I'm a second-year PhD student at Boston University, where I'm advised by Aldo Pacchiano. I'm broadly interested in reinforcement learning from an algorithmic perspective, with a focus on how transfer, structure, and diversity can improve upon tabula-rasa learning in terms of sample efficiency and performance.
research —Reinforcement Learning · Representation Learning · Post-Training · Reasoning
Previously, I was a guest researcher in the Empirical Inference department at the Max Planck Institute for Intelligent Systems (MPI-IS), and a visiting scholar in the Machine Teaching Group at the Max Planck Institute for Software Systems (MPI-SWS).
tl;dr — We propose a neural CO algorithm that learns multiple complementary solution strategies.
tl;dr — We present a framework to learn embeddings for tasks in reinforcement learning.
tl;dr — We propose a sim-to-real pipeline for muscle-actuated robots that leverages learned actuation models.
tl;dr — We introduce a large-scale dataset containing complex traffic videos of unstructured scenarios in India.
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