AI4Science: A Revolution in the Making
- Speaker
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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
Presidential Lectures are free public colloquia centered on four main themes: Biology, Physics, Mathematics and Computer Science, and Neuroscience and Autism Science. These curated, high-level scientific talks feature leading scientists and mathematicians and are intended to foster discourse and drive discovery among the broader NYC-area research community. We invite those interested in the topic to join us for this weekly lecture series.
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.
About the Speaker
Anandkumar is a Bren Professor at Caltech and director of machine learning research at Nvidia. She was previously a principal scientist at Amazon Web Services. She has received several honors, including an Alfred. P. Sloan Fellowship, a National Science Foundation Career Award, young investigator awards from the Department of Defense, and faculty fellowships from Microsoft, Google, Facebook and Adobe. She is part of the World Economic Forum’s Expert Network. She is passionate about designing principled AI algorithms and applying them in interdisciplinary applications. Her research focus is on unsupervised AI, optimization, and tensor methods.