Chief Data Officer and Institute Scientist
Co-Director, Eric and Wendy Schmidt Center
Broad Institute of MIT and Harvard
Anthony Philippakis is the chief data officer of the Broad
Institute of MIT and Harvard, and the co-director of the Eric and Wendy Schmidt Center.
He trained as a cardiologist at Brigham and Women’s Hospital, with
a focus on rare genetic cardiovascular diseases. At the Broad Institute he is
the founding director of the Data Sciences Platform, an organization of over
200 software engineers and computational biologists that develops software for
analyzing genomic and clinical data. In addition to his roles at the Broad
Institute and Brigham and Women’s Hospital, Philippakis is a venture partner at
GV, focusing on machine learning, distributed computing, and genomics.
Philippakis received his M.D. from Harvard Medical School and
completed a Ph.D. in biophysics at Harvard. As an undergraduate, he studied
mathematics at Yale University, and later completed the Part III (equivalent to
M.Phil.) in mathematics at Cambridge University.
Chief Data Officer and Institute Scientist
Co-Director, Eric and Wendy Schmidt Center
Broad Institute of MIT and Harvard
Anthony Philippakis is the chief data officer of the Broad
Institute of MIT and Harvard, and the co-director of the Eric and Wendy Schmidt Center.
He trained as a cardiologist at Brigham and Women’s Hospital, with
a focus on rare genetic cardiovascular diseases. At the Broad Institute he is
the founding director of the Data Sciences Platform, an organization of over
200 software engineers and computational biologists that develops software for
analyzing genomic and clinical data. In addition to his roles at the Broad
Institute and Brigham and Women’s Hospital, Philippakis is a venture partner at
GV, focusing on machine learning, distributed computing, and genomics.
Philippakis received his M.D. from Harvard Medical School and
completed a Ph.D. in biophysics at Harvard. As an undergraduate, he studied
mathematics at Yale University, and later completed the Part III (equivalent to
M.Phil.) in mathematics at Cambridge University.
Journal article
CONCLUSIONS: ECG2HF is a publicly available 12-lead ECG-based artificial intelligence model that discriminates the risk of future HF with favorable and consistent performance across 3 large health care samples from the northeastern United States. ECG2HF may enable efficient prioritization of high-risk individuals for HF-related preventive measures.
Journal article
CONCLUSIONS: Artificial intelligence-enabled analysis of the ECG may facilitate identification of individuals with possible undiagnosed CAD and inform downstream testing and preventive measures.