معرفی
Jing Tang is an Associate Professor at the University of Cincinnati. His research focuses on medical image formation and understanding, particularly in emission tomography imaging and machine learning-driven reconstruction techniques. He holds a PhD in Electrical Engineering from the University of Illinois at Urbana-Champaign.
- Research Interests: Medical imaging technologies including PET and SPECT, radiation dose reduction in pediatric imaging, outcome prediction in Parkinson’s disease, and integration of machine learning with image analysis.
- Key Projects: Developed artificial neural network-based methods for dose reduction in PET imaging and predictive models for motor outcomes in neurodegenerative diseases.
He has secured multiple grants totaling over $4 million from NIH and NSF, including a current NIH grant exploring dynamic PET for coronary artery disease diagnostics. His work emphasizes translational applications in clinical imaging and patient care.
- Notable Grants: NIH R01HL170245 (2024-2029), NIH R03EB028070 (2019-2023), NSF CAREER award (2015-2021).
Tang’s publications span advanced reconstruction algorithms, multimodal imaging integration (MRI-PET), and AI-driven diagnostics. His research bridges engineering and clinical medicine, addressing critical challenges in diagnostic accuracy and patient safety.
