Multi-Messenger Astrophysics (MMA) is an exciting new field of science that combines traditional astronomy with observations of gravitational waves and high-energy neutrinos. However, the MMA promise can be realized only if sufficient cyberinfrastructure is available to rapidly handle, combine, and analyze the very large-scale distributed data from all the types of astronomical measurements. This workshop was held on the UC Berkeley campus on May 30th, 2019, and focused on the algorithmic and computational approaches to inference in the MMA era. Registration has closed for this event.
VIDEO PLAYLIST
Links to individual talks/abstracts are also included in the agenda below.
8:30-8:40 Logistics, Introduction, Welcome and Summary from Previous Day -- Josh Bloom, UC Berkeley Astronomy
The Science of MMA -- Moderator, Patrick Brady
- 8:40-8:56 Introduction to MMA -- Szabi Marka, Columbia University
- 8:56-9:12 Joint constraints on neutron star equation of state using gravitational waves -- Shaon Ghosh, UWM
- 9:12-9:28 Black Hole Binaries and AGN -- Zsuzsa Marka, Columbia University
- 9:28-9:44 Numerical Relativity in the Era of Precision Gravity -- Dierdre Shoemaker, Georgia Tech
- 9:44-10:00 GW+High-energy neutrinos -- Azadeh Keivani, Columbia University
- 10:00-10:16 Gravitational wave parameter and population inference -- Richard O’Shaughnessy, Rochester Institute of Technology
- 10:16-10:30 Session Panel: The Science of MMA
- 10:30-10:40 Break
Inferencing, Emulators & Interpretability -- Moderator, David Hogg
- 10:40-10:56 Using Bayesian Inference to search for sub-threshold GW+EM signals -- Collin Capano, Albert Einstein Institute, Hannover, Germany
- 10:56-11:12 Deep Learning for Gravitational Wave Astrophysics -- Daniel George, Google X
- 11:12-11:28 Spectral Emulators for expensive radiative transfer codes -- Wolfgang Kerzendorf, NYU/MSU
- 11:28-11:44 Driving towards interpretability -- Ashish Mahabal, Caltech
- 11:44-12:00 Interpretability & Transparency -- Daniela Huppenkothen, UW
- 12:00-12:15 Session Panel: Inferencing, Emulators & Interpretability
- 12:15-1pm Lunch
Frameworks and Approaches to Accelerate Discovery -- Moderator, Daniela Huppenkothen
- 1:00-1:16 An Anomaly Catalog for the Dark Energy Survey -- Umaa Rebbapragada, JPL
- 1:16-1:32 Machine and deep learning applications in LSST user generated data products -- Federica Bianco, U Delaware
- 1:32-1:48 ANTARES: Machine Learning on Multi-messenger Alert Streams for Real-time Brokering -- Gautham Narayan, STScI
- 1:48-2:04 Dimensionality Reduction of SDSS Data with Autoencoders -- Stephen Portillo, UW
- 2:04-2:20 Deep learning for the Zwicky Transient Facility: real/bogus classification and identification of fast-moving objects -- Dmitry Duev, Caltech
- 2:20-2:35 Session Panel: Frameworks and Approaches to Accelerate Discovery
- 2:35-3:30 Poster Session, Break
Hardware Accelerating Inference and Federated Learning -- Moderator, Peter Nugent
- 3:30-3:46 Sharing without Showing: Enabling Secure Collaborative Learning via Cryptography -- Wenting Zheng, UC Berkeley
- 3:46-4:02 Probabilistic computing architectures -- Eric Jonas, UC Berkeley
- 4:02-4:18 Specialized hardware for machine learning -- Amir Khosrowshahi, Intel
- 4:18-4:34 Scalable and Efficient Deep Learning on Supercomputers -- Zhao Zhang, Texas Advanced Computing Center
- 4:34-4:50 Session Panel: Hardware Accelerating Inference and Federated Learning
- 4:50-5:00 Wrap-up and SCiMMA -- Patrick Brady, UWM
Scientific Organizing Committee
Joshua Bloom (UC Berkeley)
David Hogg (NYU)
Peter Nugent (LBL)
Eric Jonas (Berkeley)
Local Organizing Committee
Stacey Dorton
Stefan van der Walt
If you have any questions, please contact workshop@ml4science.org or Josh Bloom.
Speaker(s)
Joshua Bloom
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).