Elizabeth Cherry is an Associate Professor in the School of Computational Science and Engineering. Her research involves modeling and simulation, high-performance computing, and numerical methods. In particular, her group is focused on computational modeling of cardiac arrhythmias, including model development, validation, and parameter estimation; design and implementation of efficient solution methods; implementations on traditional parallel and GPGPU architectures; integration with experiments through data assimilation; and applications to understand the mechanisms responsible for particular complex dynamical states.
Learn more about Elizabeth's professional roles:
She is a member of the editorial board of Chaos and a review editor for Frontiers in Physiology. She has served on the organizing committees of the SIAM Conference on Applications of Dynamical Systems in 2017, Dynamics Days 2020, and the Biology and Medicine Through Mathematics Conference 2018 and 2019 and on the program committees for the International Workshop on Hybrid Systems 2019 and 2020 and the International Congress on Electrocardiology 2018 and 2019. She received a BS in Mathematics from Georgetown University and a PhD in Computer Science from Duke University focusing on efficient computational methods for solving partial-differential-equations models of electrical signals in the heart. Her research is supported by the National Science Foundation and the National Institutes of Health
Learn more about Elizabeth's leadership journey:
My journey to high-performance computing began in a fairly ordinary way: my research required it, and I was fortunate enough to have a colleague who shared an MPI-based code to get me started. I was able to take a few short classes through national supercomputing centers to learn more about MPI, and over time it became easier to read and write MPI-based codes. Since then, I’ve branched out a bit to use a little OpenMP, OpenACC, and WebGL for both CPU- and GPU-based HPC.
Learn more about our other HPC Heavy Hitters:
Arizona State University
Texas Advanced Computing Center
Carol X. Song
Ohio Supercomputer Center