I am a graduate student in Computational Science at the Oden Institute at the University of Texas at Austin. I graduated from Cornell University with a B.Sc. in Computer Science. I am currently researching large-scale Bayesian inverse problems and deep learning models applied to physical systems.

My thesis is on scalable algorithms for data-driven statistical inference of parameters in nonlinear dynamical systems. My most recent work combines dimensionality reduction, physics-informed deep learning models, and Bayesian inference to not only efficiently infer high-dimensional parameters given observational data but also quantify their uncertainty: a critical aspect of robust inference algorithms. My thesis contains a strong computational aspect in the form of applying my algorithm to perform inference for large-scale distributed nonlinear partial differential equation solvers.

I am interested in solving computational problems with an emphasis on mathematical analysis complemented by robust software engineering principles.