Summary
Ryan Steed is a research scientist and policy-minded machine learning specialist with a decade of experience at the intersection of AI, public policy, and standards. Currently at NIST’s Center for AI Standards and Innovation and an AI Policy Fellow at Princeton, he brings rigorous, data-driven research from a PhD in Machine Learning & Public Policy at Carnegie Mellon into government-facing standards work. He is known for clear writing and persuasive verbal communication, translating technical research into actionable policy and interoperable standards. Ryan’s background spans academia and industry research—including a PhD internship at Oracle and long-form research roles—giving him practical experience turning complex models and evaluations into trustworthy, auditable guidance. Colleagues rely on his adaptability and collaborative approach to drive multidisciplinary projects that bridge technical detail and public impact.
10 years of coding experience
2 years of employment as a software developer
North Carolina School of Science and Mathematics
Bachelor of Science - BS, Computational Economics (Computer Science, Economics & Data Science), Bachelor of Science - BS, Computational Economics (Computer Science, Economics & Data Science) at The George Washington University
Doctor of Philosophy - PhD, Machine Learning & Public Policy, Doctor of Philosophy - PhD, Machine Learning & Public Policy at Carnegie Mellon University