Price
$40-$490
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.
Registration opens on February 2nd, 2026.
