Speaker Profile
Ph.D., Professor, Harvard Public Health
Biography
John Quackenbush is Professor of Computational Biology and Bioinformatics in the Department of Biostatistics at the Harvard T. H. Chan School of Public Health. Trained in theoretical physics, he transitioned to genomics through a Human Genome Project fellowship, with subsequent appointments at the Salk Institute, Stanford University, and The Institute for Genomic Research, before joining Harvard in 2005. His research leverages biological data to understand how many small genetic effects combine to influence health and disease. Central to his work is modeling gene regulatory networks and characterizing how these networks vary across health states, by sex and gender, and between individuals. He has over 350 publications with more than 105,000 citations, and his Net Zoo software tools are used. His honors include being named a White House Open Science Champion of Change (2013) and being elected to the National Academy of Medicine in 2022. He founded Genospace in 2012, which was acquired in 2017.
Talk
Embracing Biological Complexity to Understand Disease Drivers
Target Discovery: Beyond Genomics - Revealing Hidden Layers of Biology with AI
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
