Speaker
Amir Farbin
(University of Texas at Arlington (US))
Description
6 Years after first demonstration of Deep Learning in HEP, the LHC community has explored a broad range of applications aiming for better, cheaper, faster, and easier solutions that ultimately extend the physics reach of the experiments and over come HL-LHC computing challenges. I’ll present a snapshot of where the ATLAS experiment currently stands in adoption of Deep Learning and suggest where it may go.
