NEW TIME! 2:00pm: Shirley Ho (Flatiron Institute): AI+ Science Schmidt Fellows Speaker Series

Organized by the University of Chicago’s Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship Program.

Agenda
2:00pm – 2:45pm:  Presentation
2:45pm – 3:00pm:  Q&A
3:00pm – 3:30pm: Reception

Meeting location
William Eckhardt Research Center. Room 401
5640 S Ellis Avenue, Chicago, IL 60637
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Abstract: Foundation models like GPT-4 have dramatically altered the modern work landscape for many industries reliant on language tasks, but no equivalent model exists yet for scientific applications. Incorporating foundation models into research workflows could enable unprecedented discoveries that connect traditionally distinct scientific sub-disciplines. However, mainstream foundation models trained on human-scale datasets will be insufficient for analyzing most scientific phenomena — a foundation model for science will require special consideration for the requirements of scientific datasets, especially those with wide dynamic ranges.

In this talk , I will introduce the Polymathic AI initiative: our goal is to accelerate the development of versatile foundation models tailored for numerical datasets and scientific machine learning tasks. The challenge we are undertaking is to build AI models which leverage information from heterogeneous datasets and across different scientific fields, which, contrary to domains like natural language processing, do not share a unifying representation (i.e., text). Such models can then be used as strong baselines or be further fine-tuned by scientists for specific applications. This approach has the potential to democratize AI in science by providing off-the-shelf models that have stronger priors (i.e., background knowledge) for shared general concepts such as causality, measurement, signal processing, and even more specialized shared concepts like wave-like behavior, which otherwise would need to be learned from scratch.I will present our initial papers and projects, including large scientific datasets designed for large scale training “MultiModal Universe” and “The Well”.

Bio: Shirley Ho is a senior research scientist at CCA and she joined the Foundation in 2018 to lead the Cosmology X Data Science group. Her research interests range from cosmology to developing new machine learning methods for scientific data that leverage shared concepts across scientific domains. Ho has extensive expertise in astrophysical theory, observation, and data science. She focuses on novel statistical and machine learning tools to address cosmic mysteries like the origins and fate of the universe.

Ho analyzes data from surveys including ACT, Euclid, LSST, Simons Observatory, SDSS, and Roman Space Telescope to understand our universe’s evolution. She earned her Ph.D. in Astrophysical Sciences from Princeton in 2008 and B.S. degrees in Computer Science and Physics from UC Berkeley in 2004. Ho was previously a Chamberlain and Seaborg Fellow at Lawrence Berkeley National Lab. She joined Carnegie Mellon as an Assistant Professor in 2011, becoming Cooper Siegel Career Development Chair Professor and tenured Associate Professor. In 2016 she moved to Lawrence Berkeley Lab as a Senior Scientist.

Since 2011, Ho has mentored over 50 postdocs, 10 Ph.D. students, and 20 undergraduates in astrophysics, computer science, and statistics. Her awards include the Macronix Prize, Carnegie Science Award, Blavatnik National Finalist, and the EPS Giuseppe and Vanna Cocconi Prize in Cosmology.

Parking
Campus North Parking
5505 S Ellis Ave
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