Price
$40-$140
Members of the committee responsible for the symposium
McGill University
University of Toronto
Biomedical research is being transformed by data at unprecedented scale and complexity: population-level genomics, high-resolution biological imaging, and expansive electronic health records (EHRs) are reshaping drug development and precision medicine. Yet current statistical and computational methods struggle to meet the challenges of heterogeneity, noise, high dimensionality, and regulatory rigor. This workshop brings together statisticians, machine learning researchers, and biomedical scientists to build a shared methodological foundation for this emerging field. Core themes include high-dimensional inference in genomics, representation learning for imaging, causal discovery from EHR data, and uncertainty quantification for decision-critical applications in drug development. Through focused talks and collaborative discussions, the workshop aims to catalyze research at the intersection of statistics, AI, and biomedicine—translating complex data into rigorous, trustworthy scientific clinical advances. This symposium will be co-organized with the Centre de Recherches Mathématiques (CRM) as part of its thematic semester “Math for Health,” to be held from August to December 2026.
Full schedule
Thursday, August 20th, 2026
Daily theme – Conformal Inference & Reliable Predictions: Statistical guarantees and robustness of methods
8:30 – 8:50: Welcome, registration and Coffee
8:50 – 9:00: Welcome Address by Aurélie Labbe (IVADO, HEC Montréal), Archer Yi Yang (McGill University) and Dehan Kong (University of Toronto), Organizers of the Symposium
9:00 – 9:45: Hongtu Zhu (UNC), Causal Generalist Medical AI
9:45 – 10:30: Erica Moodie (McGill University), Statistical and causal perspectives on ML for individualized treatment strategies
10:30 – 11:00 : Coffee Break
11:00 – 11:45 : Jin Ying (University of Pennsylvania), Quantifying and limiting false positives in AI-driven therapeutic discovery
11:45 – 13:30: Lunch (not provided)
13:30 – 14:15: Xihong Lin (Harvard), Harnessing Synthetic Data from Generative AI in Statistical Inference
14:15 – 15:00: Jessie Gronsbell (University of Toronto), ML-powered scientific research: Possibilities and pitfalls
15:00 – 15:30: Coffee Break
15:30 – 16:15: Nancy R. Zhang (UPenn), Virtual Tissue Perturbations: Causal AI and Statistics in Spatial Omics
16:15 – 17:00: Martin Lindquist (Johns Hopkins University), Individualized spatial topography in functional neuroimaging
17:00 – 19:00: Social Hour/ Mixer
Friday, August 21th, 2026
Daily theme – Applications & Emerging Methods
8:30 – 8:50: Welcome, registration and Coffee
8:50 – 9:00: Welcome Address & Program for the day
9:00 – 9:45: Elena Tuzhilina (UofT), Imputing Single-Cell Contact Data via Zero-Inflated Tensor Smoothing
9:45 – 10:30: Hongyu Zhao (Yale), Integrating Large-Scale AI Models with Statistical Genetics and Genomics for Multimodal Biomedical Discovery
10:30 – 11:00: Coffee Break
11:00 – 11:45: Eugene Katsevich (UPenn), PerturbPlan: An analytical framework for designing Perturb-seq experiments
11:45 – 13:30 : Lunch (not provided)
13:30 – 14:15: Danilo Bzdok (IVADO, McGill University), Title to be confirmed
14:15 – 15:00: Tianxi Cai (Harvard), Title to be confirmed
15:00 – 15:30: Coffee Break
15:30 – 16:15: Panel discussion – all speakers
16:15 – 16:45: Wrap-up & Closing remarks with Aurélie Labbe (HEC Montréal, IVADO), president of the Scientific Committee and Christopher Pal, Director Scientifics activities
