Jon Donnelly

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Hello! I’m a fourth-year PhD Student in Cynthia Rudin’s interpretable machine learning lab at Duke University. I research interpretable and controllable machine learning models that empower domain experts to understand, edit, and learn from complex models. In doing so, my work supports scientific discovery and keeps experts in the loop. Across these goals, I work to achieve real-world impact by spanning the research-to-practice gap, with an emphasis on applications in healthcare.

Outside of research, I enjoy running very long distances, drinking excessive amounts of coffee, playing card/board games, cooking, and watching survivor.

Selected Publications

  1. asymmirai.png
    AsymMirai: Interpretable Mammography-Based Deep Learning Model for 1–5-Year Breast Cancer Risk Prediction
    Jon Donnelly, Luke Moffett, Alina Jade Barnett, and 4 more authors
    Radiology, 2024
  2. proto-rset.png
    Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time
    Jon Donnelly, Zhicheng Guo, Alina Jade Barnett, and 3 more authors
    In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025
  3. deformable.png
    Deformable ProtoPNet: An Interpretable Image Classifier using Deformable Prototypes
    Jon Donnelly, Alina Jade Barnett, and Chaofan Chen
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022
  4. universe.png
    Doctor Rashomon and the UNIVERSE of Madness: Variable Importance with Unobserved Confounding and the Rashomon Effect
    Jon Donnelly*, Srikar Katta*, Emanuele Borgonovo, and 1 more author
    arXiv preprint arXiv:2510.12734, 2025