I am a computational scientist with a background in mathematics, modeling, data science, and software engineering. I work at Sandia National Laboratories in Livermore, California in the department of Quantitative Modeling and Software Engineering (8734). Previously, I was the John von Neumann Postdoctoral Fellow at Sandia Labs in Albuquerque, NM.

My current research interests include (1) nonlocal and fractional-order coarse grained models for subsurface transport through fracture networks, (2) data-driven surrogate models for circuits and climate systems, (3) time-series forecasting of extreme weather events, and (4) verification, validation, and uncertainty quantification in support of national security. A unifying theme of my projects involves developing scientific machine learning methods for regression, model calibration, and operator learning. My work in this area has led to tools that incorporate prior physics/system knowledge within machine learning algorithms, thereby improving robustness and fidelity while accelerating training. In parallel, I’ve done foundational work on nonlocal and fractional-order vector calculus, building theoretical frameworks and stochastic methods for applying nonlocal methods to multivariate multi-scale systems.