Aditi Krishnapriyan

I am interested in developing methods in machine learning that are driven by the distinct challenges and opportunities in the natural sciences, with particular interest in physics-inspired machine learning methods. Some areas of exploration include approaches to incorporate physical inductive biases (such as symmetries, conservation laws) 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. These methods are informed by and grounded in applications in atomistic and continuum problems, including fluid mechanics, molecular dynamics, materials design, climate science, and other related areas. This work also includes interfacing with other fields including numerical analysis, dynamical systems theory, quantum mechanical simulations, computational geometry, optimization, and category theory.