I am interested in how learning algorithms can enable machines to acquire general notions of intelligence, allowing them to autonomously learn a variety of complex sensorimotor skills in real-world settings. This includes learning deep representations for representing complex skills from raw sensory inputs, enabling machines to learn on their own, without human supervision, and allowing systems to build upon what they've learned previously to acquire new capabilities with small amounts of experience. I received a PhD in computer science at UC Berkeley in 2018, working with Sergey Levine and Pieter Abbeel on deep learning algorithms for meta-learning, reinforcement learning, imitation learning, and inverse reinforcement learning. I received my BS in electrical engineering and computer science at MIT in 2014.
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