School of Medicine
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Dan Riskin
Clinical Professor (Affiliated), Surgery - General Surgery
Staff, Surgery - General SurgeryBioDr. Riskin is a Clinical Professor of Surgery at Stanford University and a physician–entrepreneur focused on the application of artificial intelligence in healthcare. His work spans clinical practice, company building, and public policy, with a focus on using clinical data to improve care.
He has founded and led multiple healthcare technology companies applying artificial intelligence to clinical data, with products used by leading health systems, pharmaceutical companies, and insurers and influencing the care of millions of patients. He is the CEO of Verantos, a healthcare AI company focused on improving the reliability of real-world clinical data used in research and care.
Dr. Riskin has contributed to health policy in the United States and internationally, including Congressional testimony related to the 21st Century Cures Act and service on the U.S. Health Information Technology Advisory Committee (HITAC). His work has been featured in The Wall Street Journal and Forbes.
Dr. Riskin received his MD from Boston University, completed residency in surgery at UCLA, and fellowship training in critical care and acute care surgery at Stanford University. He is board-certified in surgery, critical care, palliative care, and clinical informatics. He also holds an MBA from the Massachusetts Institute of Technology and was a Stanford Biodesign Innovation Fellow. -
Jorge Roa
Software Developer Associate, Health Policy
BioJorge Roa is a software developer and data scientist in the Department of Health Policy at Stanford University. Prior to joining Stanford, Jorge completed a research fellowship in the Department of Statistics at the University of Munich. He holds an M.Sc. in Data Science for Public Policy from the Hertie School in Berlin, Germany. Jorge earned a B.A. in Public Policy from the Center for Research and Teaching in Economics (CIDE) in Aguascalientes, Mexico. His work has focused on gastric and colorectal cancer research, helping apply Bayesian methods and decision-analytic models, as well as creating and optimizing algorithms. He also has experience in developing and implementing open-source R packages. Jorge is part of the colorectal cancer group within the Cancer Intervention and Surveillance Modeling Network (CISNET). His research centers on employing data science tools and decision-analytic models to make informed decisions based on data and evidence to improve people’s lives.
