Research is funded by the National Science Foundation through the following awards: NSF RISE Award 2425922, NSF OAC Award 2313033, NSF DMS Award 2324643.
Research Experience
Works on numerical methods for the inference, prediction, and optimal design and control of complex physical systems, particularly those mathematically modeled by parametrized partial differential equations (PDEs). Blends numerical analysis, machine learning, and mechanistic modeling to create state-of-the-art algorithms to optimize and support decision making regarding complex systems with high-dimensional uncertainty.
Education
PhD in Computational Science, Engineering, and Mathematics from The University of Texas at Austin, supervised by Omar Ghattas and co-supervised by Patrick Heimbach; previously completed degrees in Engineering Mechanics and Mathematics at the University of Wisconsin--Madison.
Background
Computational Mathematician | Optimization, Simulation and Operator Learning. Assistant Professor in the Department of Mathematics at The Ohio State University.