Tal Arbel is a Professor in the Department of Electrical and Computer Engineering and an Associate Member of the School of Computer Science at McGill University, where she directs the Probabilistic Vision Group and Medical Imaging Lab within the Centre for Intelligent Machines. She is a Canada CIFAR AI Chair, a Core Member of Mila – Quebec Artificial Intelligence Institute, a Fellow of the Canadian Academy of Engineering, an Associate Member of the Goodman Cancer Institute, and a Co-Advisor for the ELLIS PhD Program.

Prof. Arbel’s research centers on the development of probabilistic deep learning methods in computer vision and medical image analysis. Her current work in causal inference, generative models, Vision-Language Models, and Agentic AI supports the safe, equitable, and trustworthy clinical deployment of personalized medicine. She has adapted her models for a wide range of real-world applications, particularly in neurological diseases. Notably, the machine learning algorithms developed by her team for detecting and segmenting lesions in brain MRIs have been utilized in the clinical trial analysis of almost all new Multiple Sclerosis (MS) drugs used worldwide.

A recipient of the 2025 McGill Bravo Award and the 2019 Christophe Pierre Award for Research Excellence, her lab’s research regularly receives Best Paper and Best Poster honors at premier venues like MICCAI and MIDL. Prof. Arbel frequently serves on the organizing committees of major international conferences (including MICCAI, MIDL, ICCV, and CVPR). She is also a co-founder of the online journal Machine Learning for Biomedical Imaging (MELBA), where she served as Editor-in-Chief for five years and currently serves as an Executive Editor.