I am a PhD Candidate in Computer Science at Princeton University advised by Chi Jin. My current research interests involve studying multi-agent interaction and open-ended learning at scale. I am interested in using foundation models (such as large language models) and reinforcement learning for decentralized decision-making that can scale individual preferences to group behavior. Previously, I received an MS in Robotics from Carnegie Mellon University, advised by Katia Sycara, where I studied emergent communication and decision-making in multi-agent teams. I obtained my undergraduate degree from Rutgers University, New Brunswick in Computer Science and Mathematics, where I received the C. Greg Hagerty Artificial Intelligence and Computer Science Award. I studied learning hierarchical control primitives under the supervision of Kostas Bekris. I previously spent some time as an Applied Scientist at Amazon studying multi-agent pathfinding (MAPF). I am a recipient of the NSF Graduate Research Fellowship and Francis Robbins Upton Fellowship. I am always excited to collaborate with others. If you are interested in open-ended learning or decentralized multi-agent teams in the era of foundation models, please do not hesitate to reach out.
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March 29, 2023: I am excited to announce that I have been awarded the National Science Foundation Graduate Research Fellowship (NSF GRFP)!
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