Pranav Kulkarni

Medical Imaging × AI Researcher • SWE @ UM-IHC • CS & Math @ UMD

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Pranav Kulkarni is a Bioinformatics Software Engineer I at the University of Maryland Institute for Health Computing (UM-IHC). He received his dual Bachelor’s degrees in Computer Science and Mathematics from the University of Maryland.

His research focuses on the overarching goal of bringing AI from bench to bedside, spanning federated continual learning frameworks, resource-efficient AI systems, and AI security, bias, and fairness in the clinical environment. His work has been recognized through spotlight talks at international conferences, such as Medical Imaging with Deep Learning (MIDL), and patent pending technology on cloud-based medical image streaming for high-throughput AI inference.

In the near future, Pranav aspires to attend graduate school to carve his path as a future computer scientist. When Pranav is not talking in third person, he enjoys gardening, photography, and hiking.

Selected Publications

  1. Spotlight Talk
    Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populations
    Pranav Kulkarni, Andrew Chan, Nithya Navarathna, Skylar Chan, Paul H. Yi, and Vishwa S. Parekh
    In Proceedings of the 2024 Conference on Medical Imaging with Deep Learning. Jul 2024
    HTML PDF Poster Slides arXiv
  2. ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging
    Pranav Kulkarni, Adway Kanhere, Eliot L. Siegel, Paul H. Yi, and Vishwa S. Parekh
    Journal of Imaging Informatics in Medicine, 1–14. Jun 2024
    HTML arXiv
  3. Privacy-Preserving Collaboration for Multi-Organ Segmentation via Federated Learning from Sites with Partial Labels
    Adway Kanhere, Pranav Kulkarni, Paul H. Yi, and Vishwa S. Parekh
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. Jun 2024
    HTML PDF Poster arXiv
  4. Economic and environmental costs of cloud for medical imaging and radiology artificial intelligence
    Florence X. Doo, Pranav Kulkarni, Eliot Siegel, Michael Toland, Paul H. Yi, Ruth C. Carlos, and Vishwa S. Parekh
    Journal of the American College of Radiology, 21(2), 248–256. Dec 2023
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