# Shirley Ho

Shirley Ho is an American astrophysicist and machine learning researcher, currently at the Center for Computational Astrophysics at the Flatiron Institute, and an affiliated faculty at the Center for Data Science at New York University.

## Biography

Ho graduated with a B.A. in physics and a B.A. in computer science from the University of California at Berkeley. She pursued her Ph.D. at the Department of Astrophysical Sciences of Princeton University. In 2008 she obtained her doctorate in Astrophysical Sciences. Subsequently, she worked in the Lawrence Berkeley National Laboratory between 2008 and 2012 in a postdoctoral position as a Chamberlain and a Seaborg Fellow.
Ho worked at Carnegie Mellon University, first as an assistant professor and then as an associate (with indefinite tenure) professor in physics. Ho was named Cooper-Siegel Development Chair Professor in 2015 at Carnegie Mellon University. In 2016, she moved back to the Lawrence Berkeley National Laboratory as a Senior Scientist while being on leave from Carnegie Mellon University.
In 2018, Ho joined the Simons Foundation as leader of the Cosmology X Data Science group at the Center for Computational Astrophysics (CCA) at the Flatiron Institute.

## Research

Ho researches cosmology, deep learning and its applications in astrophysics and data science. In particular, she is interested in developing and deploying deep learning to better understand the Universe, and other astrophysical phenomena.
She has contributed to several areas of astrophysics: cosmic microwave background, cosmological models, dark energy, dark matter, spatial distribution of galaxies and quasars, Baryon Acoustic Oscillations, and cosmological simulations.
Regarding deep learning and its and applications to cosmology and astrophysics., Ho has been involved in the development of accelerated astrophysical simulations. She took part in the development and deployment of deep-learning-accelerated simulation-based inference framework for large spectroscopic surveys, and further accelerated physical simulations ranging from fluid dynamics to planetary dynamics simulations. Her current team at the Flatiron Institute and Princeton University combines symbolic regression and neural networks to recover physical laws directly from observations, demonstrating symbolic regression as an example of good inductive bias for interpretable machine learning for science.
While she almost always failed to balance her research interests in machine learning and the universe, her passion for science management has allowed her to attribute much of her scientific success to the students and collaborators she has been fortunate enough to work with. Indeed, her ability to intercept scientific funding through connections with private foundations such as the Simons Foundation and the Schmidt Futures Foundation culminates into the Polymathic AI funding endeavor. Her team benefits from large enough resources to train large-scale machine learning models not only for astrophysics but also for Earth climate simulation, which is generally hard to achieve in a non-commercial setting. Her affiliations with multiple institutions, each with its own press department, ensure that she receives substantial media coverage, often in the form of first-person interviews that give her a direct platform to share her perspectives.

## Prizes

Ho has won several prizes for her contributions to cosmology and astrophysics, including:
* NASA Group Achievement Award for contribution to the Planck mission (2011) and Nancy Grace Roman Space Telescope (2022)
* Macronix Prize (2014): The Outstanding Young Researcher Award by International Organization of Chinese Physicists and Astronomers
* Carnegie Science Award (2015), assigned for outstanding science and technology achievements in western Pennsylvania
* Finalist for the Blavatnik Awards for Young Scientists (2023)