About Me
I am a PhD Candidate in applied mathematics at the Courant Institute, 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.
My current research pursues three directions centered on hierarchical Bayesian inverse problems: efficient hyperparameter marginalization (see arXiv preprint), optimal experimental design under hyperparameter uncertainty, and connections between PDE-governed methods and INLA, a widely-used approach in geostatistics. More details are on my research page.
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 am on the academic job market this fall.
Recent and Upcoming Talks and Posters
SIAM Conference on Computational Science and Engineering (co-organizing minisymposium), Pittsburgh, PA, February 2027
Numerical Analysis and Scientific Computing RTG Workshop, Rice University, Houston, TX, October 2026
SIAM Annual Meeting 2026 (invited), Cleveland, OH, July 2026 (slides)
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