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. She has been working in the biomedical field for over 20 years and her research interest focuses on statistical methodologies in clinical trial design, genomic analysis, and machine learning. She is currently the Chief Data AI Officer at CareDx, Inc. (Nasdaq: CDNA)- The Transplant Company. She is responsible for advancing CareDxs efforts to integrate data science methods and AI into its customer facing products to enhance patient care, and its internal business operations to achieve improved efficiency and scalability. Jing has co-authored more than 30 articles in peer-reviewed scientific journals with over ten thousand citations and is co-inventor of over 20 patent filings. Besides her daily work, she actively promotes data science through many of her volunteer activities: She is the founding president of DahShu, a 501(c)(3) nonprofit organization with the mission of promoting research and education in data science. She is currently the chapter representative of American Statistical Association San Francisco Bay Area Chapter (SFASA). In 2023, Jing was elected as lifetime Fellow of the American Statistical Association to recognize her outstanding contributions to the medical research community in the field of statistics; for numerous statistical innovations in genomic tests; and for exemplary leadership and community service to the profession.
Emerging Therapeutics Showcase:
Forta Bio
Session Abstract – PMWC 2026 Silicon Valley
Track Chair:
William Oh, Yale
PMWC Award Ceremony
• Nigam Shah, Stanford
• Thomas Fuchs, Lilly
Keynote
• Thomas Fuchs, Lilly
Keynote
• Nigam Shah, Stanford
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: Kate Estep, Flatiron Health
AI and Real-World Evidence: Building a Learning Health System 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
AI for CDS: From Models to Bedside
• Zachary Ziegler, OpenEvidence
Operationalizing AI in Health Systems: Trust, Adoption & Outcomes
• Chair: Danton Samuel Char, Stanford
• Matthew Solomon, Sutter Health
• Karan Singhal, OpenAI
• Sina Bari, iMerit Technology
• Shashi Shankar, Novellia
• Hal Paz, Khosla Ventures
Safe, Scalable AI in Clinical Practice: What’s Working and What’s Not
• Chair: Vincent Liu, Kaiser
• David Entwistle, Stanford Health Care
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 Duran, Klick Health
From Patient-Generated Data to Regulatory-Grade RWE: Design, Bias & Outcome Linkage
• Greg Bowyer, Evidation
