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About

Shiori Sagawa is a leading researcher at the intersection of machine learning, robustness, and foundation model governance. She is best known for her pivotal contributions to understanding and mitigating distribution shift, particularly through her work on group distributionally robust optimization (Group DRO), which provides a principled framework for ensuring models perform reliably across diverse subpopulations. Her widely cited paper, "On the Opportunities and Risks of Foundation Models" (2,177 citations), co-authored as part of the Stanford CRFM, offers a landmark analysis of the promises and perils of large-scale models like BERT and GPT-3, shaping global discourse on AI safety and ethics. Sagawa’s research has been instrumental in exposing the hidden vulnerabilities of deep learning systems, especially in high-stakes applications such as healthcare and autonomous systems. Her work has earned her recognition as a rising star in the field, with her findings influencing both academic best practices and industry deployment standards. Through rigorous empirical analysis and theoretical insight, Sagawa continues to advance the science of building AI that is not only powerful but also trustworthy and equitable.