial research before settling at the University of Alberta, where he is now a Professor in Computing Science (Adjunct in Psychiatry) and the founding Scientific Director of the Alberta Machine Intelligence Institute. He has been Program/Conference Chair for various major conferences, and has served on the editorial boards of a number of relevant journals. He was elected a Fellow of the AAAI (Association for the Advancement of Artificial Intelligence), was awarded a McCalla Professorship and a Killam Annual Professorship; in 2021, received the CAIAC Lifetime Achievement Award and became a CIFAR AI Chair. In 2022, the Telus World of Science museum honored him with a panel, and he received the (UofA) Precision Health Innovator Award, then in 2023, he received the CS-Can | Info-Can Lifetime Achievement Award. In 2024, he shared the Brockhouse Prize with David Wishart, for their joint work on "Machine Learning for Metabolomics". For his mentoring, he received a 2020 FGSR Great Supervisor Award, then in 2023, the Killam Award for Excellence in Mentoring. He has published over 350 refereed papers, most in the areas of machine learning and recently medical informatics, including 6 that have been awarded Best Paper prizes. The main foci of his current work are (1) bio- and medical- informatics; (2) survival prediction and (3) formal foundations of learnability.