Guangming Zhang, Ph.D. joined the UTHealth School of Biomedical Informatics (SBMI) in July 2018 as an Assistant professor. Prior to this position, Dr. Zhang was a Faculty Associate at UTHealth SBMI (2017-2018). Zhang received his B.S. degree from Soochow University, Suzhou, in 2003, his M.S. degree from Fudan University, Shanghai, China, in 2006, and his Ph.D. degree from Soochow University, Suzhou, China, in 2012. All of his degrees are in Computer Science.
Before arriving at SBMI, Zhang had served as a research fellow at Wake Forest University School of Medicine (WFUBMC) since 2013. Dr. Zhang has published many peer-reviewed research articles and has some granted patents. He also worked on several funded research projects as an investigator.
Dr. Zhang’s primary research interests and areas of expertise are medical imaging informatics, biomechanics analysis, and machine learning for computational surgery. While working at Professor Xiaobo Zhou's lab, he develops mathematical and computational models for medical image and biomechanical studies, by integrating of biomechanical and machine learning approach to address critical and challenging clinical and surgical questions. During the last years, applying his extensive knowledge and expertise in biomechanical analysis and finite element method, Dr. Zhang developed a novel biomechanical property-based machine learning model (called eSuture system) to predicting patient specific spring force for sagittal Craniosynostosis (CSO) surgery and thus optimizing the surgical design.
His current research projects include: 1) designing a systematic approach (called eOA system) to predicting the risk of unicompartmental knee arthroplasty (UKA) revision for osteoarthritis to ultimately improve the UKA outcomes, 2) developing an open-source imaging informatics platform (called eValve system) for clinicians to predict the risk of atrioventricular block and aortic regurgitation after transcatheter aortic valve replacement (TAVR), and 3) by simulating the bone and soft tissue behaviors, establishing a bio-physiological model (called eFace system) capable of accurate prediction of the soft tissue deformations following virtual osteotomy in Craniomaxillofacial (CMF) surgery.