Machine Learning for Astrophysics
Workshop at the Thirty-ninth International Conference on Machine Learning (ICML 2022)
Dates: July 22, 2022
Time: 6:15 AM – 4:15 PM Pacific
Location: Online and in Baltimore, MD
Registration for this and all other ICML workshops is handled through the main ICML conference registration. Inquiries regarding the workshop can be directed to firstname.lastname@example.org.
BIDS Faculty Affiliate Josh Bloom (UC Berkeley) is a confirmed speaker/panelist, and BIDS Alum François Lanusse (CNRS) and BIDS Faculty Affiliate Uroš Seljak (UC Berkeley) are on the organizing committee for this one-day ICML 2022 workshop, which will bring together Machine Learning researchers and domain experts in the field of Astrophysics to discuss the key open issues which hamper the use of Deep Learning for scientific discovery. Rather than focusing on the benefits of deep learning for astronomy, this workshop aims at overcoming its limitations. An important aspect to the success of this workshop is to create a two-way interdisciplinary dialog in which concrete data-analysis challenges can spur the development of dedicated Machine Learning tools. This workshop is designed to facilitate this dialog and will include a mix of interdisciplinary invited talks and panel discussions, providing an opportunity for ICML audiences to connect their research interests to concrete and outstanding scientific challenges. Contributions on these topics do not necessarily need to be Astrophysics-focused — works on relevant ML methodology, or similar considerations in other scientific fields, are welcome.
Call for Abstracts – Submissions Due May 23, 2022
Submit your anonymized extended abstract through CMT (https://cmt3.research.microsoft.com/ML4Astro2022) by/before May 23, 2022.
Josh Bloom an astronomy professor at the University of California, Berkeley, where he teaches high-energy astrophysics, Python bootcamps, and a graduate-level class on Python for data-driven science. He has published more than 250 refereed articles, largely on time-domain transients events and telescope/insight automation. Expressed in his research is output of a collaborative effort between talented astronomers, statisticians, and computer scientists (ranging from students to peers) at the nexus of physics, scalable computation, and machine learning. His book on gamma-ray bursts was published in 2011, as part of the "Frontiers in Physics" series by Princeton University Press. He has been awarded the Pierce Prize from the American Astronomical Society, and he is a former Sloan Fellow, Junior Fellow at the Harvard Society, and Hertz Foundation Fellow. He holds a PhD from Caltech and degrees from Harvard and Cambridge University. Recently, he has working as co-PI of the Moore-Sloan Data Science Initiative at UC Berkeley and an elected member of the management oversight body of the Large Synoptic Survey Telescope (LSST).
While at UC Berkeley, François Lanusse was a Data Science Fellow at BIDS and a Postdoctoral Scholar with the Berkeley Center for Cosmological Physics (BCCP) and the Foundations of Data Analysis (FODA) Institute, exploring the intersection between cosmology, statistics, and machine learning. His research was focused on measuring and exploiting the gravitational lensing effect (in which distant galaxies appear distorted due to the presence of massive structures along the line of sight) with the development of novel tools and methodologies based on sparse signal representations, convex optimization, and deep learning. Before joining Berkeley, Dr. Lanusse worked as a postdoctoral researcher within the McWilliams Center for Cosmology at Carnegie Mellon University, after completing a PhD in astrophysics at CEA Saclay near Paris.
Uroš Seljak is a professor in UC Berkeley's Physics and Astronomy departments, a Senior Scientist at LBNL in the Physics Division, as well as a co-director of Berkeley Center for Cosmological Physics. His main research is in cosmology, where he combines theoretical, numerical, and data analysis methods to investigate the universe properties using cosmological observations, from cosmic microwave background to present day galaxy and dark matter distributions. His recent work combines statistics, numerical optimization, and N-body simulation methods to analyze large cosmological surveys, both space based (WMAP, Planck, Euclid, WFIRST) and ground based (SDSS, DESI, LSST). At Berkeley he teaches a course on Bayesian statistics and data science in Physics department. He has a PhD from MIT, was a postdoctoral fellow at Harvard and a faculty at Princeton University and Zurich University prior to Berkeley. He is a recipient of Sloan and Packard Fellowships, NSF CAREER award, and the Warner Prize of the American Astronomical Society.