Shirley Ho develops machine learning methods for cosmology and scientific discovery. She leads the Cosmology X Data Science group at the Flatiron Institute and is principal investigator of Polymathic AI, which builds foundation models that learn across scientific disciplines.

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Shirley Ho develops machine learning methods for cosmology and scientific discovery. She leads the Cosmology X Data Science group at the Flatiron Institute and is principal investigator of Polymathic AI, which builds foundation models that learn across scientific disciplines.

Career

Research Professor, Physics at New York University from 2020 to Present
Group Leader and Senior Research Scientist, Cosmology X Data Science at Flatiron Institute, Simons Foundation from 2018 to Present
Principal Investigator at Polymathic AI until Present
Senior Scientist at Lawrence Berkeley National Laboratory from 2016 to 2018
Tenured Associate Professor; Cooper-Siegel Chair at Carnegie Mellon University until 2016
arxiv.org

The Well: A Large-Scale Collection of Diverse Physics Simulations for Machine Learning

Shirley’s co-authored paper introduces a shared collection of physics simulations and benchmarks for training and evaluating machine learning models across scientific domains.

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