About
I am a postdoctoral research associate in the School of Data Science and Society at the University of North Carolina at Chapel Hill working with Amarjit Budhiraja. My research interests lie broadly in the intersections of mathematics of generative machine learning, mathematical control theory, and Bayesian computation.
I will be joining the Department of Mathematics at Rutgers University as an Assistant Professor in Fall 2026.
I was previously a postdoctoral research associate between the Division of Applied Mathematics at Brown University and the Department of Mathematics and Statistics at UMass Amherst with Paul Dupuis, Markos Katsoulakis, Luc Rey-Bellet.
I earned my PhD in Computational Science and Engineering from MIT in 2022. My advisor was Youssef Marzouk who heads the Uncertainty Quantification group. I earned my Master’s degree in Aeronautics & Astronautics at MIT in 2017, and my Bachelor’s degrees in Engineering Physics and Applied Mathematics at UC Berkeley in 2015. I was a MIT School of Engineering 2019-2020 Mathworks Fellow. I spent the summer of 2017 as a research intern at United Technologies Research Center (now Raytheon), where I worked with Tuhin Sahai on novel queuing systems.
Recent News
September
Happy to announce that our paper Optimal Control for Transformer Architectures: Enhancing Generalization, Robustness and Efficiency. has been accepted to NeurIPS 2024 through the Main Track!
New preprint on applications of generative modeling to operator learning! In Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations, we place operator learning within the framework of probabilistic machine learning through a random differential equations formalism. We show that in-context operator learning (ICON) performs Bayesian inference implicitly and demonstrate how generative modeling provides uncertainty quantification for predictions made by ICON. This is joint work with Siting Liu, Stan Osher, and Markos Katsoulakis.
Recent and upcoming events
April
Data Science and Statistics Seminar, Department of Mathematics, University of Tennessee, Knoxville. April 9, 2026.
March
SIAM conference on Uncertainty Quantification 2026
Computational and Applied Mathematics Seminar, School of Mathematical and Statistical Sciences, Arizona State University, March 16, 2026
December
Virtual Analysis and PDE Seminar, December 11, 2025.
November
Colloquium, Department of Mathematics, Louisiana State University, November 20, 2025.
Special Colloquium, Department of Mathematics, Rutgers University, November 18, 2025.