Rate My Professor Shuo Li

Li's research focuses on multi-disciplinary artificial intelligence for clinical imaging-centered healthcare, leveraging machine learning, medical data analytics, and imaging techniques including deep, regression, reinforcement, adversarial, sparse, spectral, and manifold learning to tackle challenges in cardiology, radiology, urology, surgery, rehabilitation, and cancer. He has produced over 300 publications, comprising 240 journal articles, 162 conference papers, 16 patents, and six edited Springer books. Key works include "VCC-DSA: A novel vascular consistency constrained DSA imaging model for motion artifact suppression" (Medical Image Analysis, 2026), "Full Left Ventricle Quantification via Deep Multitask Relationships Learning" (Medical Image Analysis, 2018), "Contrast Agent-free Synthesis and Segmentation of Ischemic Heart Disease Images using Progressive Sequential Causal GANs" (Medical Image Analysis, 2020), and "K-Net: Integrate Left Ventricle Segmentation and Direct Quantification of Paired Echo Sequence" (IEEE Transactions on Medical Imaging, 2020). His contributions extend to leadership as a member of the MICCAI Society Board of Directors (2015-2024), General Chair of MICCAI 2022 in Singapore, and associate editor for prominent journals. Li's accolades include Fellowships from SPIE, AAIA, IET, AIMBE, and IAMBE (all 2025); Crain’s Cleveland Notable Leader in Architecture, Engineering & Construction (2026); Case School of Engineering Faculty Research Award (2025); MICCAI Young Scientist Award (2017); and GE Innovation Awards (2008, 2009).