The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.

Abstract: While survival models have traditionally focused on discrimination -- accurately ranking patient risks (e.g., prioritizing patients for a rare transplant), there is a growing interest in improving calibration performance, reflecting the alignment of predicted probabilities with the actual distribution of observations. As these measure distinct aspects of a model, it is hard for models to optimize both objectives simultaneously -- indeed, many previous results found improving calibration tends to diminish discrimination performance. This talks introduces two novel approaches, which use conformal prediction to disentangle calibration from the discrimination, allowing us to improve a model’s calibration without degrading its discrimination. We provide theoretical guarantees for this claim and demonstrate its effectiveness over diverse scenarios.

Presenter Bio: Shi-ang Qi is a Ph.D. candidate in the Department of Computing Science at the University of Alberta, supervised by Dr. Russell Greiner. His research focuses on machine learning for healthcare, survival analysis, causal inference, and recommendation systems. He collaborates with multidisciplinary teams to develop and deploy AI-driven solutions for real-world challenges across healthcare, finance, and technology. His work blends theory and application, showcasing his leadership in translating research into impactful, practical innovations.