Update May 2020: Following up on our workshop here, we have begun a virtual biweekly seminar series continuing our conversations on the interface between physics and ML. Please visit http://physicsmeetsml.org/ for more information.
The goal of Physics ∩ ML (read ‘Physics Meets ML’) is to bring together researchers from machine learning and physics to learn from each other and push research forward together. In this inaugural edition, we will especially highlight some amazing progress made in string theory with machine learning and in the understanding of deep learning from a physical angle. Nevertheless, we invite a cast with wide ranging expertise in order to spark new ideas. Plenary sessions from experts in each field and shorter specialized talks will introduce existing research. We will hold moderated discussions and breakout groups in which participants can identify problems and hopefully begin new collaborations in both directions. For example, physical insights can motivate advanced algorithms in machine learning, and analysis of geometric and topological datasets with machine learning can yield critical new insights in fundamental physics.
Greg Yang, Microsoft Research
Jim Halverson, Northeastern University
Sven Krippendorf, LMU Munich
Fabian Ruehle, CERN, Oxford University
Rak-Kyeong Seong, Samsung SDS
Gary Shiu, University of Wisconsin