Speaker Profile
Ph.D., Chief Data and AI Officer, CareDx
Biography
Jing Huang received her B. A. in Statistics and Probability from Peking University and her Ph. D. in Statistics and M. S. in Epidemiology from Stanford University. With over 20 years in the biomedical field, her work centers on statistical methodologies for clinical trial design, genomic analysis, and machine learning. She is the Chief Data AI Officer at Care Dx, Inc. (Nasdaq: CDNA) The Transplant Company, leading efforts to integrate advanced data science and AI into patientfacing products and internal operations to enhance care, efficiency, and scalability. Jing has coauthored more than 30 peerreviewed articles with over ten thousand citations and is coinventor on more than 20 patent filings. She is the founding president of Dah Shu, a nonprofit promoting data science research and education, and serves as chapter representative for the A SA San Francisco Bay Area Chapter. In 2023, she was elected a Fellow of the American Statistical Association for her innovative contributions and leadership.
Talk
Artificial intelligence is transforming how clinicians understand patient trajectories, optimize treatments, and deliver personalized care. Jing Huang, Ph.D., Chief Data AI Officer at CareDx, will highlight advances in predictive modeling, multiomics integration, and realworld clinical applications, sharing lessons from building trusted AI systems and a forward look at more proactive, equitable transplant care.
Session Abstract – PMWC 2026 Silicon Valley
Track Chair:
William Oh, Yale
PMWC Award Ceremony
• Nigam Shah, Stanford
• Thomas Fuchs, Lilly
Keynote: Responsible AI in Healthcare: From RWE to Agentic Systems
• Nigam Shah, Stanford
Keynote: Scaling Trusted AI: From Computational Pathology to Next-Gen Medicines
• Thomas Fuchs, Lilly
Real-World Evidence & Clinical AI: Closing the Loop Between Data and Care
• Chair: Roxana Daneshjou, Stanford
• Aashima Gupta, Google
• Brigham Hyde, Atropos Health
• Michael Pfeffer, Stanford
• Thomas Fuchs, Lilly
From Data to Decisions: Building Regulatory-Grade RWE from EHR Systems in Oncology
• Chair: Nadia Poluhina, Mayo Clinic
• Kate Estep, Flatiron Health
• Alyssa Pybus, Moffitt Cancer Center
• Julie Stein Deutsch, Johns Hopkins
• Jeremy Jones, Mayo Clinic
Predicting Outcomes: An AI Model Trained on RWE for Precision Care
• Rich Gliklich, OM1
AI for Clinical Decision Support: From Models to Bedside
• Chair: Amrita Basu, UCSF
• Anurang Revri, Stanford
• Emily Alsentzer, Stanford
• Okan Ekinci, Roche
Keynote: AI for CDS-From Models to Bedside
• Zachary Ziegler, OpenEvidence
Operationalizing AI in Health Systems: Trust, Adoption & Outcomes
• Chair: Danton Samuel Char, Stanford
• Karan Singhal, OpenAI
• Sina Bari, iMerit Technology
• Shashi Shankar, Novellia
• Hal Paz, Khosla Ventures
• Syed Mohiuddin, Anthropic
Safe, Scalable AI in Clinical Practice: What’s Working and What’s Not
• Chair: Vincent Liu, Kaiser
• Richard Milani, Sutter Health
Transforming Transplant Care Through AI: From Predictive Insights to Precision Decisions
• Jing Huang, CareDx
AI for Precision Psychiatry: Integrating Multimodal Data Into Clinical Decision Support
• Erwin Estigarribia, HEADLAMP health
Workflow-First Clinical AI: Integration Patterns, Guardrails & Change Management
• Jorge Durand, Klick Health
From Patient-Generated Data to Regulatory-Grade RWE: Design, Bias & Outcome Linkage
• Phil Johnson, Evidation
