Mehrtash Babadi is a group leader in machine learning at the Broad Institute of MIT and Harvard and is a member of the Data Sciences Platform. He is currently leading the development of novel machine learning algorithms for analyzing high-throughput single-cell assays, including modeling technical and biological aspects of single-cell RNA sequencing data (in collaboration with the Precision Cardiology Laboratory), end-to-end unsupervised summarization of in situ image-based molecular profiling data, and multi-modal predictive modeling of optical electrophysiology data (in collaboration with the Optical Profiling Platform). He has also led the Genomic Analysis Toolkit team's effort to develop an accurate germline copy-number variant calling tool.

Prior to joining the Broad Institute in 2016, Babadi spent two years as a postdoctoral fellow at the Institute for Quantum Information and Matter at California Institute of Technology, where he worked on the theory of non-equilibrium superconductivity and relaxation of far-from-equilibrium quantum many-body systems.

Babadi holds a Ph.D. in theoretical condensed matter physics from Harvard University and B.Sc. degrees in mathematics and physics from Sharif University of Technology.