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).
Paper on QBMMlib was published by SoftwareX after revision. It's open access, check it out!
13 August, 2020Preprint released on QBMMlib, our open-source solver for quadrature moment methods!
11 August, 2020Preprint submitted on data assimilation for rheometric data!