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Stephen Fleming


Pirruccello JP, Chaffin MD, Chou EL, Fleming SJ, et al. Deep learning enables genetic analysis of the human thoracic aorta. Nature Genetics. 2022;54:40-51.

Delorey TM, Ziegler CGK, Heimberg G, Normand R, Yang,Y, Segerstolpe A, Abbodanza D, Fleming SJ, Subramanian A, Montoro DT, Jagadeesh KA, Dey KK, Sen P, Slyper M, Pita-Juarez YH, Philips D, Biermann J, et al. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature. 2021;595:107–113.

Tucker NR, Chaffin M, Fleming SJ, et al. Transcriptional and cellular diversity of the human heart. Circulation. 2020;142(5):466-482.

Fleming SJ, Marioni JC, Babadi M. CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets. bioRxiv. 2019.

Stephen Fleming, Ph.D.

Stephen Fleming is a machine learning scientist II in the analytical method development group led by Mehrtash Babadi in the Data Sciences Platform of the Broad Institute of MIT and Harvard, which uses advanced machine learning techniques and probabilistic modeling to develop principled approaches to data analysis. Fleming works on developing data analysis methods for single-cell RNA sequencing data. His group is working to develop analysis methods that will be used by the Precision Cardiology Laboratory (part of the Broad–Bayer Collaboration) and the Human Cell Atlas project.

Fleming joined the Broad Institute in June 2018 after receiving his Ph.D. in physics from Harvard University, where he worked on nanopore DNA sequencing experiments and instrumentation. He also holds an M.Phil. in physics from the University of Cambridge, where he was a Churchill Scholar in the Physics of Medicine program, in addition to B.S. degrees in physics and biochemistry, both from Case Western Reserve University.

Contact Stephen Fleming via email at

April 2022