Shaogang (Sean) Gong BSc DPhil FIEE FBCS
Gong is Professor of Visual Computation at Queen Mary University of London, elected a Fellow of the Institution of Electrical Engineers, a Fellow of the British Computer Society, a member of the UK Computing Research Committee, and served on the Steering Panel of the UK Government Chief Scientific Advisor's Science Review. His early interest was in information theory & measurement and received a B.Sc. from the University of Electronic Sciences and Technology of China in 1985. Gong's B.Sc. thesis project was on biomedical image analysis which gave him the opportunity to develop a wider interest in robotics. This led Gong to pursue a doctorate in computer vision under the supervision of Mike Brady at Keble College Oxford and the Oxford Robotics Group in 1986. Brady introduced Gong to differential geometry in computer vision and the work of Ellen Hildreth at the MIT AI Lab on computing optic flow. During that time, Gong met David Murray who was on sabbatical at Oxford from GEC Hirst. Murray introduced Gong to the extensive work by Murray and Bernard Buxton at the GEC Hirst Centre on structure-from-motion for autonomous guided vehicle navigation. Gong received his D.Phil. from Oxford in 1989 with a thesis on computing optic flow by second-order geometrical analysis of Hessian derivatives with wave-diffusion propagation. Gong is a recipient of a Queen's Research Scientist Award in 1987, a Royal Society Research Fellow in 1987 and 1988, and a GEC sponsored Oxford research fellow in 1989. Gong worked as a research fellow with Hilary Buxton on the EU ESPRIT-II project VIEWS (Visual Interpretation and Evaluation of Wide-area Scenes) in 1989-93. A number of discussions Gong had in 1990 with Chris Brown at Rochester and Ray Rimey at Lockheed Martin and a visit to Rochester in 1991 initiated Gong's interest in variational graph models, and in particular the work of Judea Pearl on probabilistic reasoning in intelligent systems: networks of plausible inference. This led to Gong's works on visual analysis of behaviour from visual observation of object actions and activities by exploring probabilistic graphical models including Bayesian graphs, dynamic Bayesian networks, hidden Markov models, Markov networks, probabilistic latent semantic analysis, topic models, and Markov topic model. Since 1993 Gong has also been working on multi-view face recognition, body pose and gesture analysis, distributed multi-camera people tracking and in particular person re-identification, video semantic content analysis and video search. This led to Gong's works on domain transfer learning, distance metric learning, zero-shot learning (also considering person re-identification as a special case of zero-shot learning), and deep learning. Gong has published extensively research papers in computer vision and machine learning (Google Scholar and DBLP), two research monographs on Visual Analysis of Behaviour: From Pixels to Semantics (with Tao Xiang) and Dynamic Vision: From Images to Face Recognition (with Stephen McKenna and Alexandra Psarrou), and five edited books with topics ranging from Person Re-Identification (with Marco Cristani, Shuicheng Yan and Chen Change Loy) to Video Analytics for Business Intelligence and Modelling and Analysis of Faces and Gestures. Gong joined Queen Mary University of London as a Lecturer in Computer Science in 1993 and was appointed Professor of Visual Computation in 2001. Gong founded Queen Mary Computer Vision Laboratory in 1993 and has supervised over 70 research students and research assistants, one of whom Eddy Zhu won the 2016 Sullivan Prize - the UK Best PhD Thesis in Computer Vision awarded by the British Machine Vision Association. Gong won the 2017 Queen Mary Academic Commercial Enterprise Award. A commercial system built on the patents and software from his research won the 2017 Global Frost & Sullivan Award for Technical Innovation for Law Enforcement Video Forensics Technology, and the 2017 annual (12th) ADS (Aerospace Defence Security) Innovation Award for "revolutionary solution to reviewing CCTV footage".