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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. Recent work has focused on the development of probabilistic graphical models for pathology (lesion, tumour) detection and segmentation in large, multi-center patient images dataset as well as on modeling and conveying uncertainties in deep learning models. I have worked extensively on developing fast and efficient multi-modal image registration techniques for clinical interventions, such as image-guided neurosurgery. Recent work focuses on automatically identifying imaging biomarkers that predict disease progression in patients with Multiple Sclerosis as well as identifying potential responders to treatment. This includes developing spatio-temporal models for disease evolution which include clinical and imaging information in learning and inference.
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