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Brown UniversityPhD, MS — Computational Neuroscience / Cognitive ScienceSep 2011 — Jul 2016Neuroscience may inform how to close the gap between human-level and machine intelligence. Conversely, working towards artificial general intelligence offers new insights as we try to better understand the human brain and mind. Feed-forward models of the visual cortex form a broad family that includes deep neural networks. My research has focused on extending them with context-integrating mechanisms, modeled as recurrent circuitry inspired from neurophysiology. This involved a wide range of computational techniques, including simulating the dynamics of neural populations, large-scale hyperparameter search on a high-performance computing cluster, and hand-designing CUDA kernels. Main paper here. Work done in the (awesome) Serre Lab. |
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École PolytechniqueDiplôme d'Ingénieur, MS, BS — Mathematics & Theoretical PhysicsSep 2008 — Aug 2011I studied mathematics, physics, chemistry, and litterature as part of the two-year preparatory curriculum ("classes prépa") for the competitive examinations that gate admission to the French grandes écoles, continuing after admission. Coursework highlights: linear algebra; topology; probability theory; functional analysis; dynamical systems theory; general relativity; quantum mechanics; statistical mechanics; systems neuroscience. I also tutored fellow students in Mandarin Chinese and English. |
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University College LondonResearch Intern — Gatsby Computational Neuroscience UnitApr 2011 — Aug 2011Computational models of horizontal connections in the primary visual cortex. |