About Me

I received B.S. degrees in Aerospace Engineering, Astronomy & Astrophysics, and Computational Physics from Penn State. During my undergraduate career, I took a year off and interned at various space and defense entities such as NASA, AGI, and Lockheed Martin. Through these experiences, I transitioned from working on spacecraft propulsion to computational physics and machine learning. 

I graduated from the University of Utah, under my advisor Prof. Mike Kirby, with a Ph.D. in Scientific Computing. The dissertation was titled "Advancing Physics-Informed Neural Networks (PINNs): Multifidelity, Metalearning, Multitask, and Multidomain Strategies." For more details on this work, please see the publications page. Currently, I am working for Sandia National Labs as a Senior Member of Technical Staff.  

At Sandia, I am Principle Investigator (PI) of internally and externally funded R&D scientific machine learning (SciML) projects. My current research interests include geometry-aware operator learning, physics foundation models,  non-parametric design optimization and design under uncertainty using surrogate models.