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Workshop
Machine Learning and the Physical Sciences
Anima Anandkumar · Kyle Cranmer · Shirley Ho · Mr. Prabhat · Lenka Zdeborová · Atilim Gunes Baydin · Juan Carrasquilla · Adji Bousso Dieng · Karthik Kashinath · Gilles Louppe · Brian Nord · Michela Paganini · Savannah Thais

Fri Dec 11 07:00 AM -- 03:15 PM (PST) @ None
Event URL: https://ml4physicalsciences.github.io/ »

Machine learning methods have had great success in learning complex representations that enable them to make predictions about unobserved data. Physical sciences span problems and challenges at all scales in the universe: from finding exoplanets in trillions of sky pixels, to finding machine learning inspired solutions to the quantum many-body problem, to detecting anomalies in event streams from the Large Hadron Collider. Tackling a number of associated data-intensive tasks including, but not limited to, segmentation, 3D computer vision, sequence modeling, causal reasoning, and efficient probabilistic inference are critical for furthering scientific discovery. In addition to using machine learning models for scientific discovery, the ability to interpret what a model has learned is receiving an increasing amount of attention.

In this targeted workshop, we would like to bring together computer scientists, mathematicians and physical scientists who are interested in applying machine learning to various outstanding physical problems, in particular in inverse problems and approximating physical processes; understanding what the learned model really represents; and connecting tools and insights from physical sciences to the study of machine learning models. In particular, the workshop invites researchers to contribute papers that demonstrate cutting-edge progress in the application of machine learning techniques to real-world problems in physical sciences, and using physical insights to understand what the learned model means.

By bringing together machine learning researchers and physical scientists who apply machine learning, we expect to strengthen the interdisciplinary dialogue, introduce exciting new open problems to the broader community, and stimulate production of new approaches to solving open problems in sciences. Invited talks from leading individuals in both communities will cover the state-of-the-art techniques and set the stage for this workshop.



Gather Town link: https://neurips.gather.town/app/GS7AwXNphTXVVEZH/NeurIPS%20ML4PS

Fri 7:00 a.m. - 7:10 a.m.
Session 1 | Opening remarks (Opening remarks (live))
Fri 7:10 a.m. - 7:35 a.m.
Session 1 | Invited talk: Lauren Anderson, "3D Milky Way Dust Map using a Scalable Gaussian Process" (Invited talk)
Lauren Anderson, Atilim Gunes Baydin
Fri 7:35 a.m. - 7:45 a.m.
Session 1 | Invited talk Q&A: Lauren Anderson (Q&A (live))
Fri 7:45 a.m. - 8:10 a.m.
Session 1 | Invited talk: Michael Bronstein, "Geometric Deep Learning for Functional Protein Design" (Invited talk)
Michael Bronstein, Atilim Gunes Baydin
Fri 8:10 a.m. - 8:20 a.m.
Session 1 | Invited talk Q&A: Michael Bronstein (Q&A (live))
Fri 8:20 a.m. - 9:50 a.m.
Session 1 | Poster session (Poster session (Gather.town))
Fri 9:50 a.m. - 9:55 a.m.
Session 2 | Opening remarks (Opening remarks (live))
Fri 9:55 a.m. - 10:20 a.m.
Session 2 | Invited talk: Estelle Inack, "Variational Neural Annealing" (Invited talk)   
Estelle Inack, Atilim Gunes Baydin
Fri 10:20 a.m. - 10:30 a.m.
Session 2 | Invited talk Q&A: Estelle Inack (Q&A (live))
Fri 10:30 a.m. - 10:55 a.m.
Session 2 | Invited talk: Phiala Shanahan, "Generative Flow Models for Gauge Field Theory" (Invited talk)   
Phiala Shanahan, Atilim Gunes Baydin
Fri 10:55 a.m. - 11:05 a.m.
Session 2 | Invited talk Q&A: Phiala Shanahan (Q&A (live))
Fri 11:05 a.m. - 12:35 p.m.
Session 2 | Poster session (Poster session (Gather.town))
Fri 12:35 p.m. - 12:40 p.m.
Session 3 | Opening remarks (Opening remarks (live))
Fri 12:40 p.m. - 1:05 p.m.
Session 3 | Invited talk: Laura Waller, "Physics-based Learning for Computational Microscopy" (Invited talk)   
Laura Waller, Atilim Gunes Baydin
Fri 1:05 p.m. - 1:15 p.m.
Session 3 | Invited talk Q&A: Laura Waller (Q&A (live))
Fri 1:15 p.m. - 2:45 p.m.
Session 3 | Community development breakouts (Community breakout session (Gather.town))
Fri 2:45 p.m. - 3:15 p.m.
Session 3 | Feedback from community development breakouts (Feedback remarks (live))

Author Information

Anima Anandkumar (NVIDIA / Caltech)

Anima Anandkumar is a Bren professor at Caltech CMS department and a director of machine learning research at NVIDIA. Her research spans both theoretical and practical aspects of large-scale machine learning. In particular, she has spearheaded research in tensor-algebraic methods, non-convex optimization, probabilistic models and deep learning. Anima is the recipient of several awards and honors such as the Bren named chair professorship at Caltech, Alfred. P. Sloan Fellowship, Young investigator awards from the Air Force and Army research offices, Faculty fellowships from Microsoft, Google and Adobe, and several best paper awards. Anima received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, a visiting researcher at Microsoft Research New England in 2012 and 2014, an assistant professor at U.C. Irvine between 2010 and 2016, an associate professor at U.C. Irvine between 2016 and 2017 and a principal scientist at Amazon Web Services between 2016 and 2018.

Kyle Cranmer (New York University)

Kyle Cranmer is an Associate Professor of Physics at New York University and affiliated with NYU's Center for Data Science. He is an experimental particle physicists working, primarily, on the Large Hadron Collider, based in Geneva, Switzerland. He was awarded the Presidential Early Career Award for Science and Engineering in 2007 and the National Science Foundation's Career Award in 2009. Professor Cranmer developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in July, 2012. His current interests are at the intersection of physics and machine learning and include inference in the context of intractable likelihoods, development of machine learning models imbued with physics knowledge, adversarial training for robustness to systematic uncertainty, the use of generative models in the physical sciences, and integration of reproducible workflows in the inference pipeline.

Shirley Ho (Flatiron institute/ New York University/ Carnegie Mellon)
Mr. Prabhat (LBL/NERSC)
Lenka Zdeborová (CEA)
Atilim Gunes Baydin (University of Oxford)
Juan Carrasquilla
Adji Dieng (Columbia University)
Karthik Kashinath (LBNL)
Gilles Louppe (University of Liège)
Brian Nord (Fermi National Accelerator Laboratory)
Michela Paganini (Facebook AI Research)
Savannah Thais (Princeton University)

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