AI for Science Seminar Series (Spring 2024)
We are excited to announce the AI for Science seminar series which aims to bring together the computational and scientific communities to understand advances and challenges at the frontier of AI for scientific discovery. The seminars will feature both pioneers and Schmidt Futures AI for Science postdocs from nine participating institutions.
2:30–3:30pm alternating Fridays
February 9–May 3, 2024
Gates 122 and via Zoom
Our inaugural speaker will be Prof. Stefano Ermon on applications of diffusion models to multiple areas of science, including materials discovery and climate science. Recordings will be made available on our YouTube channel.
If you have speaker suggestions or are a Schmidt Futures AI in Science postdoc who is interested in presenting your research, please contact Laura Greenstreet.
Spring 2024 Seminars
|Fri Feb 9, 2024
2:30–3:30pm EST (UTC-5)
|Stefano Ermon, Stanford University
|Diffusion Models for Scientific Discovery
|Fri Apr 12, 2024
2:30–3:30pm EDT (UTC-4)
|Aditya Grover, UCLA
|Foundation Models for a Sustainable Planet
Spring 2024 Seminar Details
Stefano Ermon, Stanford University
Fri Feb 9, 2024, 2:30–3:30pm EST (UTC-5)
Title: Diffusion Models for Scientific Discovery
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.
Aditya Grover, UCLA
Fri Apr 12, 2024, 2:30–3:30pm EDT (UTC-4)
Title: Foundation Models for a Sustainable Planet
Abstract: Key global sustainability challenges, ranging from ensuring food and energy security to managing disaster response, critically depend on our ability to accurately forecast weather and project climate. While current approaches are limited by our physical understanding of the atmosphere, improvements in sensory capabilities and large-scale machine learning (ML) present immense opportunities for designing alternative solutions. In this talk, I will discuss key design principles for developing foundation models for atmospheric sciences. Unlike language and vision, scientific domains present unique challenges due to the significant heterogeneity of available datasets. Moreover, downstream tasks in atmospheric sciences require generalizing across a broad range of variables and spatiotemporal resolutions. To address these challenges, I will demonstrate novel strategies for data engineering, model design, spatiotemporal optimization, and cross-modal finetuning aimed at scalable learning of atmospheric foundation models. Our resulting models exhibit exceptional skill, speed, and adaptability across a range of predictive tasks in weather and climate science. Finally, I will summarize the broader implications of this research for scientific ML and growing external efforts to adapt our models for diverse sustainable development goals.
Bio: Aditya Grover is an assistant professor of computer science at UCLA. His research interests are at the intersection of generative modeling and sequential decision making, and grounded in applications for accelerating science and sustainability. Aditya’s research has been recognized with a best paper award (NeurIPS), several graduate fellowships and faculty awards (Google, Meta, Microsoft, Schmidt Sciences, Simons Institute), the Forbes 30 Under 30 List, the AI Researcher of the Year Award by Samsung, the Kavli Fellowship by the US National Academy of Sciences, and the ACM SIGKDD Doctoral Dissertation Award. Aditya received his postdoctoral training at UC Berkeley, PhD from Stanford, and bachelors from IIT Delhi, all in computer science.
The full schedule will be posted shortly.