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.
|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|