Anima Anandkumar, Ph.D.Bren Professor of Computing and Mathematical Sciences, California Institute of Technology
IEEE, Microsoft and Sloan Fellow
Director of Machine Learning Research, Nvidia
Simons Foundation Lectures are free public colloquia related to basic science and mathematics. These high-level talks are intended for professors, students, postdocs and business professionals, but interested people from the metropolitan area are welcome as well.
Artificial intelligence holds immense promise in enabling scientific breakthroughs and discoveries in diverse areas. However, in most scenarios, this is not a standard supervised learning framework. AI4science often requires zero-shot generalization to entirely new scenarios not seen during training. For instance, drug discovery requires predicting properties of new molecules that can vastly differ from training data. Similarly, AI-based partial differential equation (PDE) solvers require solving any instance of the PDE family. Such zero-shot generalization requires infusing domain knowledge and structure. In this talk, Anima Anandkumar will present recent success stories in using AI to obtain 1000x speedups in solving PDEs and quantum chemistry calculations.