The University of Maryland Institute for Health Computing (UM-IHC) offers students at all levels opportunities to work with seasoned researchers and tap into the institute’s expertise and technologies.
For this Student Spotlight, we asked Pranav Kulkarni about the research projects he’s working on as a graduate research assistant at the UM-IHC.
Kulkarni is a computer science Ph.D. student at the University of Maryland, advised by Heng Huang, the director of applied AI at the UM-IHC and Brendan Iribe Endowed Professor of Computer Science.
This interview has been edited for length and clarity.
What have you been working on at the UM-IHC?
I bridge machine learning and computer vision with medical imaging to transform everyday clinical practice and improve patient outcomes, with a focus on cardiovascular diseases. Currently, I’m working on three main areas.
I’m building multimodal foundation models that use large-scale imaging and clinical data to accelerate clinical decision-making and enable early-stage disease detection.
I’m also developing generative artificial intelligence (AI) tools to create “virtual contrast” in chest computerized tomography scans, allowing clinicians to diagnose life-threatening vascular diseases without needing to inject patients with contrast agents.
Finally, I’m co-organizing the AmplifAI Challenge with the FDA at MICCAI 2026, the International Conference on Medical Image Computing and Computer Assisted Intervention, to facilitate the development of interpretable AI tools for characterizing liver lesions. My work also includes evaluating the safety of radiology AI tools and discovering novel imaging biomarkers.
What UM-IHC tools and technologies have been helpful in your work?
The faculty and resources at the IHC have been critical to what I’m doing, and I’m incredibly grateful to my Ph.D. advisor Heng Huang and to Florence Doo [an assistant professor of diagnostic radiology and nuclear medicine at the University of Maryland School of Medicine and UM-IHC faculty member] for their constant guidance. By tapping Dr. Huang’s technical expertise and Dr. Doo’s clinical insight, I’ve been able to advance the field of machine learning and design my projects with an eye toward improving patient care. Additionally, the IHC’s computational resources have enabled my research, including support from the University of Maryland Medical System team in providing secure research environments to validate my models on real clinical data.
What questions do you hope to answer through your UM-IHC research?
My goal is to build AI models for diseases that affect the people of Maryland. Cardiovascular disease is the leading cause of death in the state. Globally, it accounts for a third of all deaths, with liver cancer the third leading cause of cancer-related mortality. Early detection is essential to improving patient outcomes, so I aim to translate my work from bench to bedside to support early diagnosis, revolutionize everyday clinical practice and save lives.
How has your experience at the UM-IHC prepared you for your next step?
The opportunities have been fundamental to my professional and scientific growth. The collaborative environment allows me to work directly with clinicians and lead projects with tangible, real-world clinical impact. Furthermore, resources like the IHC Travel Award have enabled me to present my research at top-tier international conferences, helping me establish myself and my work, and to represent the IHC on a global stage. Ultimately, these experiences are invaluable, having prepared me to pursue my long-term goal of joining academia as a faculty member.
