BIO

Aisha Nájera is an AI Policy Fellow at the Princeton AI Lab , and a senior mathematician at the RAND Corporation. Her research focuses on AI safety, risk governance and public policy for federal and state agencies. She is the recipient of the 2024 NIST ARIA Red-Teaming Award and a winner of the Stanford Accelerator for Learning Create + AI Challenge, sponsored by Google.org. She served as editor of the Springer volume Research in Mathematics and Public Policy.

At Princeton, she advises the California Health and Human Services Agency’s Office of Technology and Solutions Integration (OTSI) on responsible AI adoption, shaping risk management and procurement policy for large-scale GenAI deployment. Her current research develops novel LLM evaluation methods for government use cases where standard accuracy metrics fall short.

At RAND, she co-led the development of a digital content forgery mitigation framework for DHS, led RAND's public comment on NIST AI 100-4 (with analysis incorporated into the final published standard), and contributed to CISA's cross-sector analysis of AI threats to critical infrastructure. She is a past member of the NIST AI Safety Institute Consortium. Her defense research applies ML and deep learning to Army equipment readiness, demand forecasting, and supply chain cybersecurity, with findings briefed to senior Army leadership. She also co-authored research on U.S. grid capacity constraints limiting AI infrastructure growth and provided public comments to the DOE on AI data center siting

She co-organized an NSF-funded workshop at UCLA's Institute for Pure and Applied Mathematics, convening mathematicians and policymakers on cybersecurity and climate change. Earlier in her career, she was a consultant with IBM Global Business Services. She holds a Ph.D. in mathematics from Claremont Graduate University, an M.S. from the University of Arizona, and a B.S. from the National University of Mexico. She is fluent in English and Spanish.