Скачать или смотреть CU AI for Science Seminar - Dr. Stefano Ermon on Diffusion Models for Scientific Discovery

The goal of the Cornell AI for Science Seminar Series is to bringing together the computational and scientific communities to understand advances and challenges at the frontier of AI for scientific discovery. In addition to senior speakers, the seminar features the research of Schmidt Futures AI for Science Postdoctoral researchers from multiple institutions. Seminars occur roughly biweekly on Fridays from 2:30-3:30pm EST. See our website for more information: https://science.ai.cornell.edu/

Abstract: Diffusion models are at the core of many state-of-the-art generative AI systems for media content such as images, videos, and audio. Due to their excellent sample quality and theoretical guarantees, they are emerging as an important tool in many scientific, medical, and engineering applications. In this talk I will present several extensions of diffusion models tailored to the unique challenges that arise in these domains. I will discuss techniques for incorporating prior knowledge through constraints, symmetries and invariances for geometric data (such as molecules), and new approaches for tackling inverse problems. These techniques provide significant benefits across a variety of applications ranging from molecule generation to medical imaging.

Bio: Stefano Ermon is an Associate Professor in Stanford’s Department of Computer Science and a senior fellow at the Woods Institute of the Environment. His research focuses on machine learning and generative AI motivated by real-world applications in science and engineering with societal relevance. Dr. Ermon has received many awards including ONR and AFOSR Young Investigator Awards, Sloan and Microsoft Research Fellowships, and an NSF CAREER award and is a co-founder of Atlas AI. He received his PhD from Cornell University working with Cornell University AI for Science Institute co-director Carla Gomes.