Maoguo Gong
Inner Mongolia Normal University, Xidian University

Maoguo Gong is the Director of Key Laboratory of Collaborative Intelligence Systems, Ministry of Education of China. He is the Vice President of Inner Mongolia Normal University and the Leading Professor of Xidian University.
His research interests are broadly in the area of computational intelligence, with applications to optimization, learning, data mining and image understanding. He has published over two hundred papers in journals and conferences, and holds over thirty granted patents as the first inventor. His papers have been cited over 23000 times in Google Scholar, possessing 76 H-index and 345 I10-index. He is Highly Cited Researcher (Clarivate), and China's Highly Cited Scholar (Elsevier). He is leading or has completed over thirty projects as the Principle Investigator, funded by the National Natural Science Foundation of China, the National Key Research and Development Program of China, and others. He is also the recipient of the National Program for Support of Top-notch Young Professionals, the Excellent Young Scientist Foundation, the National Program for Support of the Leading Innovative Talents, and the National Natural Science Award of China.
He is a Fellow of IEEE, the Director of Chinese Association for Artificial Intelligence-Youth Branch, Associate Editor or Editorial Board Member for over five journals including the IEEE Transactions on Neural Networks and Learning Systems, and the IEEE Transactions on Emerging Topics in Computational Intelligence. He has received the Outstanding Associate Editor of IEEE Transactions on Evolutionary Computation in 2020.

Title: Collaborative Intelligence Systems for AI application in mega scenarios
Abstract: The development of information technology has propelled technological reform in artificial intelligence (AI). To address the needs of diversified and complex applications in mega scenarios, AI has been increasingly trending towards intelligent, collaborative, and systematized development across different levels and tasks. To deploy AI models in diverse and complex mega application scenarios in the future, it is necessary to build collaborative intelligence systems by integrating multiple participants. This talk reviews research on systematic, intelligent, and collaborative AI technology from the perspective of individual collaboration, decision variables collaboration, multi-task and multi-party collaboration, and macro-level collaboration such as terrestrial-satellite collaboration, space-air-ground-sea collaboration, vehicle-road-cloud collaboration.

IEEE websites place cookies on your device to give you the best user experience. By using our websites, you agree to the placement of these cookies. To learn more, read our Privacy Policy