I am a Postdoctoral Researcher at the Data Science Institute, University of Chicago, working with Dr. Rebecca Willett. My current work explores the learnability of non-stationary dynamical systems motivated by applications in data assimilation task such as weather forecasting.

In December 2025, I obtained my PhD in Computer Science from Johns Hopkins University, where I was advised by Dr. Jeremias Sulam. My doctoral research focused on theoretical guarantees and experimental evaluations of generalization and robustness of machine learning models. My work showed that accounting for structure and parsimony in the interactions between models and data yields provides a more accurate evaluation.

Prior to starting my graduate studies, I was a research assistant at Cornell University supervised by Dr. Madeleine Udell. At Ithaca, I worked on developing low-memory algorithms for PDE-optimization based on randomized sketching.

Download my resumé.