Sam Young

I’m a second year PhD student in the Physics Department at Stanford University working at the intersection of particle physics and machine learning. I am advised by Kazu Terao in the neutrino group at SLAC National Accelerator Laboratory and work on applying deep learning techniques to speed up physics simulations and improve event reconstruction at the scale the upcoming Deep Underground Neutrino Experiment will require. The experiment aims to understand the properties of neutrinos, fundamental particles that rarely interact with matter and are thus extremely difficult to detect.

I’m currently interested in differentiable surrogate models and unsupervised representation learning for physics events in liquid argon time-projection chamber (LArTPC) experiments.

I’m grateful to be supported by the Stanford Institute for Human-Centered Artificial Intelligence (HAI) Graduate Fellowship.

Previously, I worked with Julia Gonski of the SLAC ATLAS group on applying machine learning techniques for anomaly detection in the ATLAS experiment at CERN, and with Giorgio Gratta of the Stanford neutrino group on detector R&D for the upcoming nEXO experiment. Before that, I was a research assistant at the University of Pennsylvania working with Prof. Josh Klein on novel neutrino detector instrumentation for THEIA, a proposed hybrid optical neutrino detector. I graduated summa cum laude from Penn’s Department of Physics and Astronomy in 2023 with a bachelor’s and master’s in physics.