Ph.D. student at the Robotics Institute at Carnegie Mellon University advised by Jeff Schneider.
Newell-Simon Hall 3128
5000 Forbes Ave
Pittsburgh, PA 15213
As a researcher, I am broadly interested in reinforcement learning, generative models, and dynamical systems. In particular I work on solving control problems in science with machine learning in regimes where the data-generating process is expensive. Much of my work is motivated by the problem of plasma control for nuclear fusion; the various difficulties we face there frequently inspire more general machine learning problems we can solve as computer scientists. My fusion research is conducted in collaboration with the Princeton Plasma Physics Laboratory.
Prior to my time at CMU, I spent time at KKR helping jumpstart their efforts with alternative data and analytics and at Hum Capital (formerly Capital Technologies) doing initial work on automating parts of capital allocation in private finance.
I completed a B.S. in Mathematics and an M.S. in Computer Science at Stanford University, where I conducted research on 3D vision and robot learning advised by Silvio Savarese.
I’m from Austin, Texas and spend my time bouncing between there, Pittsburgh, and sometimes NYC, the Bay Area, and Tahoe. Outside of work, I spend my time flying airplanes, lifting weights, reading, and skiing.
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical PredictionIn IEEE Conference on Decision and Control 2021
Representational aspects of depth and conditioning in normalizing flowsIn International Conference on Machine Learning 2021
An Experimental Design Perspective on Model-Based Reinforcement LearningIn International Conference on Learning Representations 2022
Near-optimal Policy Identification in Active Reinforcement LearningIn International Conference on Learning Representations (oral, top 5% of accepted papers) 2023
Controlling Plasma Profiles in a Learned Model via Reinforcement LearningIn American Physical Society Division of Plasma Physics Annual Meeting Nov 2021
Exploration via Planning for Information about the Optimal TrajectoryIn Advances in Neural Information Processing Systems 2022