معرفی
Miaomiao Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering and the Department of Computer Science at the University of Virginia, where she conducts interdisciplinary research at the intersection of machine learning, statistics, and medical imaging. She leads the Medical Image Analysis (MIA) Lab, focusing on developing advanced computational models for image registration, segmentation, and statistical shape analysis.
- PhD, University of Utah, 2016
- Postdoctoral Associate, MIT CSAIL
Her research interests center on biomedical image analysis with a strong emphasis on machine learning and statistical shape modeling. She develops deep learning methods to analyze deformable anatomical structures in medical images, particularly for cardiovascular and neurological applications. Her work bridges theoretical advances in geometric deep learning with practical clinical impact in areas such as cardiac strain analysis and computer-assisted surgery.
The recent publications highlight a strong trend toward generative modeling—especially diffusion models—for medical image synthesis and transformation, while maintaining a core focus on diffeomorphic and shape-preserving operations. There is a clear trajectory toward modeling motion, deformation dynamics, and topology preservation in anatomical structures using neural operators and contrastive learning frameworks.
- NSF CAREER Award (2023)
- NIH Trailblazer R21 Award (2023)
- Best Paper Award, ISBI (2025)
- Best Thematic Paper Award, ML4H (2023)
- MICCAI Young Scientist Award (2014)
- MIT IMPACT Fellow (2016)
Professor Zhang has successfully mentored multiple PhD and master’s students, several of whom have gone on to postdoctoral positions at Harvard Medical School and Yale University. Her research is supported by prestigious grants including the NSF CAREER and NIH R21 awards, often in collaboration with clinical partners such as Dr. Fred Epstein and Dr. Ken Bilchick. She actively contributes to the broader scientific community through service as an area chair for MICCAI, ISBI, and MIDL, and as a reviewer for top journals like Medical Image Analysis and IEEE TMI.
She leads the MIA Lab, which develops open-source tools like FLASH and DeepFLASH for fast diffeomorphic image registration. The lab fosters collaboration across engineering and clinical domains, with ongoing projects in placental imaging, cardiac motion modeling, and brain tumor segmentation.
