
Sam Young
youngsam [at] stanford.edu
I’m a third year PhD student in the Physics Department at Stanford University. My goal is to help computers understand and reason about data from particle physics experiments. I am advised by Kazu Terao and Hiro Tanaka in the neutrino group at SLAC National Accelerator Laboratory.
I’m broadly interested in:
- Self-supervised representation learning and foundation models for scientific imaging data
- 3D computer vision for particle physics
- Differentiable surrogate models for simulation calibration
I’m grateful to be supported by the Stanford Institute for Human-Centered Artificial Intelligence (HAI) Graduate Fellowship in the 2023-2024 academic year.
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 from Penn in 2023 with a bachelor’s and master’s in physics.
News!
2025 | Gave a talk on foundation models for neutrino physics at the Machine Learning for Fundamental Physics (ML4FP) summer school. Slides |
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2025 | Gave a talk on on foundation models for scientific imaging (LArTPC data) at the APS Global Summit. |
2024 | I was awarded the HAI Graduate Fellowship! |
2024 | Gave a talk on differentiable surrogate modeling of optical propagation in LArTPCs at APS April. |
2024 | Won Stanford’s CS 229 Machine Learning’s Best Project Award for my rotation work on pileup synthesis and anomaly detection for the ATLAS experiment. |
2023 | I graduated from Penn with a bachelor’s and master’s in physics, and will continue my studies at Stanford. |