Where/when:  E2-215  (pizza will be provided!)

Presenter:  David Shih (Rutgers University)

Title: “New Approaches to Anomaly Detection Inspired by High Energy Physics”

Abstract: With an enormous and complex dataset, and the ability to cheaply generate realistic simulations, the Large Hadron Collider (LHC) provides a novel arena for machine learning and artificial intelligence. In this talk I will give an overview of a number of recently proposed methods for anomaly detection that were motivated by the search for new particles and forces at the LHC. This includes methods based on autoencoders, weak supervision, density estimation and simulation-assisted reweighting. I will also summarize the status of the ongoing “LHC Olympics 2020” anomaly detection data challenge, where many of these techniques are being applied to “black box” datasets by a number of groups from around the world. Finally, while these methods are being developed in the context of high energy physics, I will attempt to highlight the ways in which they are general and could be applied to data-driven discovery in other branches of science and beyond.