My research is in computational science and scientific artificial intelligence, including machine learning and data-driven modelling. I have applied these approaches to physics discovery by model inference, scale bridging, partial differential equation soolvers, representation of complexity and constructing reduced-order models of high-dimensional systems. My research is motivated by and applied to phenomena in bioengineering, biophysics, mathematical biology and materials physics. Of specific interest to me are patterning and morphogenesis in developmental biology, cellular biophysics, soft matter and mechano-chemical phase transformations in materials. More fundamentally, the. foundations of my research lie in applied mathematics, numerical methods and scientific computing.
Alexander von Humboldt Research Fellowship, 2005-2006
Presidential Early Career Award for Scientists and Engineers, 2004
Department of Energy Early Career Award for Scientists and Engineers, 2004
2022 James Knowles Lecturer, Caltech
Fellow of the US Association for Computational Mechanics
Fellow of the International Association for Computational Mechanics
ME605: Advanced Finite Element Methods (advanced graduate level)
ME599: Multiphysics Phenomena at Microscales (rarely offered, advanced graduate)
ME511: Theory of Solid Continua (Continuum Mechanics, entry-level graduate)
ME505: Finite Element Methods (entry-level graduate)
ME382: Mechanical Behavior of Materials (junior year)
Editorial board member: Nature Scientific Reports; Computer Methods in Applied Mechanics and Engineering; Computational Mechanics; Engineering Mechanics; Engineering with Computers; Brain Multiphysics