Emeritus Professor
Biomedical Data Science & Radiology
Stanford University
FACMI — American College of Medical Informatics
FSIIM — Society of Imaging Informatics in Medicine
Distinguished Investigator Award
Academy for Radiology & Biomedical Imaging Research
Honored Educator Award (2012, 2013)
Radiological Society of North America
Biomedical Data Science at Stanford
Daniel L. Rubin, MD, MS is Emeritus Professor of Biomedical Data Science and Radiology at Stanford University, where he was also Professor of Medicine (Biomedical Informatics) and Ophthalmology (by courtesy). Over a career spanning more than two decades at Stanford, he established one of the leading programs in biomedical imaging informatics and data science.
He served as Principal Investigator of two centers in the National Cancer Institute's Quantitative Imaging Network (QIN), Director of Biomedical Informatics for the Stanford Cancer Institute, Director of the Research Informatics Center of the School of Medicine, and Director of the Scholarly Concentration in Informatics and Data Driven Medicine at Stanford School of Medicine.
His NIH-funded research program focused on quantitative imaging — integrating imaging data with clinical and molecular data to discover imaging biomarkers, define disease subtypes, and personalize cancer treatment. He chaired the Informatics Committee of the ECOG-ACRIN cooperative group, the Quantitative Imaging Network Executive Committee, and the RadLex Steering Committee of the Radiological Society of North America.
He has published more than 400 scientific papers in biomedical imaging informatics, data science, and radiology, and is a Fellow of AIMBE, ACMI, and SIIM. He is a Faculty Affiliate of Stanford's Institute for Human-Centered AI (HAI) and a Member of the Stanford Cancer Institute, Bio-X, the Cardiovascular Institute, and the Wu Tsai Neurosciences Institute.
From Biomedical Research to the Performing Arts
As Emeritus Professor, Dr. Rubin recognized a compelling opportunity: the same data science and artificial intelligence techniques that transformed biomedical research can address urgent challenges facing the performing arts. Opera — one of humanity's most ambitious artistic achievements — is experiencing a measurable decline in attendance, with younger audiences largely absent from concert halls and opera houses. These are tractable problems for data science, machine learning, and AI.
Working in conjunction with the Opera Verace Foundation (OVF) — a California 501(c)(3) public charity co-founded by Dr. Rubin and Professor Terry Desser (Professor Emerita of Radiology, Stanford) — this laboratory develops data science methods to understand and reverse the decline of opera audiences, with a particular focus on attracting listeners aged 18–45. OVF partners with leading opera institutions in North America and Europe to deploy these approaches at scale.
The research program rests on three pillars: conversational AI agents and content generation to make opera discoverable; an AI-powered live-avatar docent to guide new audiences through the art form; and the STAGE data platform to enable rigorous, cross-institutional study of audience behavior and engagement.
View the Three Research Pillars →