With David Buckeridge, Joon Lee, Nate Osgood, and Tracie Risling
Chaired by Daniel Lizotte
November 13, 2026, 12:00-1:30PM ET

AI Methods in Public Health (Part 2), will focus on advanced AI approaches and considerations for public health. Responsible AI considerations and governance principles will be integrated throughout the discussion.

Speakers will discuss simulation and systems modelling, operational AI, surveillance and health informatics applications, emerging agentic AI capabilities, and strategies for responsible implementation in public health settings. 

David Buckeridge is a Professor in the School of Population and Global Health at McGill University, the Chief Digital Health Officer and the lead for Data Science and Digital Health Research at the MUHC. He holds a Canada Research Chair (Tier 1) in Population Health Informatics and is an Associate Member with the Montreal AI Institute (Mila). He has a MD (Queen's), a MSc in Epidemiology (Toronto), a PhD in Biomedical informatics (Stanford), and is a Fellow of the Royal College of Physicians of Canada.

Tracie Risling RN, PhD is the Associate Dean Innovation and Research in the Faculty of Nursing at the University of Calgary, the President of the Canadian Nurses Association and a Past-President of the Canadian Nursing Informatics Association. With a practice background in pediatric and public health nursing, Dr. Risling currently leads a program of informatics research including study on artificial intelligence, social media and misinformation, and other digital co-design. She is a passionate advocate for increased clinician engagement in the development, use, and evaluation of digital health solutions.

Dr. Joon Lee is the Director of the Data Intelligence for Health Lab and Professor at the Cumming School of Medicine, University of Calgary. He is a health ML/AI researcher with over 20 years of experience. He holds a PhD in Biomedical Engineering from the University of Toronto and a BASc in Electrical Engineering from the University of Waterloo, and completed a Postdoctoral Fellowship in Medical Data Science at the Harvard-MIT Division of Health Sciences and Technology.

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.