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Deep Learning for Physical Sciences
Atilim Gunes Baydin · Mr. Prabhat · Kyle Cranmer · Frank Wood

Fri Dec 08 08:00 AM -- 06:30 PM (PST) @ 104 C
Event URL: https://dl4physicalsciences.github.io/ »

Physical sciences span problems and challenges at all scales in the universe: from finding exoplanets and asteroids in trillions of sky-survey pixels, to automatic tracking of extreme weather phenomena in climate datasets, to detecting anomalies in event streams from the Large Hadron Collider at CERN. Tackling a number of associated data-intensive tasks, including, but not limited to, regression, classification, clustering, dimensionality reduction, likelihood-free inference, generative models, and experimental design are critical for furthering scientific discovery. The Deep Learning for Physical Sciences (DLPS) workshop invites researchers to contribute papers that demonstrate progress in the application of machine and deep learning techniques to real-world problems in physical sciences (including the fields and subfields of astronomy, chemistry, Earth science, and physics).

We will discuss research questions, practical implementation challenges, performance / scaling, and unique aspects of processing and analyzing scientific datasets. The target audience comprises members of the machine learning community who are interested in scientific applications and researchers in the physical sciences. By bringing together these two communities, we expect to strengthen dialogue, introduce exciting new open problems to the wider NIPS community, and stimulate production of new approaches to solving science problems. Invited talks from leading individuals from both communities will cover the state-of-the-art techniques and set the stage for this workshop.

Author Information

Atilim Gunes Baydin (University of Oxford)
Mr. Prabhat (LBL/NERSC)
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.

Frank Wood (University of British Columbia)

Dr. Wood is an associate professor in the Department of Engineering Science at the University of Oxford. Before that he was an assistant professor of Statistics at Columbia University and a research scientist at the Columbia Center for Computational Learning Systems. He formerly was a postdoctoral fellow of the Gatsby Computational Neuroscience Unit of the University College London. He holds a PhD from Brown University (’07) and BS from Cornell University (’96), both in computer science. Dr. Wood is the original architect of both the Anglican and Probabilistic-C probabilistic programming systems. He conducts AI-driven research at the boundary of probabilistic programming, Bayesian modeling, and Monte Carlo methods. Dr. Wood holds 6 patents, has authored over 50 papers, received the AISTATS best paper award in 2009, and has been awarded faculty research awards from Xerox, Google and Amazon. Prior to his academic career he was a successful entrepreneur having run and sold the content-based image retrieval company ToFish! to AOL/Time Warner and served as CEO of Interfolio.

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