Sam is a machine learning scientist in the data sciences platform at the Broad Institute. He received a BS in electric media and obsolescence at Hunter College and a PhD in computer science at the Graduate Center at the City University of New York. His thesis presented algorithms for object detection and registration in 3D point clouds. Sam previously worked at Apple building machine learning tools for semantic segmentation and 3D modeling at massive scales.
Sam is keenly interested in how machine learning can advance our understanding of cardiovascular disease to impact clinical practice. As technical lead for the Broad Institute’s Machine Learning for Cardiovascular Disease group, he develops model architectures, interprets learned features, and tries to fuse multiple modalities into meaningful representations.
