
Professor and Chair of Biomedical Informatics
Professor of Medicine
Rm 416 Eskind Biomedical Library
2209 Garland Ave
Nashville, TN
Phone: (615) 936-1556
Fax: (615) 936-1427
Email: dan.masys@vanderbilt.edu
Dr. Daniel R. Masys is Professor and Chair of the Department of Biomedical Informatics. Previously he served as Director of Biomedical Informatics at the University of California, San Diego School of Medicine, Director of the UCSD Human Research Protections Program, and Professor of Medicine. An honors graduate of Princeton University and the Ohio State University College of Medicine, he completed postgraduate training in Internal Medicine, Hematology and Medical Oncology at the University of California, San Diego, and the Naval Regional Medical Center, San Diego. He served as Chief of the International Cancer Research Data Bank of the National Cancer Institute, National Institutes of Health, and from 1986 through 1994 was Director of the Lister Hill National Center for Biomedical Communications, which is a computer research and development division of the National Library of Medicine. In this capacity he was the principal architect of the National Center for Biotechnology Information (NCBI), which hosts the data from the Human Genome Project and other resources and tools for molecular biology.
Dr. Masys is an elected member of the Institute of Medicine of the National Academy of Sciences. He is a Diplomate of the American Board of Internal Medicine in Medicine, Hematology, and Medical Oncology. He is a Fellow of the American College of Physicians, and Fellow and Past President of the American College of Medical Informatics. He is a founding associate editor of the Journal of the American Medical Informatics Association, and has received numerous awards including the NIH Director's Award, Public Health Service Oustanding Service Medal, and the US Surgeon General's Exemplary Service Medal.
Dr. Masys' research interests include methods for analysis and meta-analysis of HIV-related epidemiology data, Internet utilities for conducting clinical and translational research, and genome-phenome correlation using phenotype data derived from electronic medical records data.
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