I am currently a first year Ph.D. student in the Theory group at the University of Southern California fortunate to be working with Haipeng Luo, Shang-Hua Teng, and Shaddin Dughmi. Previously, I was a Research Assistant in the Machine Learning Theory Group at Cornell University and prior to this, I recieved my M.S. in Computer Science from Cornell University (2024) where I was advised by Karthik Sridharan. I recieved my B.A. in Computer Science and Mathematics from Cornell University (2022) under the supervision of Noah Stevens-Davidowitz.

Research Interests

Currently I’m interested in avenues for developing a more practical theory for machine learning. This has taken shape in my current research with projects aiming to answer the following questions:

• Relaxing Realizability: Are there practical and interpretable conditions that ensure learnability of regression-based learning paradigms—such as active learning, bandits, and reinforcement learning—beyond the classical realizability assumption?

• Adaptive Bounds for Interactive Decision Making: Given intuition that the underlying model may have some favorable property, can we design algorithms that achieve model-dependent, adaptive regret bounds—performing better on problem instances that align with this intuition while maintaining strong worst-case guarantees?

More broadly, I am interested in uncovering the theoretical foundations of machine learning.