Kyle Harrington 🤖🧪
Kyle Harrington

Team Lead for Machine Learning

Chan Zuckerberg Institute for Advanced Biological Imaging

About Me

Kyle Harrington is a Team Lead for Machine Learning at the Chan Zuckerberg Institute for Advanced Biological Imaging in Redwood City, CA. His research interests include machine learning, deep learning, and innovative research in biological imaging. He applies both qualitative and quantitative methods to explore the role of science and technology in living systems.

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Interests
  • Artificial Intelligence
  • Machine Learning
  • Computational Biology
  • Artificial Life
Education

  • Postdoctoral Fellow, Pathology

    Harvard Medical School

  • PhD Computer Science

    Brandeis University

  • MA Computer Science

    Brandeis University

  • BA Artificial Life

    Hampshire College

📚 My Research

I’m Kyle Harrington, a Team Lead for Machine Learning at the Chan Zuckerberg Institute for Advanced Biological Imaging. I work on machine learning, deep learning, and innovative research in biological imaging. I apply qualitative and quantitative methods to explore the role of science and technology in living systems.

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Featured Publications
Annotating CryoET Volumes: A Machine Learning Challenge
Surforama: interactive exploration of volumetric data by leveraging 3D surfaces
SNT: a unifying toolbox for quantification of neuronal anatomy
Multi-sample SPIM image acquisition, processing and analysis of vascular growth in zebrafish
Recent Publications
Ariana Peck, Yue Yu, Jonathan Schwartz, Anchi Cheng, Utz Heinrich Ermel, Saugat Kandel, Dari Kimanius, Elizabeth Montabana, Daniel Serwas, Hannah Siems, Feng Wang, Zhuowen Zhao, Shawn Zheng, Matthias Haury, David Agard, Clinton Potter, Bridget Carragher, Kyle Harrington, * Co-Corresponding, Mohammadreza Paraan, * Co-Corresponding (2024). Annotating CryoET Volumes: A Machine Learning Challenge. bioRxiv.
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Kyle I. Harrington, Zhuowen Zhao, Jonathan Schwartz, Saugat Kandel, Utz Ermel, Mohammadreza Paraan, Clinton Potter, Bridget Carragher (2024). Open-source Tools for CryoET Particle Picking Machine Learning Competitions. bioRxiv.
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Kevin A. Yamauchi, Lorenz Lamm, Lorenzo Gaifas, Ricardo D. Righetto, Daniil Litvinov, Benjamin D. Engel, Kyle Harrington (2024). Surforama: interactive exploration of volumetric data by leveraging 3D surfaces. bioRxiv.
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Wolfgang Giese, Jan Philipp Albrecht, Olya Oppenheim, Emir Bora Akmeriç, Julia Kraxner, Deborah Schmidt, Kyle Harrington, Holger Gerhardt (2024). Polarity-JaM: An image analysis toolbox for cell polarity, junction and morphology quantification. bioRxiv.
PDF Code Source Document DOI
Gabriel J. Selzer, Curtis T. Rueden, Mark C. Hiner, Edward L. Evans, Kyle I. S. Harrington, Kevin W. Eliceiri (2023). napari-imagej: ImageJ Ecosystem Access from napari. Nature Methods, 20(10), 1443-1444.
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