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Sam Young

Physics PhD @ Stanford

I’m a third year PhD student in the Physics Department at Stanford University, interested in building intelligent systems that can understand and reason about raw, sensor-level data from particle physics experiments. I am advised by Kazu Terao in the neutrino group at SLAC National Accelerator Laboratory, building tools for analyzing data from neutrino detectors.

For my PhD, I’ve been working on:

  • Self-supervised learning of physically meaningful representations of unlabeled imaging data.
  • 3D computer vision for particle and interaction identification.
  • Differentiable surrogate models for calibrating optical simulations to real data

Papers

  1. Panda: Self-distillation of Reusable Sensor-level Representations for High Energy Physics
    tl;dr: An application of DINO-like self-distillation to unlabeled 3D images of particle interactions in a neutrino detector. We show the model learns to cleanly separate particles and interaction types in latent space, which reduced the number of labeled images required to reach the state-of-the-art for semantic segmentation by 1,000×.
    Samuel Young, and Kazuhiro Terao
    arXiv, 2025. Project, Code
  2. 10 Million Particle Events: Enabling Foundation Models for Sparse 3D Inverse Problems
    tl;dr: A proposal for a new dataset containing 10 million images of particle interactions with realistic detector response and multiple modalities.
    Omar Alterkait, Sam Young, Ka Vang Tsang, Junjie Xia, Carolyn H Smith, Taritree Wongjirad, and Kazuhiro Terao
    NeurIPS 2025 AI4Science Workshop (Spotlight), 2025
  3. Particle trajectory representation learning with masked point modeling
    tl;dr: We proposed a masked point modeling approach, PoLAr-MAE, for learning representations of particle trajectories from unlabeled LArTPC images.
    Samuel Young, Yeon-jae Jwa, and Kazuhiro Terao
    Machine Learning: Science and Technology, 2025. Project, Code

News

2025 Gave a talk on recent work on building a foundation model for neutrino physics at the NPML 2025. Slides
2025 Gave a talk on foundation models for neutrino physics at the Machine Learning for Fundamental Physics (ML4FP) summer school. Slides
2025 Gave a talk on on foundation models for scientific imaging at the 2025 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.