Institute for Human-Centered Artificial Intelligence (HAI)
Showing 141-150 of 244 Results
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Matthew Lungren
Adjunct Professor, Biomedical Data Science
BioDr. Matthew Lungren is a physician-scientist and AI leader whose work has helped shape modern multimodal healthcare AI from early research through large-scale deployment. He joined Stanford University in 2014 as clinical research faculty, where he led a fully dedicated pediatric interventional radiology clinical service and established an NIH- and industry-supported clinical AI research program that helped catalyze what became the Stanford Center for AI in Medicine & Imaging. He remains an Adjunct Professor of Biomedical Data Science at Stanford and also holds a part-time clinical appointment at UCSF.
Dr. Lungren has authored more than 200 peer-reviewed publications with more than 35,000 citations, and he has taught more than 100,000 learners through AI-in-healthcare courses across platforms including Coursera and LinkedIn Learning. His broader contributions include advancing multimodal imaging-plus-EHR approaches, open-sourcing AI-ready medical imaging datasets and models, and serving in national leadership roles across the radiology AI community. After a sabbatical in 2021, he transitioned from academia to industry and joined Microsoft, where he served in senior leadership roles including Chief Scientific Officer for Microsoft Health & Life Sciences. At Microsoft, he founded and led cross-company teams that shipped multimodal healthcare foundation models and agentic, auditable generative AI workflows into production, including healthcare agent orchestration capabilities and major EHR partnerships, and led the health and life sciences partnerships with OpenAI.
Dr. Lungren is also a top rated instructor leading AI in Healthcare courses designed especially for learners with non-technical backgrounds:
Stanford/Coursera: https://www.coursera.org/learn/fundamental-machine-learning-healthcare
LinkedIn Learning: https://www.linkedin.com/learning/an-introduction-to-how-generative-ai-will-transform-healthcare -
Katharine (Kate) Maher
Professor of Earth System Science, Senior Fellow at the Woods Institute for the Environment and Professor, by courtesy, of Earth and Planetary Sciences
Current Research and Scholarly InterestsHydrology, reactive transport modeling and environmental geochemistry
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Christopher Manning
Thomas M. Siebel Professor of Machine Learning, Professor of Linguistics, of Computer Science and Senior Fellow at the Stanford Institute for Human-Centered AI
On Leave from 10/01/2025 To 06/30/2026BioChristopher Manning is the inaugural Thomas M. Siebel Professor in Machine Learning in the Departments of Linguistics and Computer Science at Stanford University, Director of the Stanford Artificial Intelligence Laboratory (SAIL), and an Associate Director of the Stanford Institute for Human-Centered Artificial Intelligence (HAI). From 2010, Manning pioneered Natural Language Understanding and Inference using Deep Learning, with impactful research on sentiment analysis, paraphrase detection, the GloVe model of word vectors, attention, neural machine translation, question answering, self-supervised model pre-training, tree-recursive neural networks, machine reasoning, dependency parsing, and summarization, work for which he has received two ACL Test of Time Awards and the IEEE John von Neumann Medal (2024). He earlier led the development of empirical, probabilistic approaches to NLP, computational linguistics, and language understanding, defining and building theories and systems for Natural Language Inference, syntactic parsing, machine translation, and multilingual language processing, work for which he won ACL, Coling, EMNLP, and CHI Best Paper Awards. In NLP education, Manning coauthored foundational textbooks on statistical approaches to NLP (Manning and Schütze 1999) and information retrieval (Manning, Raghavan, and Schütze, 2008), and his online CS224N Natural Language Processing with Deep Learning course videos have been watched by hundreds of thousands. In linguistics, Manning is a principal developer of Stanford Dependencies and Universal Dependencies, and has authored monographs on ergativity and complex predicates. He is the founder of the Stanford NLP group (@stanfordnlp) and was an early proponent of open source software in NLP with Stanford CoreNLP and Stanza. He is an ACM Fellow, a AAAI Fellow, and an ACL Fellow, and a Past President of the ACL (2015). Manning has a B.A. (Hons) from The Australian National University, a Ph.D. from Stanford in 1994, and an Honorary Doctorate from U. Amsterdam in 2023. He held faculty positions at Carnegie Mellon University and the University of Sydney before returning to Stanford.
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David J. Maron
C. F. Rehnborg Professor and Professor of Medicine (Stanford Prevention Research Center)
Current Research and Scholarly InterestsDr. Maron is the Co-Chair and Principal Investigator of the ISCHEMIA trial, and Co-Chair of the ISCHEMIA-CKD trial. These large, international, NIH-funded studies will determine whether an initial invasive strategy of cardiac catheterization and revascularization plus optimal medical therapy will reduce cardiovascular events in patients with and without chronic kidney disease and at least moderate ischemia compared to an initial conservative strategy of optimal medical therapy alone.
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Jay McClelland
Lucie Stern Professor in the Social Sciences, Professor of Psychology and, by courtesy, of Linguistics and of Computer Science
Current Research and Scholarly InterestsMy research addresses topics in perception and decision making; learning and memory; language and reading; semantic cognition; and cognitive development. I view cognition as emerging from distributed processing activity of neural populations, with learning occurring through the adaptation of connections among neurons. A new focus of research in the laboratory is mathematical cognition and reasoning in humans and contemporary AI systems based on neural networks.
