Connection details (including Zoom room) here: https://indico.cern.ch/event/1365682/
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.
Biography: David W. Hogg is 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, and he has an affiliation with the Max-Planck-Institut für Astronomie. 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. In all areas, he is interested in developing the engineering systems that make these projects possible. The comprehensive goals are long-term goals but he is involved in present-day projects that work towards them including Astrometry.net, Gaia and SDSS.