Speaker Profile
Ph.D., Director of Product Planning, CytoReason
Biography
Dr. Liat Dassa is a product leader and a scientist at CytoReason, a leading biological intelligence company that builds computational disease models for biopharma R&D.
Liat works at the intersection of product, business, and data, working closely with leadership to translate product vision into actionable priorities. She’s experienced with leading the product data strategy, including identification of high-value multi-omics data, external partnerships, and integration into ML-based product capabilities to drive clinical outcomes. Liat has led multidisciplinary teams across immunology, bioinformatics, and data management to turn computational disease models into products that scientists trust and adopt. With a strong academic background in immunology, she brings a practical perspective on how AI can be embedded into biotech R&D environments to drive real-world clinical impact.
She has previously spoken at PMWC on behalf of CytoReason and is passionate about applying data-driven approaches that support confident decision-making, accelerate discovery, and improve patient outcomes.
Talk
Prioritizing Targets and Indications Using Disease Models
CytoReason's computational disease models draw on molecular and clinical data to capture disease biology. This talk showcases how pharma R&D teams leverage CytoReason’s AI technology to prioritize targets and indications, and to make faster, more accurate decisions at critical inflection points.
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
