Probabilistic Theories of Deep Learning from first principles, Neurally-inspired learning and computation, Medical Imaging Diagnosis, Reverse-engineering neocortex, Deep Learning for Particle Physics
Dr. Patel is pursuing the unification of traditional hierarchical machine learning with deep neural networks, with applications to a variety of fields, including neuroscience, robotics, and particle physics. In this vein, he is designing and building the first generative convolutional net, which promises to enable (1) the training of sophisticated deep vision models from large quantities of unlabeled data, and (2) the execution of top-down inference for tasks in which fine-scale information is important (e.g. segmentation, pose estimation). He is also working with visual neuroscientists to build a bridge between machine learning models and real neural networks, using the latter to make testable predictions about the former. And finally, he is working with physicists at the Large Hadron Collider to build efficient new algorithms to separate signal from noise, in search of New Physics beyond our best generative model of the Universe thus far -- the Standard Model of Particle Physics.
Publications/Creative Works
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Affiliations
Research Consortia
Gulf Coast Consortium for Theoretical and Computational Neuroscience
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