At the Crossroad of Physics and Machine Learning
Coordinators: Giuseppe Carleo, Kyle Cranmer, Rose Yu, and Lenka Zdeborova
The ongoing developments in machine learning are influencing physics research and offer the potential to open new routes to discovery. Simultaneously, concepts from physics are being used to better understand some of the machine learning methods and inspire new ones. This conference will gather researchers on the forefront of these developments to discuss existing key progress and promising new directions. We will highlight the use of machine learning in several areas of data-intensive physics including condensed matter, soft-matter, high-energy physics, cosmology, and astrophysics. We will also focus on the complementary developments in the application of physics concepts to the theory and practice of machine learning.
PLEASE NOTE: During the conference there will be an opportunity to present a poster. If you are interested in presenting a poster please visit the conference website and submit your title and abstract. Each poster board is 4 feet high x 6 feet wide. We ask that the posters be no larger than 44 inches high x 34 inches wide at the most (it is important to follow these measurements or posters may be turned down due to limited space). There will not be a poster selection, all posters are welcome. The deadline to submit posters is 2 weeks prior to the conference January 28, 2019.