Biography
Ho graduated with a B.A. in physics and a B.A. in computer science from the University of California at Berkeley. [3] She pursued her Ph.D. at the Department of Astrophysical Sciences of Princeton University. [1] [4] In 2008 she obtained her doctorate in Astrophysical Sciences. [1] Subsequently, she worked in the Lawrence Berkeley National Laboratory between 2008 and 2012 in a postdoctoral position as a Chamberlain and a Seaborg Fellow. [1]
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. [5] 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 [6] at the Center for Computational Astrophysics (CCA) at the Flatiron Institute. [7] Ho currently leads the Cosmology and ML X Astro and Foundation Models for Science groups at the Center for Computational Astrophysics.
Research
Ho researches cosmology, deep learning and its applications in astrophysics and data science. [8] In particular, she pioneers in developing and deploying deep learning to better understand the Universe, and other astrophysical phenomena. [9]
She has contributed significantly to several areas of astrophysics: cosmic microwave background, [10] cosmological models, dark energy, dark matter, [11] [12] spatial distribution of galaxies and quasars, [13] Baryon Acoustic Oscillations, [14] [15] and cosmological simulations. [16]
Regarding deep learning and its and applications to cosmology and astrophysics., [17] [18] [19] Ho's team has led in the development of accelerated astrophysical simulations. [20] She is the senior lead in the development and deployment of deep-learning-accelerated simulation-based inference framework for large spectroscopic surveys, [21] and further accelerated physical simulations ranging from fluid dynamics to planetary dynamics simulations. [22] [23] [24] Her current team at the Flatiron Institute and Princeton University is the first to combine 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. [25] [26] [27]
More recently, Ho has led a team of researchers at Polymathic AI to create foundation models for sciences, most notably releasing large datasets and foundation models in astrophysics and fluid dynamics [28] [29] [30] .
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