PeopleofSuperintelligence

Researcher

Qingyu Zhao

Qingyu Zhao is a computer scientist whose AI work applies machine learning to neuroimaging and medical-image analysis. He earned a computer science PhD from the University of North Carolina at Chapel Hill in 2017 and coauthored methods for brain-age prediction, confounder-aware medical imaging and longitudinal MRI in 2019 and 2020. By 2022, he had worked as a postdoctoral researcher, research scientist and instructor in Stanford's Department of Psychiatry and Behavioral Sciences, and he was a recipient of NIAAA's K99/R00 Pathway to Independence Award. In October 2023, he joined Weill Cornell as Assistant Professor of Artificial Intelligence in Radiology. His work includes an NIAAA-funded diffusion imaging project on adolescent alcohol use from 2024 to 2026 and LILAC, a machine-learning method for longitudinal images; as of October 2026, he is an Assistant Professor of Artificial Intelligence in Radiology at Weill Cornell Medical College and principal investigator of Machine Intelligence in Neuroimaging.

Works
125
Citations
1,752
h-index
22

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. 2017

    A computer science PhD

    He earns a PhD in computer science from the University of North Carolina at Chapel Hill in 2017.

  2. 2019

    A VAE for brain aging

    In 2019, he coauthors a variational autoencoder for supervised regression that predicts age from structural MRIs and reveals brain-development patterns.

  3. 2020

    Confounder-free medical imaging

    In 2020, he coauthors a deep-learning method that derives medical-image features invariant to confounders while modeling their relationship with prediction outcomes.

  4. 2020

    Longitudinal self-supervised learning

    In 2020, he coauthors Longitudinal Self-Supervised Learning, a model that uses repeated MRI scans to identify changes over time and disentangle brain age from image representations.

  5. By 2022

    A K99/R00 award at Stanford

    By 2022, he is an instructor in Stanford's Department of Psychiatry and Behavioral Sciences after postdoctoral and research scientist roles there. He is a recipient of NIAAA's K99/R00 Pathway to Independence Award.

  6. October 2023

    Assistant Professor at Weill Cornell

    In October 2023, he joins Weill Cornell as Assistant Professor of Artificial Intelligence in Radiology. Before the move, he is a faculty member in Stanford's Department of Psychiatry and Behavioral Sciences.

  7. 2024–2026

    An NIAAA imaging study

    From 2024 to 2026, he serves as principal investigator on a National Institute on Alcohol Abuse and Alcoholism project using diffusion tensor imaging to study adolescent alcohol use.

  8. 2025

    LILAC for longitudinal images

    In 2025, he coauthors LILAC, a machine-learning method that compares longitudinal images to identify changes and predict outcomes such as clinical scores.

  9. Since 2023

    Assistant Professor at Weill Cornell

    As of October 2026, he is Assistant Professor of Artificial Intelligence in Radiology at Weill Cornell Medical College and principal investigator of Machine Intelligence in Neuroimaging. His research applies machine-learning analysis to neuroimaging and neuropsychological data to explain brain-behavior relationships.

Career

  1. Since 2023Cornell UniversityAssistant Professor of Artificial Intelligence in Radiology
  2. NowCornell UniversityPrincipal investigator of Machine Intelligence in Neuroimaging
  3. Stanford UniversityPostdoctoral researcher in Department of Psychiatry and Behavioral Sciences
  4. Stanford UniversityResearch scientist in Department of Psychiatry and Behavioral Sciences
  5. Stanford UniversityInstructor in Department of Psychiatry and Behavioral Sciences

Education

  1. 2017University of North Carolina at Chapel HillPhD in computer science

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. FAIMI | Fairness of AI in Medical Imaging Workshop faimi-workshop.github.io. Checked 27 October 2026.
  2. Variational AutoEncoder For Regression: Application to Brain Aging Analysis arxiv.org. Checked 27 October 2026.
  3. Training confounder-free deep learning models for medical applications nature.com. Checked 27 October 2026.
  4. Longitudinal Self-Supervised Learning arxiv.org. Checked 27 October 2026.
  5. Promotion and Appointment of WCM Faculty--October 2023 faculty.weill.cornell.edu. Checked 27 October 2026.
  6. Qingyu Zhao | Graduate School of Medical Sciences gradschool.weill.cornell.edu. Checked 27 October 2026.
  7. Zhao, Qingyu vivo.weill.cornell.edu. Checked 27 October 2026.
  8. Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain vivo.weill.cornell.edu. Checked 27 October 2026.
  9. Qingyu Zhao | Machine Intelligence in Neuroimaging mini-cornell.github.io. Checked 27 October 2026.