Prof. Hongli Dong

Northeast Petroleum University, China

Bio: HongliDong, female, Han Chinese, born in November 1977 in Keshan County, Heilongjiang Province. She is a member of the Communist Party of China, a professor, and a doctoral supervisor. She has repeatedly been recognized under major national talent programs. She is an expert receiving the Special Government Allowance from the State Council. She serves as the Director of the Heilongjiang Provincial Key Laboratory of "Networking and Intelligent Control," the Leader of the Heilongjiang Provincial Leading Talent Echelon in "Pattern Recognition and Intelligent Systems," and the Head of the Heilongjiang Provincial "Head Goose" Team for "High-Efficiency Oilfield Development and Intelligent Innovation Research." She is also Vice Chair of the China Petroleum and Chemical Automation Association and a Board Member of the Chinese Association of Automation.

She received her Bachelor's degree in Computer and Application from Heilongjiang Institute of Science and Technology in 2000, her Master's degree in Control Theory and Control Engineering from Northeast Petroleum University in 2003, and her Ph.D. in Control Science and Engineering from Harbin Institute of Technology in 2012. From 2012 to 2014, she was an Alexander von Humboldt Fellow in Germany. She has held various administrative positions, including Vice Dean of the School of Electrical and Information Engineering, Dean of the Institute of Artificial Intelligence Energy, Dean of the School of Computer and Information Technology, and Member of the Party Committee and Vice President of Northeast Petroleum University.

Her research focuses on networked control, intelligent control, and sensor network information processing. She has developed a comprehensive theoretical framework for the analysis and design of nonlinear stochastic systems in networked environments, providing critical theoretical and technical support for high-efficiency oilfield development and intelligent construction. She has authored three monographs (in Chinese and English), published over 100 SCI-indexed journal papers (including 10 hot papers, 30 highly cited papers, and 41 IEEE Transactions papers), and been granted 21 invention patents. She has led more than 10 national-level research projects, including those funded by the National Natural Science Foundation of China (NSFC) Excellent Young Scientist Fund, Joint Fund, and General Program. Her research achievements have earned her the First Prize (twice) and Second Prize of the Heilongjiang Provincial Natural Science Award, as well as the Second Prize of the China Overseas Contribution Award. She was listed on Stanford University's World's Top 2% Scientists Lifetime Achievement List in 2022 and has been named a Clarivate Highly Cited Researcher for six consecutive years. She has also received honors including the National May Day Labor Medal, the Youth Scientific and Technological Outstanding Contribution Award of the China Petroleum and Chemical Industry Federation, and the inaugural Young Female Scientist Award from the Chinese Association of Automation.

Prof.Chenguang Yang

The Hong Kong Polytechnic University, Hong Kong SAR, China  

Bio: Chenguang (Charlie) Yang is a Professor with the Department of Computing at The Hong Kong Polytechnic University, Previously, he held professorships at University of Liverpool, University of the West of England (UWE Bristol) as well as South China University of Technology. He holds fellowships with Institute of Electrical and Electronics Engineers (IEEE), Institute of Engineering and Technology (IET), Institution of Mechanical Engineers (IMechE), and Aisa-Pacific AI Association (AAIA). He is a member of European Academy of Sciences and Arts (EASA) and a member of National Academy of Artificial Intelligence (NAAI). Professor Yang was selected as a Featured Author on IEEE Xplore in 2023. As lead author, he received the IEEE Transactions on Robotics Best Paper Award in 2012 and IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award in 2022. He also was a lead contributor to the 1st Prize of Chinese Association of Automation (CAA) Natural Science Award in 2022. His current research focuses on embodied AI for robot learning, human robot interaction, and intelligent system design.

Speech Title:  Human-like robot control, skill learning, and human–robot collaboration

Abstract:  The presenter proposed a safe contact-control method with humanoid-like compliance adjustment, developed a generalization framework for skill learning in force–position coupled manipulation, investigated optimization mechanisms for personalized collaboration strategies, and developed human–robot collaboration techniques with dynamic adaptability. Addressing core bottlenecks in force-contact tasks—namely the lack of coupled representation of force and position information and the separation between skill learning and low-level control—he proposed a multi-modal, comprehensive skill primitive system covering motion, force control, stiffness, and manipulability, and established a unified skill representation for force–position coupled manipulation. The presenter’s work integrates adaptive control into skill learning algorithms, fully leveraging adaptive control’s ability to compensate for uncertainties and thereby enhancing skill generalization in previously unknown scenarios.

Prof. Tao Yang

Northeastern University, China

Bio: Tao Yang received the Ph.D. degree in electrical engineering from Washington State University, USA in 2012. Between August 2012 and August 2014, he was an ACCESS Postdoctoral Researcher with the ACCESS Linnaeus Centre, Royal Institute of Technology, Sweden. He then joined the Pacific Northwest National Laboratory as a Postdoc, and was promoted to Scientist/Engineer II in 2015. He was an Assistant Professor at the Department of Electrical Engineering, University of North Texas, USA, from 2016–2019. He is a Professor at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. His research interests include industrial artificial intelligence, integrated optimization and control, distributed control and optimization with applications to process industries, cyber physical systems, networked control systems, and multi-agent systems.

He is an Associate Editor for IEEE Transactions on Control of Network Systems, IEEE Transactions on Control Systems Technology, and IEEE Transactions on Neural Networks and Learning System. He received Ralph E. Powe Junior Faculty Enhancement Award and Best Student Paper award (as an Advisor) of several international conference.

Speech Title:  Advances in Distributed Nonconvex Optimization

Abstract:  Distributed optimization algorithms solve large-scale optimization problems by leveraging the cooperative coordination among multiple agents. Compared to traditional centralized optimization methods, distributed approaches are more flexible, convenient, and efficient. However, most existing distributed optimization algorithms do not consider the nonconvex nature of the agents’ objective functions; moreover, they assume continuous communication among agents and overlook the limited capacity of practical communication channels. To address the nonconvex objective function issue, a distributed first-order primal-dual nonconvex optimization algorithm is proposed. To overcome the challenge of continuous communication between agents, an event-triggered distributed nonconvex optimization algorithm is introduced. Furthermore, to tackle the limited capacity of communication channels, a quantized communication-based distributed nonconvex optimization algorithm is developed. The convergence of all the proposed algorithms is theoretically proven, and their effectiveness is verified through simulation examples.