New medical devices and therapies stand to improve human health outcomes the world over. Though innovating in this space is challenging, predictive simulations provide a promising path forward. I am a computational scientist who clears these paths via new data-driven models, algorithms, and extreme-scale software. Recent examples include the most efficient sub-grid model for simulating cavitation, a low-order model for cell-scale blood flow, and MFC, my open-source multi-phase flow solver. These developments guide biomicrofluidic device design and improve treatment outcomes (e.g. burst-wave lithotripsy).
I am a Senior Postdoctoral Scholar at the California Institute of Technology, working with Professor Tim Colonius. I also work with Professor Themis Sapsis at the Massachusetts Institute of Technology on machine-learned model closures. Previously, I was a Postdoctoral Researcher at the Center for Exascale Simulation of Plasma-Coupled Combustion (XPACC). I have a Ph.D. and M.S. in Theoretical and Applied Mechanics from the University of Illinois at Urbana–Champaign (2017 and 2015), where I worked with Professor Jonathan Freund. I hold B.S. degrees in Mechanical Engineering and Mathematics from the University of Michigan–Dearborn (2013).
Preprint submitted on data assimilation for rheometric data!
CAV2021: 11th International Symposium on Cavitation
180th Meeting of the Acoustical Society of America
25th International Congress on Theoretical and Applied Mechanics