Mikaela Koutrouli
Postdoctoral Researcher at Genentech
San Francisco, California, United States
Summary
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Top SchoolMikaela Koutrouli is a postdoctoral bioinformatics researcher based in San Francisco specializing in methods development and data analysis for single-cell technologies, with four years of research experience and a PhD from the University of Copenhagen. As a core member and manager at scverse.org and currently a Postdoc in the Marioni lab at Genentech, she builds and maintains open-source tools in both Python and R that power single-cell and spatial transcriptomics workflows. Her work spans academia and industry, including short exchanges at Broad and Mount Sinai and roles in Copenhagen and ZS|Intomics, giving her a pragmatic focus on reproducible software and method deployment. She combines deep computational skills with hands-on biological insight, having navigated projects from cellular cartography in her PhD to production-oriented analyses at biotech. Notably, she contributes to community-driven software ecosystems, bridging researcher needs and sustainable tool development.
4 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Bioinformatics, Doctor of Philosophy - PhD, Bioinformatics at Københavns Universitet - University of CopenhagenMaster's degree, Molecular Biomedicine, Master's degree, Molecular Biomedicine at Ethnikon kai Kapodistriakon Panepistimion AthinonBachelor's degree, Biology/Biological Sciences, General, Bachelor's degree, Biology/Biological Sciences, General at University of Patras
Stackoverflow
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Github Skills (46)
scales10
python10
data-science10
variational-autoencoder10
machine-learning10
transcriptomics10
single-cell-analysis10
bioinformatics10
scanpy10
hierarchical9
autoencoder9
spatial-transcriptomics9
tsne9
density-estimation8
topological-data-analysis8
Programming languages (4)
Github contributions (5)
mikelkou/ucie
Sep 2021 - Oct 2024
Contributions:85 pushes, 1 comment, 1 issue in 3 years 2 months
mikelkou/fava
Mar 2022 - Jan 2023
Functional Associations using Variational Autoencoders
Contributions:5 releases, 66 commits, 22 PRs in 10 months
networksdeep-learningautoencodersco-expressionmachine-learning
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