Institute for Human-Centered Artificial Intelligence (HAI)
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Morris Arnold and Nona Jean Cox Senior Fellow at the Hoover Institution, Senior Fellow at the Freeman Spogli Institute for International Studies, and Professor, by courtesy, of Political Science
Current Research and Scholarly InterestsU.S. intelligence, cybersecurity, political risk, grand strategy
Social Science Research Scholar, Institute for Human-Centered Artificial Intelligence (HAI)
BioDaniel Zhang is the senior manager for policy initiatives at the Stanford Institute for Human-Centered Artificial Intelligence (HAI) where he leads the Institute's policy research, outreach, and education initiatives. With the goal of developing evidence-based AI policy recommendations, his research interests lie at the intersection of technology policy, governance, and societal impact, including translational and original research on AI regulation and standards, the geopolitical implication of emerging technology, and the governance of large-scale ML models.
Daniel is also a member of the High-Level Expert Group on AI Ethics at UNESCO, advising the agency on the implementation of its Recommendation on the Ethics of AI. Previously, he was the manager of the AI Index where he lead-authored the 2021 and 2022 annual reports that measure and evaluate the rapid rate of AI advancement.
Before Stanford, he worked on global AI talent flows and security risks at the Center for Security and Emerging Technology and public education policy at the Riley Institute Center for Education and Leadership. Daniel holds a Master's in Security Studies from Georgetown University's Walsh School of Foreign Service, where he concentrated on technology policy, and a Bachelor's from Furman University.
Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering
Current Research and Scholarly InterestsMy group works on both foundations of statistical machine learning and applications in biomedicine and healthcare. We develop new technologies that make ML more accountable to humans, more reliable/robust and reveals core scientific insights.
We want our ML to be impactful and beneficial, and as such, we are deeply motivated by transformative applications in biotech and health. We collaborate with and advise many academic and industry groups.