Nathan Shoman

Papers

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About

Nathan Shoman is a researcher at the forefront of applied machine learning, with a focus on enhancing nuclear nonproliferation and safeguards through edge computing. His work bridges the gap between advanced artificial intelligence and real-world, field-deployed systems, particularly in high-stakes environments like in-field inspections. Shoman’s most-cited paper, “Machine learning at the edge to improve in-field safeguards inspections” (2024), introduces novel approaches to deploying lightweight ML models directly on portable devices, enabling real-time data analysis without reliance on cloud connectivity. This contribution is critical for improving the speed, accuracy, and autonomy of nuclear material verification, reducing human error and operational delays. While his citation count is still growing—reflecting the recent nature of his work—the practical implications of his research have already drawn attention from international safeguards communities. Shoman’s achievements include developing algorithms that operate under constrained computational resources, a key challenge in edge AI. His work not only advances technical capabilities in nonproliferation but also sets a precedent for secure, low-latency machine learning in sensitive field operations, making him a rising voice in both AI and nuclear security research.