Dr. Xiaogang Wang is a distinguished scholar and leader in the field of computer vision and artificial intelligence. He currently serves as a Full Professor in the Department of Electronic Engineering at The Chinese University of Hong Kong, having progressed through the academic ranks from Assistant Professor in 2009 to Associate Professor in 2015 and Full Professor in 2020. Dr. Wang earned his Bachelor's degree from the Special Class of Gifted Young at the University of Science and Technology of China, followed by an MPhil in Information Engineering from The Chinese University of Hong Kong and a PhD in Computer Science from the Massachusetts Institute of Technology. His early career achievements were recognized through prominent leadership roles including Area Chair for the IEEE International Conference on Computer Vision in 2011 and subsequent major computer vision conferences.
Dr. Wang's research has made seminal contributions to computer vision, pattern recognition, and deep learning, with his work accumulating over 141,815 citations according to Google Scholar. His group pioneered the first transfer learning framework that automatically transfers a generic pedestrian detector to a scene-specific detector without manual labeling, improving detection accuracy by more than 40% on benchmark datasets. In 2015, his CUvideo team achieved remarkable success by winning the object detection in videos challenge at ILSVRC 2015 with a mean Average Precision of 67.8%, significantly outperforming the second-place team's 35.9%. His research bridges theoretical foundations with practical applications in visual surveillance, face recognition, image and video search, and medical imaging, demonstrating exceptional translational impact across multiple domains.
Beyond his research achievements, Dr. Wang has significantly shaped the computer vision community through his leadership roles and educational contributions, including teaching a pioneering deep learning course at CUHK that has trained numerous students in cutting-edge methodologies. He actively mentors postdoctoral researchers and research assistants focused on advancing deep learning and computer vision, while also recruiting software engineers to translate research into practical implementations. His service as Area Chair for multiple prestigious conferences including ICCV, ECCV, and ACCV has helped guide the research agenda of the computer vision field for over a decade. Dr. Wang continues to drive innovation in visual understanding systems, with his current research exploring the next frontiers of deep learning applications that promise to further transform how machines interpret and interact with visual information.
