Rate My Professor James Wang

James Z. Wang is a Distinguished Professor of Information Sciences and Technology in the Department of Informatics and Intelligent Systems at the Pennsylvania State University College of Information Sciences and Technology, where he has served on the faculty since 2000. He earned his Ph.D. in Medical Information Sciences from Stanford University in 2000, M.S. degrees in Computer Science and Mathematics from Stanford University in 1997, and a B.S. in Mathematics and Computer Science, summa cum laude, from the University of Minnesota Twin Cities in 1994. Wang's research expertise includes biomedical informatics, affective computing, robotics and computer vision, data science, and visual art. He directs the James Z. Wang Research Group, which develops technologies for modeling objects, concepts, aesthetics, and emotions in visual data. His affiliations extend to the Huck Institutes of the Life Sciences, the Institute of Energy and the Environment, and the Center for Socially Responsible Artificial Intelligence.

Wang has garnered significant recognition for his contributions, including the NSF CAREER Award, four consecutive Amazon Research Awards from 2019 to 2022, and elevation to Distinguished Professor status in 2022. He is the author or co-author of two monographs and more than eighty journal articles, with his publications highly influential in the field—such as 'Image retrieval: Ideas, influences, and trends of the new age' (ACM Computing Surveys, 2008), 'SIMPLIcity: Semantics-sensitive integrated matching for picture libraries' (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001), 'Studying aesthetics in photographic images using a computational approach' (European Conference on Computer Vision, 2006), 'Automatic linguistic indexing of pictures by a statistical modeling approach' (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003), and 'Real-time computerized annotation of pictures' (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008). Recent works include 'Enhancing AI-Assisted Stroke Emergency Triage with Adaptive Uncertainty Estimation' (MICCAI, 2025) and 'Refining pseudo-labels through iterative mix-up for weakly supervised semantic segmentation' (Pattern Recognition, 2026). His innovations have advanced content-based image retrieval, automatic annotation systems like ALIPR, and AI applications in healthcare, including stroke diagnosis tools and emotion recognition from body movements. Wang has presented lectures such as the McMurtry Award Lecture and holds patents in affective computing.