Hello, and welcome to my site! I am a third year PhD student at the University of Texas at Austin working with Dr. Ufuk Topcu. I previously received my BS in computer engineering from Iowa State University. My recent research is in reinforcement learning and transfer learning. In particular, I have been applying the logic of Hilbert spaces to machine learning problems, and I have found it to be an excellent approach to achieving transfer in all sorts of problems!

Selected Publications

Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces [Under Review]

We introduce several improvements to the function encoder algorithm, prove a universal function space approximation theorem for function encoders, and demonstrate that the function encoder outperforms SOTA on several inductive transfer learning tasks.

Basis-to-Basis Operator Learning Using Function Encoders [CMAME 2024]

Basis-to-Basis operator learning is a novel method based on learned basis functions that achieves state-of-the-art performance in operator learning tasks.

Zero-Shot Transfer of Neural ODEs [NeurIPS 2024]

This work combines the recent advances in learned basis functions with neural ODEs, allowing for online transfer of learned system models at execution time without retraining.

Zero-Shot Reinforcement Learning via Function Encoders [ICML 2024]

By representing the context of a reinforcement learning problem using function encoders, basic reinforcement learning algorithms can achieve excellent zero-shot transfer in multi-task, multi-agent, and hidden-parameter reinforcement learning problems.