Speaker Profile
Ph.D., Associate Professor of Biomedical Data Science, Stanford U.
Biography
James an Associate Professor of Biomedical Data Science and, by courtesy, of Computer Science and Electrical Engineering at Stanford University. James works on making AI more reliable, human-compatible and statistically rigorous, and am especially interested in applications in human disease and health. James received his Ph. D from Harvard in 2014, and was at one time a member of Microsoft Research, a Gates Scholar at Cambridge and a Simons fellow at U. C. Berkeley. James joined Stanford in 2016 and am excited to also be a Chan-Zuckerberg Investigator. We are also a part of the Stanford AI Lab. His research is supported by two Chan-Zuckerberg Biohub Investigator Awards, the Sloan Fellowship, the NSF CAREER Award, a Top Ten Clinical Achievement Award and faculty awards from Google, Adobe and Amazon.
Session Abstract – PMWC 2026 Silicon Valley
Track Chair:
Alex Morgan, Khosla Ventures and Gad Getz, Broad Institute
PMWC Award Ceremony
• Steve Wozniak, Apple
• Greg Brockman, OpenAI
Fireside Chat
• Vinod Khosla, Khosla Ventures
• Greg Brockman, OpenAI
Target Discovery: Beyond Genomics – Revealing Hidden Layers of Biology with AI
• Chair: Cindy Lawley, Olink
• Aritro Nath, City of Hope
• John Quackenbush, Harvard
• Massa Shoura, Phinomics
• Omar Serang, DNAnexus
Foundation Models of Human Cancer Biology to Predict Clinical Outcomes
• Ron Alfa, Noetik
Mechanistic Modeling of the Human Immune System: A Data-Integrated Approach to Target and Biomarker Discovery
• Liat Dassa, CytoReason
Biological Foundation Models: Harmonizing Data to Accelerate Drug Discovery
• Vitalay Fomin, Numenos
From Prediction to Translation: AI and In Vivo Validation to Improve Drug Development Success
• Gabriel Musso, BioSymetrics
Interpretable AI for Biomarker Discovery: Accelerating Drug Development and Advancing Precision Medicine
• Chair: Shivanni Kummar, PATHOMIQ
• Dale Muzzey, Myriad Genetics
• Sanoj Punnen, University of Miami
• Mark Burkard, UI
Can AI Really Create the Next Blockbuster Drug? Closing the Loop from Drug Discovery to Development
• Chair: Amar Das, Guardant Health
• Ari Caroline, Weave Bio
• Dina Katabi, Emerald/MIT
• Andrei Georgescu, Vivodyne
• James Zou, Stanford
Gemini Digital Twins Accelerate Precision Medicine
• Collin Hill, Aitia
Scaling Rare Disease Discovery with AI: From Genomic Data to Therapeutic Insights
• Lisa Gurry, GeneDx
Limited Sample Models for Faster Lead Discovery, High Accuracy, and Regulatory Grade AI
• Lalin Theverapperuma, Expert Intelligence
Enabling Targeted Precision Drug & Gene Delivery with Predictive AI
• Andre Watson, Ligandal
Is AI the New Drug or the New Therapeutic Modality
• Chair: Alex Morgan, Khosla Ventures
• Michael J. Kahana, Nia Therapeutics
• Marc Tessier-Lavigne, Xaira
