PeopleofSuperintelligence

Researcher

David R. Haynor

Also known as David Haynor

David R. Haynor is a University of Washington neuroradiologist and Professor of Radiology whose research connects medical image processing with machine learning. He earned a PhD in mathematics from the University of California, Berkeley, in 1971 and an MD from Harvard Medical School in 1979, then coauthored studies on active-contour cardiac boundary detection in 1996 and gene-expression clustering in 2001. His later work included connectivity-based cortical parcellation from diffusion MRI in 2015, a National Science Foundation grant in August 2017, convolutional neural networks for head CT bleed detection in 2019, and graph neural networks for cortical segmentation in 2021. As of October 2026, he practices neuroradiology at UW Medical Center Montlake and UWMC Northwest Hospital, focusing on image processing and computer applications in radiology.

Works
279
Citations
16,359
h-index
55
Badges

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 1971–1979

    Mathematics and medical degrees

    He earns a PhD in mathematics from the University of California, Berkeley, in 1971 and an MD from Harvard Medical School in 1979.

  2. 1996

    Cardiac image contours

    In 1996, he coauthors a paper in IEEE Transactions on Medical Imaging with Vikram Chalana, David T. Linker, and Yongmin Kim on a multiple active-contour model for detecting cardiac boundaries in echocardiographic sequences.

  3. 2001

    Gene-expression clustering

    With Ka Yee Yeung and Walter L. Ruzzo, he coauthors “Validating Clustering for Gene Expression Data,” published in Bioinformatics in 2001.

  4. 2015

    Diffusion-MRI cortical metrics

    His 2015 paper in the IEEE Journal of Biomedical and Health Informatics, coauthored with Rosalia F. Tungaraza, Sonya H. Mehta, and Thomas J. Grabowski, introduces anatomically informed metrics for connectivity-based cortical parcellation from diffusion MRI.

  5. August 2017

    National Science Foundation grant

    In August 2017, he receives a National Science Foundation grant.

  6. 2019

    Deep learning for head CT

    Working with P. Ojeda, M. Zawaideh, and Mahmud Mossa-Basha in 2019, he coauthors a study evaluating a convolutional neural network for detecting intracranial bleeds on non-contrast head computed tomography studies.

  7. 2021

    Graph neural networks for cortex

    With Kristian Eschenburg and Thomas Grabowski, he develops a graph-neural-network method for cortical-surface segmentation from resting-state connectivity. In 2021, the method reaches a mean classification accuracy of 79.91% relative to a previously published parcellation.

  8. As of October 2026

    Professor of Radiology

    As of October 2026, he is a Professor of Radiology and neuroradiologist at the University of Washington, practicing at UW Medical Center Montlake and UWMC Northwest Hospital. He has a longstanding interest in image processing and computer applications in radiology.

Career

  1. Since 1984University of WashingtonProfessor of Radiology and neuroradiologist

Education

  1. 1979Harvard UniversityMD
  2. 1971University of California, BerkeleyPhD

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

  1. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes University of Washington. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  2. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of Washington. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  3. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes University of Washington. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.
  4. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes University of Washington. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. David Haynor – UW Bioengineering bioe.uw.edu. Checked 22 October 2026.
  2. David R. Haynor csauthors.net. Checked 22 October 2026.
  3. Validating Clustering for Gene Expression Data faculty.washington.edu. Checked 22 October 2026.
  4. Haynor | Department of Radiology rad.uw.edu. Checked 22 October 2026.
  5. Learning Cortical Parcellations Using Graph Neural Networks frontiersin.org. Checked 22 October 2026.