Online & In-Person
Join us at 12:00 PM on July 10th online via Zoom or in-person at the Clinical Sciences Building (CSB), 2-193 on the U of A campus. Registration is required for both virtual and in-person attendance. Zoom link available through registration.
Free Lunch
If you are attending in-person and would like to enjoy our complimentary lunch, please fill out our lunch form. To keep our iSMART Talks green, we request that you please bring your own water bottle.
Please note that, unfortunately, we are unable to accommodate allergy-specific requests for this event. Selection and quantities are limited, and availability may vary throughout the event.
Meet the Speaker
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 (and Adjunct Professor 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 many journals. He was elected a Fellow of the AAAI, has been awarded a McCalla Professorship and a Killam Annual Professorship; and 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 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 prizes. The main foci of his current work are (1) bio- and medical- informatics; (2) survival prediction; and (3) formal foundations of learnability.
Learning Models that Predict Objective, Actionable Labels
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.
























