Pranav Kulkarni
6116 Executive Blvd, Suite 200
North Bethesda, MD 20852
pranavk [at] umd.edu
Hi! I am a first-year CS PhD student at the University of Maryland, College Park, advised by Heng Huang. I am also a Graduate Research Assistant at the University of Maryland Institute for Health Computing (UM-IHC), where I work on multi-modal foundation models for cardiovascular and lung diseases.
My research is primarily focused on the intersection of machine learning, computer vision, and medical imaging, with the goal of enabling opportunistic screening and early-stage disease detection in everyday clinical practice. I am currently interested in 1️⃣ Multi-modal foundation models that integrate imaging with clinical and multi-omics data for clinical decision-making; 2️⃣ Federated learning methods to develop cross-institutional models using distributed, heterogeneous data while preserving patient privacy; and 3️⃣ Trustworthy and explainable AI systems that adapt to distribution shifts over time, align with nuanced human feedback, and mitigate algorithmic bias.
In my free time, I enjoy hiking, gardening, and reading about history, biogeography, and urban planning!
Recent News
| Jul 2025 | One paper accepted at ICCV CVAMD’25 in Honolulu. |
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| May 2025 | A review article that I co-authored has been published in Radiology. |
| Apr 2025 | Received UM-IHC Travel Award to support my travel for MIDL’25. |
| Mar 2025 | Two papers accepted at MIDL’25 in Salt Lake City. |
| Mar 2025 | A book chapter that I co-authored for the MICCAI Society’s Book on Federated learning for Medical Imaging has been published. |
| Mar 2025 | One tiny paper accepted at ICLR’25 Workshop on Bidirectional Human-AI Alignment in Singapore. |
| Feb 2025 | One paper published in the American Journal of Roentgenology. |
| Nov 2024 | One paper accepted at ML4H’24 in Vancouver. |
| Oct 2024 | Three papers published in the Journal of Imaging Informatics in Medicine. |
| Jul 2024 | One paper accepted for oral presentation at IEEE QCE’24 in Montreal. |
Selected Publications
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Pitfalls and Best Practices in Evaluation of Algorithmic Biases in RadiologyRadiology, 315(2), e241674. May 2025 -
