Speaker Profile
Biography
Dr. Lungren is Chief Medical Information Officer at Nuance Communications, a Microsoft Company. As a physician and clinical machine learning researcher, he maintains a parttime interventional radiology practice at UCSF while also serving as adjunct faculty for other leading academic medical centers including Stanford and Duke. Prior to joining Microsoft, Dr Lungren was an interventional radiologist and research faculty at Stanford University Medical School where he led the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). More recently he served as Principal for Clinical AIML at Amazon Web Services in World Wide Public Sector Healthcare, focusing on business development for clinical machine learning technologies in the public cloud. His scientific work has led to more than 100 publications, including work on multimodal data fusion models for healthcare applications, new computer vision and natural language processing approaches for healthcare specific domains, opportunistic screening with machine learning for public health applications, open medical data as public good, and prospective clinical trials for clinical AI translation. He has served as advisor for early stage startups and large fortune500 companies on healthcare AI technology development and gotomarket strategy. Dr. Lungren is frequently featured in national news outlets such as NPR, Vice News, Scientific American, and he regularly speaks at national and international scientific meetings on the topic of AI in healthcare. Dr. Lungren is also a top rated instructor on Coursera where his AI in Healthcare course designed especially for learners with nontechnical backgrounds has been completed by more than 10k students around the world enrollment is open now: https:www.coursera.orglearnfundamentalmachinelearninghealthcare
Session Abstract – PMWC 2024 Silicon Valley
Track Chair:
Gad Getz, Broad Institute
- PMWC 2024 Luminary Award
Luminary Honoree: Heidi Rehm, MGH and Broad Institute
- Opening Talk: The Power of Data by Track Chair Gaddy Getz, Broad Institute
- Global Data Sharing Collaboration: Overcoming Organizational and Sectoral Barriers (PANEL)
Chair: Gad Getz, Broad Institute
- Peter Goodhand, GA4GH
- Valentina Di Francesco, NHGRI - Challenges of Remote Data Harmonization and Querying
Chair: Heidi Rhem, MGH and Broad Institute
- Andrea Ramirez, All of Us
- Simona Carini, UCSF
- Josh Peterson, Emerge/Vanderbilt University Medical Center
- Nara Sobreira, Johns Hopkins/GeneMatcher - Keynote: Machine Learning for Therapeutic Discovery
- Daphne Koller, insitro
- Exploring the Power of AI and Data Sciences in Drug Discovery
Chair: Russ Altman, Stanford University
- Kim Branson, GSK
- Sun-Gou Ji, BridgeBio
- Angela Oliveira Pisco, Insitro - Foundation Models to Advance Precision Medicine (PANEL)
Chair: Nigam Shah, Stanford
- Aashima Gupta, Google
- Matthew Lungren, Microsoft
- Rod Tarrago, AWS