This event is organized by the Vector Institute
Talk title:
AI Accelerating Scientific Understanding: Neural Operators for Learning on Function Spaces
Date & Time:
Friday, May 10, 2024, 12:30 p.m. - 1:30 p.m. EST
Location:
Virtual
Speaker:
Anima Anandkumar, Bren Professor of Computing and Mathematical Sciences
Abstract:
Language models have been used for generating new ideas and hypotheses in scientific domains. For instance, language models could suggest new drugs or engineering designs. However, this is not sufficient to attack the hard part of science which is the physical experiments needed to validate the proposed ideas. This is because language models lack physical validity and the ability to internally simulate the processes. Traditional simulation methods are too slow and infeasible for complex processes observed in many scientific domains. We propose AI-based simulation methods that are 4-5 orders of magnitude faster and cheaper than traditional simulations. They are based on Neural Operators which learn mappings between function spaces, and have been successfully applied to weather forecasting, fluid dynamics, carbon capture and storage modeling, and optimized design of medical devices, yielding significant speedups and improvements.
Bio:
Anima Anandkumar is currently Bren professor at Caltech, and works on AI algorithms and its applications to many domains in scientific areas. She is a fellow of the IEEE, ACM, AAAI, and has received fellowships from Schmidt Science AI 2050, Guggenheim, and Sloan foundations. She is recipient of the NSF Career award, and best paper awards at venues such as Neural Information Processing and the ACM Gordon Bell Special Prize for HPC-Based COVID-19 Research. She recently presented her work on AI+Science to the White House Science Council. She received her B. Tech from the Indian Institute of Technology Madras and her Ph.D. from Cornell University and did her postdoctoral research at MIT. She was previously a principal scientist at Amazon Web Services, and a senior director of AI research at NVIDIA.
About the Vector Distinguished Lecture Series:
The Vector Distinguished Talk series is a formal gathering of academic and industrial data scientists across the Greater Toronto Area (GTA) to discuss advanced topics in machine learning and its goal is to build a stronger machine learning community in Toronto. The talks will be given by international and local faculty and industry professionals.
The seminar series is intended for university faculty and graduate students in machine learning across computer science, ECE, statistics, mathematics, linguistics, and medicine, as well as PhD-level data scientists doing interesting applied research in the GTA. The Toronto machine learning community will be stronger when we know each other and know what problems people are working on. At the end of each talk there will also be an opportunity to have in-person meetings with the speaker.
Vector Distinguished Lecture Series will be held remotely. We look forward to welcoming everyone in-person at Vector's new office space soon. Our talks will continue to be streamed online for those not able to come in-person.
* This event is open to the public with emphasis on graduate students in machine learning, computer science, ECE, statistics, mathematics, linguistics, medicine, as well as PhD-level data scientists in the GTA.
Note: Event details can change. Please visit the unit’s website for the latest information about this event.