Institute for Human-Centered Artificial Intelligence (HAI)
Showing 241-258 of 258 Results
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Risa Wechsler
Director, Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), Humanities and Sciences Professor, Professor of Physics and of Particle Physics and Astrophysics, and Senior Fellow at Stanford HAI
BioRisa Wechsler is the Humanities and Sciences Professor and the Director of the Kavli Institute of Particle Astrophysics and Cosmology. She is also Professor of Physics (H&S) and Professor of Particle Physics & Astrophysics (SLAC), Director of the Center for Decoding the Universe, and an Associate Director at Stanford Data Science. She is a cosmologist whose work investigates some of the biggest outstanding questions about our universe — how it formed, what it is made of, how it is structured, and what its future holds.
Her research focuses on understanding the evolution of galaxies, the large-scale structure of the universe, and the nature of dark matter and dark energy. She uses large numerical simulations, theoretical models, and the largest observed maps of the universe to explore these forces that shape the cosmos. Her recent work also investigates the formation and cosmological context of the Milky Way and probes dark matter through small-scale cosmic structure, and explores how data science and AI/ML can drive new understanding. Wechsler has played key leadership roles in major international collaborations including the Dark Energy Survey, Dark Energy Spectroscopic Instrument, and Rubin Observatory's Legacy Survey of Space and time, a decade-long survey that will reveal the dynamic universe in unprecedented detail. She is recently involved in the Via Survey, which will map the Milky Way at high precision to probe dark matter physics in new ways.
Wechsler is an elected member of the National Academy of Sciences and the American Academy of Arts and Sciences and a Fellow of the American Physical Society and the American Association for the Advancement of Science.
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Terry Winograd
Professor of Computer Science, Emeritus
BioProfessor Winograd's focus is on human-computer interaction design and the design of technologies for development. He directs the teaching programs and HCI research in the Stanford Human-Computer Interaction Group, which recently celebrated it's 20th anniversary. He is also a founding faculty member of the Hasso Plattner Institute of Design at Stanford (the "d.school") and on the faculty of the Center on Democracy, Development, and the Rule of Law (CDDRL)
Winograd was a founding member and past president of Computer Professionals for Social Responsibility. He is on a number of journal editorial boards, including Human Computer Interaction, ACM Transactions on Computer Human Interaction, and Informatica. He has advised a number of companies started by his students, including Google. In 2011 he received the ACM SIGCHI Lifetime Research Award. -
Jiajun Wu
Assistant Professor of Computer Science and, by courtesy, of Psychology
BioJiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University, working on computer vision, machine learning, robotics, and computational cognitive science. Before joining Stanford, he was a Visiting Faculty Researcher at Google Research. He received his PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. Wu's research has been recognized through the IJCAI Computers and Thought Award, the Young Investigator Programs (YIP) by ONR and by AFOSR, the NSF CAREER award, the Okawa research grant, the AI's 10 to Watch by IEEE Intelligent Systems, paper awards and finalists at ICCV, CVPR, SIGGRAPH Asia, ICRA, CoRL, and IROS, dissertation awards from ACM, AAAI, and MIT, the 2020 Samsung AI Researcher of the Year, and faculty research awards from Microsoft, Google, Nvidia, J.P. Morgan, Samsung, Amazon, and Meta.
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Lei Xing
Jacob Haimson and Sarah S. Donaldson Professor and Professor, by courtesy, of Electrical Engineering
Current Research and Scholarly Interestsartificial intelligence in medicine, medical imaging, Image-guided intervention, molecular imaging, biology guided radiation therapy (BGRT), treatment plan optimization
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Daniel Yamins
Associate Professor of Psychology and of Computer Science
Current Research and Scholarly InterestsOur lab's research lies at intersection of neuroscience, artificial intelligence, psychology and large-scale data analysis. It is founded on two mutually reinforcing hypotheses:
H1. By studying how the brain solves computational challenges, we can learn to build better artificial intelligence algorithms.
H2. Through improving artificial intelligence algorithms, we'll discover better models of how the brain works.
We investigate these hypotheses using techniques from computational modeling and artificial intelligence, high-throughput neurophysiology, functional brain imaging, behavioral psychophysics, and large-scale data analysis. -
Seema Yasmin
Clinical Assistant Professor, Medicine - Primary Care and Population Health
BioSeema Yasmin is an Emmy Award-winning journalist, poet, medical doctor and author. Yasmin served as an officer in the Epidemic Intelligence Service at the U.S. Centers for Disease Control and Prevention where she investigated disease outbreaks and was principal investigator on a number of CDC studies. Yasmin trained in journalism at the University of Toronto and in medicine at the University of Cambridge.
Yasmin was a finalist for the Pulitzer Prize in breaking news in 2017 with a team from The Dallas Morning News for coverage of a mass shooting, and recipient of an Emmy award for her reporting on neglected tropical diseases and their impact on resource poor communities in the U.S. She received multiple grants from the Pulitzer Center on Crisis Reporting for coverage of gender based violence in India and the aftermath of the Ebola epidemic in West Africa. In 2017, Yasmin was a John S. Knight Fellow in Journalism at Stanford University investigating the spread of health misinformation and disinformation during public health crises. Previously she was a science correspondent at The Dallas Morning News, medical analyst for CNN, and professor of public health at the University of Texas at Dallas. She teaches crisis management and crisis communication at the UCLA Anderson School of Management as a Visiting Assistant Professor.
She is the author of 12 non-fiction, fiction, poetry and childrens books, including: Can Scientists Succeed Where Politicians Fail? (Johns Hopkins University Press, 2025) which was co-authored with Nobel laureate Dr. Peter Agre; What the Fact?! Finding the Truth in All the Noise (Simon and Schuster, 2022); Viral BS: Medical Myths and Why We Fall For Them (Johns Hopkins University Press, 2021); Muslim Women Are Everything: Stereotype-Shattering Stories of Courage, Inspiration and Adventure (HarperCollins, 2020); If God Is A Virus: Poems (Haymarket, 2021); Unbecoming: A Novel (Simon and Schuster, 2024); Djinnology: An Illuminated Compendium of Spirits and Stories from the Muslim World (Chronicle, 2024); and The ABCs of Queer History (Workman Books, 2024). Her writing appears in The New York Times, Rolling Stone, WIRED, Scientific American and other outlets.
Yasmin’s unique expertise in epidemics and communications has been called upon by the Vatican, the Presidential Commission for the Study of Bioethical Issues, the Aspen Institute, the Skoll Foundation, the Biden White House, and others. She teaches a new paradigm for trust-building and evidence-based communication to leadership at the World Health Organization and CDC. In 2019, she was the inaugural director of the Stanford Health Communication Initiative.
Her scholarly work focuses on the spread of scientific misinformation and disinformation, information equity, and the varied susceptibilities of different populations to false information about health and science. In 2020, she received a fellowship from the Emerson Collective for her work on inequitable access to health information. She teaches multimedia storytelling to medical students in the REACH program. -
Serena Yeung-Levy
Assistant Professor of Biomedical Data Science and, by courtesy, of Electrical Engineering and of Computer Science
BioDr. Serena Yeung-Levy is an Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering at Stanford University. Her research focus is on developing artificial intelligence and machine learning algorithms to enable new capabilities in biomedicine and healthcare. She has extensive expertise in deep learning and computer vision, and has developed computer vision algorithms for analyzing diverse types of visual data ranging from video capture of human behavior, to medical images and cell microscopy images.
Dr. Yeung-Levy leads the Medical AI and Computer Vision Lab at Stanford. She is affiliated with the Stanford Artificial Intelligence Laboratory, the Clinical Excellence Research Center, and the Center for Artificial Intelligence in Medicine & Imaging. She is also a Chan Zuckerberg Biohub Investigator and has served on the NIH Advisory Committee to the Director Working Group on Artificial Intelligence. -
Greg Zaharchuk
Professor of Radiology (Neuroimaging and Neurointervention)
Current Research and Scholarly InterestsImproving medical image value using AI
Stroke and dementia imaging
Outcome prediction with AI
Imaging of cerebral hemodynamics with MRI and CT
Noninvasive oxygenation measurement with MRI
PET/MRI in Neuroradiology
Resting-state fMRI for perfusion imaging and stroke -
Amy Zegart
Morris Arnold and Nona Jean Cox Senior Fellow at the Hoover Institution, Senior Fellow at Freeman Spogli Institute for International Studies, at Stanford Institute for Human-Centered AI, & Professor, by courtesy, of Political Science
Current Research and Scholarly InterestsU.S. intelligence, cybersecurity, political risk, grand strategy
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Daniel Zhang
Chief of Staff, Institute for Human-Centered Artificial Intelligence (HAI)
BioDaniel Zhang is the chief of staff at the Stanford Institute for Human-Centered Artificial Intelligence (HAI). Previously, he led 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. -
Harrison G. Zhang
MD Student, expected graduation Spring 2028
Ph.D. Student in Biomedical Data Science, admitted Autumn 2025
MSTP Student
Grad Student, Institute for Human-Centered Artificial Intelligence (HAI)BioHarrison is an artificial intelligence researcher, Stanford MD-PhD trainee in the Medical Scientist Training Program, and Columbia alumnus advancing precision medicine and drug discovery. He studied statistics and biology at Columbia, where he was elected to Phi Beta Kappa and awarded Magna Cum Laude with Highest Honors in Field for his academic achievements.
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James Zou
Associate 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.
