I am a postdoctoral research associate and lecturer in the Department of Mathematics and Statistics at UMass Amherst working with Markos Katsoulakis, Luc Rey-Bellet, and Paul Dupuis. My research lies at the intersection of computational statistics and computational dynamics. I enjoy studying how these two fields interact with and complement each other for predictive modeling and uncertainty quantification.

My current research interests include mathematics of generative modeling, rare event simulation for dynamical systems, and sampling methods for Bayesian computation.

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.

NEW COURSE for Spring 2024! Math 590STA: Intro to Mathematical Machine Learning

Join us for MATH 590STA, an introduction to mathematical machine learning! We will be covering classical solutions to machine learning tasks such as regression, classification, and dimension reduction from fundamental mathematical concepts.

Learning Learning

I am the co-organizer of the Learning Learning seminar, along with Hyemin Gu. This is an internal seminar at UMass Amherst where graduate students and postdocs discuss latest developments in machine learning and data science through reading groups and tutorials. It is also a venue for students to present their research. Please contact us if you wish to participate in the group!

Recent news & upcoming events

February: I am co-organizing a minisymposium at SIAM UQ 2024 titled Optimal Transport for Uncertainty Quantification with Panagiota Birmpa. I will also be presenting in the Computational Transport minisymposium where I will be presenting on our recent work relating mean-field games with generative modeling.

I am excited to announce our new preprint title Wasserstein proximal operators describe score-based generative models and resolve memorization. We that score-based generative models can be fundamentally understood as the Wasserstein proximal operator of cross-entropy and we build informed models that resolve the memorization phenomenon in SGMs. This is joint work with Siting Liu, Wuchen Li, Markos Katsoulakis, and Stan Osher.

News archive.