With Affan Shoukat, Lisa Lix, and Gemma Postill
Chaired by Daniel Lizotte
October 26, 2026, 12:00-1:30 PM ET

A practical walkthrough of the methods in use across Canadian public health today. Methods to be discussed include: health data and biostatistics, predictive modelling, machine learning and forecasting with responsible AI and governance woven throughout.

Dr. Lisa Lix is a Professor of Biostatistics in the College of Community and Global Health at the University of Manitoba, Canada, a Tier 1 Canada Research Chair in Methods for Electronic Health Data Quality, and Director of Data Science in the George & Fay Yee Centre for Healthcare Innovation at the University of Manitoba. Her research expertise lies in statistical and machine learning methods for complex healthcare data and patient-reported outcome measures.

Gemma Postill is an MD/PhD candidate at the University of Toronto. Her research focuses on the application of machine learning and predictive modelling to population health. Her doctoral thesis investigates the use of machine learning to predict long-term outcomes among older adults following traumatic injury, to enable improved anticipation of patient outcomes. Gemma’s thesis work has received support from the Vanier Canada Graduate Scholarship and the AI4PH graduate scholarship. Gemma is also recipient of the 2026 Adel S. Sedra Distinguished Graduate Award, which recognizes her leadership beyond the graduate program. Specifically, Gemma also serves as the Student Education Co-Lead at the Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), where she contributes to initiatives advancing AI literacy and education among future and current healthcare professionals at the University of Toronto and beyond.

Dr. Affan Shoukat is an SGI Assistant Professor in Data Science in the mathematics department and an adjunct professor in computer science at the University of Manitoba. He leads a multi-national team, developing the Hybrid Epidemiological Networks platform, an innovative framework that blends advanced AI techniques with long-established epidemiological modelling, supported by NSERC, CIHR, and the French National Research Agency (ANR). His research focuses on how AI tools can be used to help track, understand, and predict infectious disease transmission.

Dr. Daniel Lizotte is Associate Professor in the Department of Computer Science and the Department of Epidemiology and Biostatistics at Western. His research aims to support health decision-making by developing and applying machine learning and statistical tools to new sources of data including electronic health records and social media to better support patients and health professionals, particularly in public health and primary health care. His methodological research combines machine learning, optimal sequential decision-making, and multiple objective optimization.

With David Buckeridge, Joon Lee, Nate Osgood, and Tracie Risling
November 2026 (full date & time coming soon).

Session information coming soon.

Professor Laura Rosella introduces the core tenets of AI4PH: how AI is being used across the public health system, the opportunities and risks, and why responsible AI and health equity are central to how this technology must be applied.

For those that attended the session, please find the evaluation form below. The session was recorded and a recording will be available here in French and English in due course.

SLIDO – Evaluation Form [click here]

Making Connections Form is here [click the link]

The report launch event took place on May 22nd 2024. Please check back soon for the report response and Q&A from the event. 

This report is based on the discussion and findings from a workshop organized by AI4PH and Statistics Canada that took place in October, 2023.

The 2023 workshop focused on creating connections and understanding around national data, artificial intelligence (AI) and public health. Workshop participants were from the national agencies working on public health (Statistics Canada, the Public Health Agency of Canada, Health Canada and the Canadian Institution of Health Information). Workshop themes include data availability, linkage and standards, modern analytical infrastructure, building capacity for AI work, collaboration between organizations, community engagement and the importance of considering bias and equity in AI applications for public health.

The workshop was funded by the CIHR Planning and Dissemination Grants through the Institute of Community Support competition.

This report includes an executive summary and recommendations, a detailed outline of the workshop sessions, discussion of alignment between academic and government perspectives, the workshop method, and a synopsis of attendee discussions.