About Me
I am a 5th year PhD Candidate in applied mathematics at the Courant Institute of Mathematical Sciences, New York University, advised by Georg Stadler. My research focuses on uncertainty quantification, particularly Bayesian inverse problems that arise from PDE-governed systems. These are parameter estimation problems that are often infinite-dimensional, so much of my research is aimed at deriving methods that are computationally feasible even in very high dimensions.
Most recently, I have been working on developing efficient methods of marginalizing out the effects of unknown hyperparameters on PDE-governed Bayesian inverse problems. This work will be on the arxiv in Spring 2026.
The first four years of my PhD were supported by the Department of Energy’s Computational Sciences Graduate Fellowship. Prior to that, I completed my B.S. in Mathematics with Computer Science at MIT. I expect to finish my PhD in Spring 2027, and I will be on the academic job market this fall.
Recent and Upcoming Talks and Posters
SIAM Annual Meeting 2026 (invited), Cleveland, OH, July 2026
ICERM Workshop on Bayesian Inverse Problems and UQ, Providence, RI, March 2026 (poster)
IMSI Workshop on Data Assimilation and Inverse Problems for Digital Twins, Chicago, IL, October 2025 (poster)
DOE CSGF Outgoing Fellow Presentation, Washington D.C., July 2025 (slides, video)
SIAM Conference on Computational Science and Engineering (invited), Fort Worth, TX, March 2025 (slides)
Mid-Atlantic Numerical Analysis Day, Temple University, Philadelphia, PA, November 2024