We are a computational sustainability group. Our group leverages applied mathematics, scientific computation, and machine learning to model, design, and control complex systems with application to food, energy and environment, and health.
We are particularly interested in energy and environment related phenomena. Recent examples include flow physics across complex geometries (buildings, vehicles), charge transport in organic electronic devices and electrochemical systems, coupled phenomena during soft matter manufacturing, and enabling resilient agriculture. We develop mathematical techniques and computational tools — model reduction, multiscale frameworks, multiphysics simulators, control algorithms, data-driven methods — to efficiently represent these systems.
Our group is very collaborative and is always looking for enthusiastic students, post-docs, and collaborators. Please reach out if you are interested in working at the intersection of simulation science, data science, and sustainability applications!