In fundamental physics our aim is to understand the building blocks of the universe, and we do so with some of the biggest and complex scientific experiments and largest datasets on earth. The rapid progress of artificial intelligence and machine learning is having a profound effect on fundamental physics, from the ways data are generated and analyzed to the ways experiments are operated and designed, ultimately both changing the ways we purse science and expanding our potential for new discoveries.  In this talk I will discuss the frontiers of AI in fundamental physics, how the use of AI is massively improving the way we analyze data and how challenges in fundamental physics have opened new avenues in AI.

Bio:

Dr. Michael Kagan is a Panofsky Fellow at the SLAC National Accelerator Laboratory at Stanford University. His work on the ATLAS experiment focuses on understanding the rare interactions of the Higgs boson and how it gives mass to other particles. Dr. Kagan and his team work at the interface of physics and machine learning to develop new methods to better understand and extract insights from the large and complex data generated by the LHC. Dr. Kagan holds a Ph. D. in Particle Physics from Harvard University.