Speaker Profile
Biography
Dr. Fei-Fei Li is the inaugural Sequoia Professor in the Computer Science Department at Stanford University, and a Founding Co-Director of Stanford’s Human-Centered AI Institute. She served as the Director of Stanford’s AI Lab from 2013 to 2018.
In industry, Dr. Li has served as a Vice President at Google and Chief Scientist of AI/ML at Google Cloud (2017-2018), board member or advisor in various public or private companies (notably Twitter). She is currently a Co-founder/CEO of World Labs, an AI company focusing on Spatial Intelligence and generative AI.
Dr. Li obtained her B.A. degree in physics from Princeton in 1999 with High Honors, and her PhD degree in electrical engineering from California Institute of Technology (Caltech) in 2005. She has been recognized as the Distinguished Alumni by both Princeton and Caltech in 2020 and 2024 respectively. Dr. Li also holds Honorary Doctorate Degrees from Yale University and Harvey Mudd College.
Dr. Li is a researcher and technologist in AI, currently focusing on deep learning, robotic learning, spatial intelligence and ambient intelligence for healthcare delivery. In the past she has also worked on cognitive and computational neuroscience. Dr. Li has published more than 400 scientific articles in top-tier journals and conferences in science, engineering and computer science, and is recognized as one of the most cited computer scientists. She is the inventor of ImageNet and the ImageNet Challenge, a critical large-scale dataset and benchmarking effort that has been widely regarded as one of the three driving forces of the birth of modern AI and deep learning revolution. Dr. Li is the author a popular science memoir called “The Worlds I See: Curiosity, Exploration and Discovery at the Dawn of AI”, published by Macmillan Publishers.
Dr. Li is widely recognized as a pioneer of modern AI. She is an elected Member of the National Academy of Engineering (NAE), the National Academy of Medicine (NAM) and American Academy of Arts and Sciences (AAAS). She is also a Fellow of ACM, a member of the Council on Foreign Relations (CFR). Among her many recognitions, Dr. Li is a laureate of the Queen Elizabeth Prize for Engineering (2025), VinFuture Prize (2024), a recipient of the Intel Lifetime Achievements Award (2023), the IEEE PAMI Thomas Huang Memorial Prize (2022), the IEEE PAMI Longuet-Higgins Prize (2019), the National Geographic Society Further Award (2019), the IAPR J.K. Aggarwal Prize (2016), the IEEE PAMI Mark Everingham Award (2016), the Alfred Sloan Faculty Award (2011), among others.
Session Abstract – PMWC 2027 Silicon Valley
Track Chair:
Sharat Israni, UCSF
PMWC Award Ceremony
• Regina Barzilay, MIT
• Joe Petro, Microsoft
• Curtis P. Langlotz, Stanford University
Keynote: The Future of AI in Medical Imaging
• Curtis P. Langlotz, Stanford University
From Foundation Models to Digital Twins: AI Reshaping Clinical Imaging
• Chair: Alexander Weir, Canon Medical
• Regina Barzilay, MIT
• Kevin Blyth, University of Glasgow
• Curtis P. Langlotz, Stanford University
Fireside Chat
• Eric Horvitz, Microsoft
• Curtis P. Langlotz, Stanford University
Getting Real Results from AI
• Martin Stumpe, Danaher
Keynote: AI Tools for Cancer Diagnostics and Treatment
• Regina Barzilay, MIT
From Multimodal Data to Clinical Digital Twins: Linking Imaging, Omics, and Decisions
• Stephen Quake, Stanford
AI & Omics Foundation Models Powering Translational Research
• Chair: Janusz Dutkowski, Data4Cure
• Alex Moreau, Champions Oncology
• Jadwiga Bienkowska, Pfizer
AI-Enhanced Imaging: Radiomics, Radiogenomics, and Simulation
• Chair: Chris Hare, Canon Medical
• Ángel Alberich-Bayarri, Quibim
• Kevin Blyth, University of Glasgow
• Maria del Mar Alvarez-Torres, Columbia University
Clinical Workflow Integration & Decision Support in Imaging
• Chair: Sharat Israni, UCSF
• David S. Liebeskind, UCLA
• Rajesh Shah, UCSF
• Roxana Daneshjou, Stanford University
• Mohammad Alexanderani, U Pitt
• Orly Ardon, Memorial Sloan Kettering Cancer Center
AI-Powered Serverless HPC for Scientific Discovery
• Fengbo Ren, Fovus




