Aditi Krishnapriyan

Bruce & Susan Stangeland Professor
Chemical and Biomolecular Engineering

Assistant Professor of Chemical and Biomolecular Engineering
Assistant Professor of Electrical Engineering and Computer Sciences
Member, Berkeley AI Research (BAIR)

  • 2010 - 2013, B.S., University of California, Santa Barbara
  • 2014 - 2019, Ph.D., Stanford University
  • 2014 - 2018, DOE Computational Science Graduate Fellowship
  • 2020 - 2022, Luis W. Alvarez Fellow in Computing Sciences, Lawrence Berkeley National Laboratory

Development of new physics-inspired machine learning methods, geometric deep learning, differentiable physics, dynamical systems, numerical methods, computational geometry, optimization

Our research interests are focused on developing machine learning methods that are motivated by the opportunities and challenges in science and engineering. Some of the areas of exploration include approaches to incorporate physical inductive biases into ML models to improve generalization, 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 and molecular dynamics, materials design, climate science, and other related areas. This work also includes interfacing with other fields including numerical methods, dynamical systems theory, quantum mechanical simulations, computational geometry, and optimization.