Stephen Fleming
Senior Machine Learning Scientist I at Broad Institute of MIT and Harvard
Cambridge, Massachusetts, United States
Summary
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Senior🎓
Top SchoolStephen Fleming is a Senior Machine Learning Scientist at the Broad Institute with 11 years of experience developing computational methods for single-cell RNA sequencing and other genomic data. Trained as an experimental physicist (PhD, Churchill Scholar at Cambridge), he blends rigorous physical science intuition with machine learning and software engineering to solve biological problems. At Broad he progressed from computational scientist to senior ML scientist, contributing to data-science platforms that enable large-scale single-cell analyses. His background includes nanopore DNA sequencing and mathematical biology, giving him a rare mix of wet-lab-adjacent experimental insight and computational expertise. Stephen is based in Cambridge, MA, and is known for turning complex biological questions into reproducible, production-ready analysis tools. He often bridges research and engineering priorities, ensuring methods are both scientifically robust and practically deployable.
11 years of coding experience
10 years of employment as a software developer
Bachelor of Science - BS, Bachelor of Science - BS at Case Western Reserve UniversityMaster of Philosophy - MPhil, Master of Philosophy - MPhil at University of CambridgeDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at Harvard University Graduate School of Arts and Sciences
Github Skills (117)
projection10
python10
seq10
topological-data-analysis10
throughput10
density-estimation10
transcriptomics10
deep-learning10
gpu10
genomics10
approximation10
microscopy10
dimensionality-reduction10
variational-autoencoder10
artifacts10
Programming languages (5)
Github contributions (5)
sjfleming/NanoporeCode
Dec 2014 - Apr 2018
Contributions:123 commits, 109 pushes in 3 years 4 months
nanoporemachine-learningnanopore-datalabanalyze
broadinstitute/CellBender
Feb 2019 - Dec 2022
CellBender is a software package for eliminating technical artifacts from high-throughput single-cell RNA sequencing (scRNA-seq) data.
Contributions:15 releases, 38 reviews, 72 commits in 3 years 10 months
single-cell-rna-sequencingrnaseqsequencinggenomicsscrna-seq
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