PeopleofSuperintelligence

Researcher

Kun-Hsing Yu

Also known as Kun

Kun-Hsing Yu is a biomedical informatics researcher whose AI work uses pathology images to classify cancer and predict patient outcomes. He completed a PhD in Biomedical Informatics at Stanford in 2016, where his early machine-learning work analyzed lung-cancer images. He won the Junior Parasite Award in 2017 for reanalyzing cancer pathology data, held a Harvard Data Science Initiative postdoctoral fellowship from 2017 to 2019, and a January 2020 notice identified him as a recently appointed assistant professor in Harvard Medical School's Department of Biomedical Informatics. In 2024, he coauthored CHIEF, a pathology foundation model for cancer diagnosis and prognosis, and received AMIA's New Investigator Award; in December 2025, he coauthored FAIR-Path to reduce bias in pathology AI. He was listed as the speaker for Stanford's March 17, 2026 seminar on trustworthy pathology AI; as of October 2026, he is Associate Professor of Biomedical Informatics at Harvard Medical School, Assistant Professor of Pathology at Brigham and Women's Hospital, and Instructor in Epidemiology at Harvard Chan School of Public Health.

Works
153
Citations
9,527
h-index
37

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. 2016

    A biomedical informatics PhD

    In 2016, he completes a PhD in Biomedical Informatics at Stanford, with a minor in Computer Science. His education also includes an MD from National Taiwan University.

  2. 2016

    Machine learning for lung cancer

    In 2016, he leads Stanford work that uses machine learning on lung-cancer images to distinguish tumor subtypes and predict patient survival.

  3. 2017

    A Junior Parasite Award

    In 2017, he wins the Pacific Symposium on Biocomputing's Junior Parasite Award for work that reanalyzes TCGA histopathology images and Stanford Tissue Microarray data with machine-learning methods.

  4. 2017–2019

    Harvard data science fellowship

    From 2017 to 2019, he is a postdoctoral fellow with the Harvard Data Science Initiative.

  5. 2020

    Assistant Professor at Harvard

    A January 22, 2020 welcome notice from Harvard Medical School's Department of Biomedical Informatics identifies him as a recently appointed assistant professor. His research background combines pathology image classification with molecular multi-omic characterization of tumors.

  6. 2024

    The CHIEF pathology model

    In 2024, he coauthors CHIEF, a weakly supervised machine-learning foundation model for pathology evaluation. The model uses 60,530 whole-slide images across 19 anatomical sites and is validated on 19,491 images from 32 independent slide sets.

  7. 2024

    AMIA New Investigator Award

    He receives the American Medical Informatics Association's New Investigator Award in 2024.

  8. December 2025

    FAIR-Path for pathology AI

    In December 2025, he coauthors FAIR-Path, which applies contrastive learning and weakly supervised machine learning to reduce bias in AI-based pathology evaluation. The framework mitigates 88.5% of performance disparities identified in a pan-cancer analysis across race, gender, and age groups.

  9. March 2026

    Trustworthy pathology AI

    He is listed as the speaker for Stanford's March 17, 2026 seminar, "Trustworthy AI for Pathology: Quantifying Uncertainty and Ensuring Fairness," on uncertainty-aware pathology foundation models and methods to reduce performance gaps across populations and institutions.

  10. As of October 2026

    Associate Professor at Harvard

    He is an Associate Professor of Biomedical Informatics at Harvard Medical School, an Assistant Professor of Pathology at Brigham and Women's Hospital, and an Instructor in Epidemiology at Harvard T.H. Chan School of Public Health.

Career

  1. NowHarvard UniversityAssociate Professor of Biomedical Informatics
  2. NowMass General BrighamAssistant Professor of Pathology
  3. NowHarvard UniversityInstructor in Epidemiology
  4. 2017–2019Harvard UniversityPostdoctoral Fellow, Harvard Data Science Initiative
  5. Harvard UniversityAssistant Professor

Education

  1. 2016Stanford UniversityPhD in Biomedical Informatics
  2. National Taiwan UniversityMD

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

Colleagues

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

Sources and credits

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  1. Kun-Hsing Yu | Harvard T.H. Chan School of Public Health hsph.harvard.edu. Checked 27 October 2026.
  2. Researchers' computer program assesses lung cancer better than pathologists stanforddaily.com. Checked 27 October 2026.
  3. Computers trounce pathologists in predicting lung cancer type, severity med.stanford.edu. Checked 27 October 2026.
  4. The Research Parasite Awards researchparasite.com. Checked 27 October 2026.
  5. Kun-Hsing Yu – HDSI datascience.harvard.edu. Checked 27 October 2026.
  6. Welcome, Kun-Hsing Yu! | Department of Biomedical Informatics dbmi.hms.harvard.edu. Checked 27 October 2026.
  7. A pathology foundation model for cancer diagnosis and prognosis prediction | Nature nature.com. Checked 27 October 2026.
  8. Contrastive learning enhances fairness in pathology artificial intelligence systems - PMC pmc.ncbi.nlm.nih.gov. Checked 27 October 2026.
  9. Medical Physics Seminar | Department of Radiation Oncology | Stanford Medicine med.stanford.edu. Checked 27 October 2026.