I am currently a first year Ph.D. student in the Theory group at the University of Southern California fortunate to be advised by Shaddin Dughmi and Shang-Hua Teng, while also working closely with Haipeng Luo. 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:

• Learning with Auxiliary Information: Can auxiliary information, such as chain-of-thought trajectories, fundamentally alter the statisitcal complexity of learning—turning tasks that are provably unlearnable from input–output data alone into ones that become learnable once such information is observed while also accelerating learning when it is already possible?

• 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?

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