While AI has shown tremendous success in many scientific fields, it remains a grand challenge to incorporate physical principles in a systematic manner into the design and training of these models. In this talk, Dr. Yu will introduce Physics-Guided AI, a framework that aims to integrate first-principled physical knowledge into data-driven methods. By combining the best of both worlds, we can significantly improve sample complexity, computational efficiency, prediction accuracy, and scientific validity of AI models. Dr. Yu will showcase the applications of this framework to accelerate hypothesis generation and discovery in various scientific disciplines.
Featured Speaker:
Dr. Rose Yu
Associate Professor at University of California, San Diego
Dr. Yu is an Associate Professor at UC San Diego department of Computer Science and Engineering. She is a primary faculty with the AI Group and is affiliated with Halıcıoğlu Data Science Institute. Her research interests lie primarily in machine learning, especially for large-scale spatiotemporal data. She is particularly excited about AI for scientific discovery. She has won DARPA Young Faculty Award, ECASE Award, NSF CAREER Award, Hellman Fellowship, Faculty Awards from JP Morgan, Meta, Google, Amazon, and Adobe, several Best Paper Awards, Best Dissertation Award at USC. She was named as MIT Technology Review Innovators Under 35 in AI.
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