Babadi M, et al. Precise common and rare germline CNV calling with GATK. Cancer Research 78, 2287 (2018).
Shukla SA, et al. Cancer-germline antigen expression discriminates clinical outcome to CTLA-4 blockade. Cell 173 (3), 624-633 (2018).
Babadi M, et al. Theory of parametrically amplified electron-phonon superconductivity. Physical Review B 96 (1), 01452 (2017).
Babadi M, et al. Far-from-equilibrium field theory of many-body quantum spin systems: Prethermalization and relaxation of spin spiral states in three dimensions. Physical Review X 5 (4), 041005 (2015).
Mehrtash Babadi, Ph.D.
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.