Krishna Garikipati
Professor of Aerospace and Mechanical Engineering
Education
Biography
Krishna Garikipati obtained his PhD at Stanford University in 1996, and after a few years of post-doctoral work, he joined the University of Michigan in 2000, rising to Professor in the Departments of Mechanical Engineering and Mathematics. Between 2016 and 2022, he served as the Director of the Michigan Institute for Computational Discovery & Engineering (MICDE). In January 2024 he moved to the University of Southern California as a Professor of Aerospace and Mechanical Engineering. His research is in scientific machine learning and computational science, with applications drawn from biophysics, mathematical biology, materials physics and nonlinear mechanics. He has been awarded the DOE Early Career Award for Scientists and Engineers, the Presidential Early Career Award for Scientists and Engineers (PECASE), a Humboldt Research Fellowship, and the 2025 Oden Medal in Computational Science from the US Association for Computational Mechanics. He is a fellow of the US Association for Computational Mechanics, the International Association for Computational Mechanics and the Society of Engineering Science, a Life Member of Clare Hall at University of Cambridge, and a visiting scholar in Computational Biology at the Flatiron Institute of the Simons Foundation.Honors and Awards
-2025 Oden Medal in Computational Science, US Association for Computational Mechanics
-Fellow of the Society of Engineering Science since 10/2024
-Fellow of the International Association for Computational Mechanics since 7/2022
-2022 James Knowles Lecturer, California Institute of Technology
-Fellow of the US Association for Computational Mechanics since 7/2019
-Alexander von Humboldt Research Fellowship: 5/2005-8/2006
-Presidential Early Career Award for Scientists and Engineers, 2004
-Department of Energy Early Career Award for Scientists and Engineers, 2004
Research Summary
Krishna Garikipati's research is in scientific machine learning and computational science, with foundations in applied mathematics, numerical methods and scientific computing. In these areas he has made contributions to system identification, physics discovery, machine learning solvers for partial differential equations, and machine learning for scale bridging. Most recently his group has been working on foundation AI models for physics and optimal transport theory-based learning of population dynamics. His scientific machine learning and computational research has applications in biophysics and materials physics. Of specific interest to him are population dynamics, patterning and morphogenesis in developmental biology, cellular biophysics, soft matter and mechano-chemical phase transformations in materials.
Appointments
Social Media
- Aerospace and Mechanical Engineering
- Krishna Garikipati has not listed an office location.
- garikipa@usc.edu