Guangming Zhang, PhD joined McWilliams School of Biomedical Informatics at UTHealth Houston, formerly UTHealth Houston School of Biomedical Informatics (SBMI) in July 2018 as an Assistant Professor. Before arriving at McWilliams School of Biomedical Informatics, Zhang had served as a research fellow at Wake Forest University School of Medicine (WFUBMC) since 2013.
Dr. Zhang’s primary research interests and areas of expertise are medical imaging informatics, biomechanics analysis, and machine learning for computational surgery. 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. In recent years, by 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.
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