Gokul Swamy

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Hi there! I’m Gokul, a PhD candidate in the Robotics Institute at Carnegie Mellon University working on interactive learning from implicit human feedback (e.g. imitation/RLHF). I work with Drew Bagnell and Steven Wu. I completed my B.S. / M.S. at UC Berkeley, where I worked with Anca Dragan on Learning with Humans in the Loop. I’ve spent summers working on ML @ SpaceX, Autonomous Vehicles @ NVIDIA, Motion Planning @ Aurora, and Research @ Microsoft and @ Google. In my free time, I do origami, hackathons, and run/lift. I’m a huge fan of birds (especially lovebirds), books (especially those by Murakami), and bands (especially Radiohead). If you’d be interested in working with me, feel free to shoot me an email! July 2024 - Four workshop papers at ICML’24! One on what is fundamentally different about multi-agent imitation learning, one on REBEL: a scalable and theoretically elegant RLHF algorithm., one on the differences between online and offline preference fine-tuning algorithms, and on

Hi there! I’m Gokul, a recent PhD graduate from Carnegie Mellon University’s Robotics Institute, working on the algorithmic foundations and science of interactive decision-making. I work on efficient interactive learning algorithms for training agents (e.g., robots, language models). More fundamentally, I am interested in techniques for learning to make good decisions efficiently, even when “good” is hard to specify. I value closing the theory-practice loop: my research proceeds in cycles of deeply understanding empirical phenomena , making algorithmic advancements , and deploying my ideas bro

Gokul Swamy

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