Professor Tal Arbel

Prof. Tal Arbel

Biography

Tal Arbel is a Professor in the Department of Electrical and Computer Engineering, where she is the Director of the Probabilistic Vision Group and Medical Imaging Lab in the Centre for Intelligent Machines, McGill University. She is a Canada CIFAR AI Chair - MILA (Montreal Institute for Learning Algorithms) and Associate Member of the Goodman Cancer Research Centre.

Prof. Arbel's research focuses on development of probabilistic, deep learning methods in computer vision and medical image analysis, for a wide range of real-world applications involving neurological diseases. For example, the machine learning algorithms developed by her team for the detection and segmentation of lesions in brain MRI of patients with Multiple Sclerosis (MS) have been used in the clinical trial analysis of almost all the new MS drugs currently used worldwide. She is a recipient of the 2019 McGill Engineering Christophe Pierre Research Award. She regularly serves on the organizing team of major international conferences in computer vision and in medical image analysis (e.g. MICCAI, MIDL, ICCV, CVPR). She was an Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and Computer Vision and Image Understanding (CVIU). She is currently the Editor-in-Chief and co-founder of the arXiv overlay journal: Machine Learning for Biomedical Imaging (MELBA).

Research Interests

My research goals are to develop new probabilistic machine learning frameworks in computer vision and in medical imaging, particularly in the context of neurology and neurosurgery. This includes the development of probabilistic graphical models for pathology (lesion, tumour) detection and segmentation in large, multi-center patient images dataset, on automatically identifying imaging biomarkers that predict disease progression in patients as well as potential responders to treatment. I have worked extensively on developing fast and efficient multi-modal image registration techniques for clinical interventions, such as image-guided neurosurgery.

Key topics of interest: Bayesian inference, statistical models, statistical pattern recognition, information theory, face detection and trait classification, medical image analysis, neurology and neurosurgery, including multi-modal image registration and lesion and tumour, detection, segmentation, classification and prediction.

Professional Affiliations

new-mila

Core Member, MILA

Québec AI Institute

2024 - present

CIFAR

Canadian AI CIFAR Chair, MILA

Montreal Institute for Learning Algorithms

2019 - 2024, 2025 - 2029(renewed)

cae

Fellow

Canadian Academy of Engineering

2024 - present

gci

Associate Member

Goodman Cancer Institute

2019 - present

MELBA Journal

Editor-in-Chief, MELBA Journal

Machine Learning for Biomedical Imaging

2020 - present

CVPR Conference

Member, ILLS

International Laboratory on Learning Systems

2022 - present

Media

Contact

Contact Information

Address:
McGill University
Department of Electrical and Computer Engineering
Centre for Intelligent Machines
3480 University Street, Room 425
Montreal, Quebec, H3A 0E9

Connect

For research collaboration inquiries, please contact via email with the subject line "Research Collaboration".