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Workshop
Machine Learning and the Physical Sciences
Atilim Gunes Baydin · Adji Bousso Dieng · Emine Kucukbenli · Gilles Louppe · Siddharth Mishra-Sharma · Benjamin Nachman · Brian Nord · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Lenka Zdeborová

@ Physical
Event URL: https://ml4physicalsciences.github.io/ »

The Machine Learning and the Physical Sciences workshop aims to provide an informal, inclusive and leading-edge venue for research and discussions at the interface of machine learning (ML) and the physical sciences. This interface spans (1) applications of ML in physical sciences (ML for physics), (2) developments in ML motivated by physical insights (physics for ML), and most recently (3) convergence of ML and physical sciences (physics with ML) which inspires questioning what scientific understanding means in the age of complex-AI powered science, and what roles machine and human scientists will play in developing scientific understanding in the future.

Author Information

Atilim Gunes Baydin (University of Oxford)
Adji Bousso Dieng (Princeton University & Google AI)
Emine Kucukbenli (Boston University)
Gilles Louppe (University of Liège)
Siddharth Mishra-Sharma (MIT)
Benjamin Nachman (Lawrence Berkeley National Laboratory)
Brian Nord (Fermi National Accelerator Laboratory)
Savannah Thais (Princeton University)
Anima Anandkumar (NVIDIA / Caltech)
Kyle Cranmer (New York University & Meta AI)

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.

Lenka Zdeborová (CEA)

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