Queensland University of Technology
Peter Corke is a robotics researcher and educator. He is the distinguished professor of robotic vision at Queensland University of Technology, director of the ARC Centre of Excellence for Robotic Vision and Chief Scientist at Dorabot.
His research is concerned with enabling robots to see, and the application of robots to mining, agriculture and environmental monitoring. He created widely used open-source software for teaching and research, wrote the best selling textbook “Robotics, Vision, and Control”, created several MOOCs and the Robot Academy, and has won national and international recognition for teaching including 2017 Australian University Teacher of the Year.
He is a fellow of the IEEE, the Australian Academy of Technology and Engineering, the Australian Academy of Science; founding editor of the Journal of Field Robotics; founding multi-media editor of the International Journal of Robotics Research; member of the editorial advisory board of the Springer Tracts on Advanced Robotics series; former editor-in-chief of the IEEE Robotics & Automation magazine and member of the executive editorial board member of the International Journal of Robotics Research; the recipient of the Qantas/Rolls-Royce and Australian Engineering Excellence awards; and has held visiting positions at Oxford, University of Illinois, Carnegie-Mellon University and University of Pennsylvania. He received his undergraduate and masters degrees in electrical engineering and PhD from the University of Melbourne.
Nanyang Technological University
Chen Change Loy is a Nanyang Associate Professor with the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He is also an Adjunct Associate Professor at the Chinese University of Hong Kong.
He received his PhD (2010) in Computer Science from the Queen Mary University of London. Before joining NTU, he served as a Research Assistant Professor at the MMLab of the Chinese University of Hong Kong, from 2013 to 2018. He is the recipient of the 2019 Nanyang Associate Professorship (Early Career Award) from Nanyang Technological University.
He is recognized by inclusion in the AI 2000 Most Influential Scholar Annual List (AI 2000). His research interests include computer vision and deep learning with a focus on image/video restoration, enhancement, and manipulation. His journal paper on image super-resolution was selected as the `Most Popular Article’ by TPAMI in 2016. It remains as one of the top 10 articles to date. He serves as an Associate Editor of IJCV and TPAMI. He also serves/served as the Area Chair of CVPR 2021, CVPR 2019, BMVC 2019, ECCV 2018, and BMVC 2018. He has co-organized several workshops and challenges at major computer vision conferences.
University of Toronto
Sven Dickinson received the B.A.Sc. degree in Systems Design Engineering from the University of Waterloo, in 1983, and the M.S. and Ph.D. degrees in Computer Science from the University of Maryland, in 1988 and 1991, respectively.
He is Professor and past Chair of the Department of Computer Science at the University of Toronto, and is also Vice President and Head of the new Samsung Toronto AI Research Center, which opened in May, 2018. Prior to that, he was a faculty member at Rutgers University where he held a joint appointment between the Department of Computer Science and the Rutgers Center for Cognitive Science (RuCCS).
His research research interests revolve around the problem of shape perception in computer vision and, more recently, human vision. He has received the National Science Foundation CAREER award, the Government of Ontario Premiere’s Research Excellence Award (PREA), and the Lifetime Research Achievement Award from the Canadian Image Processing and Pattern Recognition Society (CIPPRS).
He currently serves on eight editorial boards, including the role of Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence, and the role of co-editor of the Morgan & Claypool Synthesis Lectures on Computer Vision. He is a Fellow of the International Association for Pattern Recognition (IAPR).
Queen Mary University of London
Shaogang Gong is Professor of Visual Computation at Queen Mary University of London (since 2001), a pioneer in computer vision research for visual surveillance and person re-identification, and for video analytics technology deployment in law enforcement video forensic analysis.
He served on the Steering Panel of the UK Government Chief Scientific Adviser’s Science Review.
Gong was a research fellow on the EU ESPRIT VIEWS (Visual Interpretation and Evaluation of Wide-area Scenes) in 1989-1993, the world’s first multinational collaborative computer vision project on visual surveillance in urban environments. He led the EU Security Programme SAMURAI (Suspicious and Abnormal Behaviour Monitoring Using a Network of Cameras for Situation Awareness Enhancement) that pioneered Person Re-Identification (RE-ID) in-the-wild for public infrastructure protection in 2008-2011. Between 2009-2013, he led the UK government project on developing a system for Multi-Camera Object Tracking by RE-ID funded by the UK INSTINCT Programme (Innovative Science and Technology in Counter-Terrorism), in collaboration with the BAE Systems. He won the 2019 Bruce Dickinson Entrepreneur of the Year Award, the 2019 Queen Mary Innovation Award, and the 2017 Queen Mary Academic Commercial Enterprise Award. A commercial system built on the patents and software from Gong’s research won the 2017 Global Frost & Sullivan Award for Technical Innovation for Law Enforcement Video Forensics Technology, and won the 2017 Aerospace Defence Security Innovation Award given by the UK Security Minister for “revolutionary solution to reviewing CCTV footage”.
Gong has authored and edited 7 books on Person Re-Identification, Visual Analysis of Behaviour, Video Analytics for Business Intelligence, Dynamic Vision from Images to Face Recognition, Analysis and Modelling of Faces and Gestures. His recent research has been on Zero-Shot Learning, Transfer Learning, Distributed Learning, Unsupervised and Semi-Supervised Deep Learning, Imbalanced Deep Learning, Deep Reinforcement Learning, Attention Deep Learning, and Human-In-The-Loop Active Learning.
Gong is a Turing Fellow of the Alan Turing Institute of Data Science and Artificial Intelligence, and was a Royal Society Research Fellow. He received his DPhil degree from Keble College, Oxford University in 1989, sponsored by GEC Hirst and the Royal Society. He is a Fellow of IEE (now IET), a Fellow of the British Computer Society, and a Member of the UK Computing Research Committee.
Professor Dana Kulić – Monash University
Prof. Dana Kulić conducts research in robotics and human-robot interaction (HRI), and develops autonomous systems that can operate in concert with humans, using natural and intuitive interaction strategies while learning from user feedback to improve and individualize operation over long-term use.
Dana Kulić received the combined B. A. Sc. and M. Eng. degree in electro-mechanical engineering, and the Ph. D. degree in mechanical engineering from the University of British Columbia, Canada, in 1998 and 2005, respectively. From 2006 to 2009, Dr. Kulić was a JSPS Post-doctoral Fellow and a Project Assistant Professor at the Nakamura-Yamane Laboratory at the University of Tokyo, Japan. In 2009, Dr. Kulić established the Adaptive System Laboratory at the University of Waterloo, Canada, conducting research in human robot interaction, human motion analysis for rehabilitation and humanoid robotics. Since 2019, Dr. Kulić is a professor and director of Monash Robotics at Monash University, Australia. In 2020, Dr. Kulić was awarded the ARC Future Fellowship. Her research interests include robot learning, humanoid robots, human-robot interaction and mechatronics.