Institute for Human-Centered Artificial Intelligence (HAI)
Showing 11-15 of 15 Results
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Ron Li
Clinical Associate Professor, Medicine
BioRon Li is a Clinical Associate Professor of Medicine in the Division of Hospital Medicine and Center for Biomedical Informatics Research at Stanford University School of Medicine. As the Medical Informatics Director for Digital Health at Stanford Health Care, he provides medical and informatics direction for the health system's enterprise digital health portfolio, including expanding digital referral networks and virtual care modalities. He is the co-founder and Director for the Stanford Emerging Applications Lab (SEAL), which helps clinicians and staff build ideas into novel digital products that are prototyped and tested for care delivery at Stanford Health Care.
Ron's academic interests focus on the "delivery science" of new technological capabilities such as digital and artificial intelligence in healthcare and how to design, implement, and evaluate new tech enabled models of care delivery. Ron's work spans across multiple disciplines, including clinical medicine, data science, digital health, information technology, design thinking, process improvement, and implementation science. He has consulted for various companies in the digital health and artificial intelligence space. He is an attending physician on the inpatient medicine teaching service at Stanford Hospital and is the Associate Program Director for the Stanford Clinical Informatics Fellowship. -
Percy Liang
Associate Professor of Computer Science, Senior Fellow at the Stanford Institute for HAI, and Associate Professor, by courtesy, of Statistics
BioPercy Liang is an Associate Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011) and the director of the Center for Research on Foundation Models (CRFM). He is currently focused on making foundation models (in particular, language models) more accessible through open-source and understandable through rigorous benchmarking. In the past, he has worked on many topics centered on machine learning and natural language processing, including robustness, interpretability, human interaction, learning theory, grounding, semantics, and reasoning. He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets. His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), a Microsoft Research Faculty Fellowship (2014), and paper awards at ACL, EMNLP, ICML, COLT, ISMIR, CHI, UIST, and RSS.
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C. Karen Liu
Professor of Computer Science
BioC. Karen Liu is a professor in the Computer Science Department at Stanford University. Prior to joining Stanford, Liu was a faculty member at the School of Interactive Computing at Georgia Tech. She received her Ph.D. degree in Computer Science from the University of Washington. Liu's research interests are in computer graphics and robotics, including physics-based animation, character animation, optimal control, reinforcement learning, and computational biomechanics. She developed computational approaches to modeling realistic and natural human movements, learning complex control policies for humanoids and assistive robots, and advancing fundamental numerical simulation and optimal control algorithms. The algorithms and software developed in her lab have fostered interdisciplinary collaboration with researchers in robotics, computer graphics, mechanical engineering, biomechanics, neuroscience, and biology. Liu received a National Science Foundation CAREER Award, an Alfred P. Sloan Fellowship, and was named Young Innovators Under 35 by Technology Review. In 2012, Liu received the ACM SIGGRAPH Significant New Researcher Award for her contribution in the field of computer graphics.
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Matthew Lungren
Adjunct Professor, Biomedical Data Science
BioDr. Lungren is Chief Data Science Officer for Microsoft Health & Life Sciences where he focuses on translating cutting edge technology, including generative AI and cloud services, into innovative healthcare applications. As a physician and clinical machine learning researcher, he maintains a part-time clinical practice at UCSF while also continuing his research and teaching roles as adjunct professor at Stanford University.
Prior to joining Microsoft, Dr Lungren was a clinical interventional radiologist and research faculty at Stanford University Medical School where he led the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). He later served as Principal for Clinical AI/ML at Amazon Web Services in World Wide Public Sector Healthcare, focusing on business development for clinical machine learning technologies in the public cloud.
His scientific work has led to more than 150 publications, including work on multi-modal data fusion models for healthcare applications, new computer vision and natural language processing approaches for healthcare specific domains, opportunistic screening with machine learning for public health applications, open medical data as public good, prospective clinical trials for clinical AI translation, and application of generative AI in healthcare. He has served as advisor for early stage startups and large fortune-500 companies on healthcare AI technology development and go-to-market strategy. Dr. Lungren's work has been featured in national news outlets such as NPR, Vice News, Scientific American, and he regularly speaks at national and international scientific meetings on the topic of AI in healthcare.
Dr. Lungren is also a top rated instructor on Coursera where his AI in Healthcare course designed especially for learners with non-technical backgrounds has been completed by more than 20k students around the world - enrollment is open now: https://www.coursera.org/learn/fundamental-machine-learning-healthcare
