• Professor, Dept. of Electrical and Computer Engineering
  • Canada CIFAR AI Chair, MILA
  • Member of the McGill Centre for Intelligent Machines
  • Research Director of the Probabilistic Vision Group, and Electrical & Computer Engineering "Medical Imaging Lab"
  • Editor-in-Chief, Journal on Machine Learning for Biomedical Image Analysis (MELBA)
  • Associate Member of MILA
  • Associate Member of Goodman Cancer Research Centre
  • Member of the Ordre des ingenieurs du Quebec
  • P.Eng.

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.

Community Activities and News
Editor-in-Chief, Journal Launch Editorial Executive Board Member, Journal on Machine Learning for Biomedical Image Analysis (MELBA) 2020 - present
Canadian AI CIFAR Chair Recipient, MILA 2019-2024
Invited Keynote Speaker, 11th Israel Machine Vision Conference (IMVC), "Modelling and Propagating Uncertainties in Machine Learning for Medical Image of Patients with Neurological Diseases", Virtual Conference Oct. 2020

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