In recent years, High Energy Physics (HEP) has seen a resurged interest in Artificial Intelligence and Machine Learning (AI/ML), driven by the current renaissance of these fields. Whilst most of the AI/ML applications in HEP have been developed in an experimental context, there is a wealth of unexplored ideas for the application of AI/ML to phenomenological and theoretical studies.
In this talk, I will discuss some of my recent and on-going work on applying AI/ML to Collider and Beyond the Standard Model (BSM) Phenomenology. For the Collider applications, I will focus on the efforts to isolate jets that have interacted with the Quark Gluon Plasma at Heavy Ion Collisions, an essential task to enable using jets as a probe for medium properties. I will then discuss the application of AI/ML algorithms for BSM model building, with a particular focus on black box search algorithms to find valid regions of multidimensional parameter spaces in highly constrained models, where random sampling efficiency can be prohibitively low.