A3D3 Seminar: David Hogg

Title: Is machine learning good or bad for astrophysics?
Abstract: Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology—in which only the data exist—and a strong epistemology—in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standard practices and key philosophies in astrophysics. I show that there are contexts in which the introduction of ML introduces strong, unwanted (and currently unfixable) statistical biases. However, there are locations for ML in astrophysics in which the ontology and epistemology are valuable: I will even give an example of a place where the introduction of ML makes your project or measurement or conclusion more conservative, reliable, and trustworthy.

David W. Hogg is Professor of Physics and Data Science in the Center for Cosmology and Particle Physics in the Department of Physics at New York University. He is also Group Leader for the Astronomical Data Group in the Center for Computational Astrophysics of the Flatiron Institute. His main research interests are in observational cosmology, especially approaches that use galaxies to infer the physical properties of the Universe. He also works on the properties and kinematics of stars in the Galaxy, and the measurement and discovery of planets around other stars. 


The A3D3 Seminar is a monthly lecture series that hosts scholars working across applied areas of artificial intelligence, such as hardware algorithm co-development, high energy physics, multi-messenger astrophysics,  and neuroscience. Our presenters come from all four domain fields and include occasional external speakers beyond the A3D3 science areas, governmental agencies and industry. The seminar will be recorded and published in YouTube. To receive future event updates, subscribe here.

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Matthew Graham Kate Scholberg

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Shih-Chieh Hsu
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Javier Mauricio Duarte, Menglu Zhang, Miaoran Lu, Philip Coleman Harris, Mark Neubauer
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