Teaching Schedule

Spring 2024


Research interests: development of physics-inspired machine learning methods, geometric deep learning, optimization, scientific applications of machine learning

Our research interests are focused on developing machine learning methods that are motivated by the opportunities and challenges in science and engineering, with particular interest in physics-inspired machine learning methods. Some of the areas of exploration include approaches to incorporate physical inductive biases into ML models to improve generalization for scientific problems, the advantages that ML can bring to classical physics-based numerical solvers (such as through end-to-end differentiable frameworks and implicit layers), and better learning strategies for distribution shifts in the physical sciences. Our foundational research is informed by and grounded in applications in physics, fluid mechanics, molecular dynamics, materials design, climate science, and other related areas. This work also includes interfacing with other fields including differentiable physics, numerical methods, dynamical systems theory, quantum mechanical simulations, computational geometry, and optimization.


  • 2020, Ph.D., ㅤ, Stanford University