| Prof. Weinan GaoNortheastern University, China （IEEE Senior Member）Research Area: Aritifical Intellignece, Automatic Control, Power Systems, Intelligent Transportation Systems Introduction: Weinan Gao received the Ph.D. degree in Electrical Engineering from New York University, Brooklyn, NY, USA. He is a Professor with the State Key Laboratory of Synthetical Automation for Process Industries at Northeastern University, Shenyang, China. Previously, he was an Assistant Professor of Mechanical and Civil Engineering at Florida Institute of Technology, Melbourne, FL, USA, an Assistant Professor of Electrical and Computer Engineering at Georgia Southern University, Statesboro, GA, USA, and a Visiting Professor of Mitsubishi Electric Research Laboratory (MERL), Cambridge, MA, USA. His research interests include reinforcement learning, adaptive dynamic programming (ADP), optimal control, cooperative adaptive cruise control (CACC), intelligent transportation systems, sampled-data control systems, and output regulation theory. Prof. Gao is the recipient of the best paper award in IEEE Data Driven Control and Learning Systems (DDCLS) Conference in 2023, IEEE International Conference on Real-time Computing and Robotics (RCAR) in 2018 and the David Goodman Research Award at New York University in 2019. He is an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, IEEE/CAA Journal of Automatica Sinica, Control Engineering Practice, Neurocomputing and IEEE Transactions on Circuits and Systems II: Express Briefs, a member of Editorial Board of Neural Computing and Applications, and a Technical Committee member in IEEE Control Systems Society on Nonlinear Systems and Control, IFAC TC 1.2 Adaptive and Learning Systems, and CAAI Industrial Artificial Intelligence. Speech Title: Learning-based Intelligent Control and Management of Connected and Autonomous Vehicles Abstract: The connected and autonomous vehicle (CAV) technology can prevent crashes, reducing property damage and injury, congestion and emissions. Among all CAV studies, the controller design and management of CAV has attracted large attention among researchers in the field of control, optimization and communication. In this talk, I will introduce several intelligent cruise control design strategies under the framework of reinforcement learning. Professional traffic simulation results show that these strategies can increase the traffic throughput and reduce the fuel usage. |
Assoc. Prof. Tang JinhuanShenyang Aerospace University, ChinaResearch Area: operation of new energy vehicles and optimization of low-carbon supply chains. Introduction：Dr. Tang Jinhuan, born in March 1985, is an associate professor, vice dean, and master's supervisor at the School of Economics and Management, Shenyang Aerospace University. She graduated from Northeastern University with a Ph.D. in Management Science and Engineering. She is selected as a candidate of the "Hundred, Thousand, and Ten Thousand Talents Project" in Liaoning Province. Her research focuses on the operation of new energy vehicles and optimization of low-carbon supply chains. Dr. Tang has led nearly 20 projects at the provincial and ministerial levels and has been involved in 2 national natural science foundation projects and 1 national social science foundation project. She has published nearly 30 academic papers, including 14 in CSSCI journals like "Chinese Journal of Management" and "Journal of Management Science and Engineering", and over 10 in SCI/SSCI journals such as "Energy" and "Journal of Systems Science and Systems Engineering". Additionally, she has authored 2 academic monographs, is a member of the China Society of Logistics, and serves as a reviewer for journals like Energy, Energy Policy, and Industrial Engineering and Management. Dr. Tang has been awarded the second prize for scientific achievements in Natural Science in Shenyang City. Speech Title：How to promote the value co-creation of stakeholders in the intelligent connected vehicle innovation ecosystem? Abstract: As information and communication technology continues to advance, the concept of the "software defined vehicle" is emerging as a prevailing trend. Intelligent connected vehicles (ICVs) are steering the future of the automotive industry. However, the lack of collaborative innovation, the fragmentation of the industrial chain, and the absence of sustained and consistent partnerships present significant challenges to value co-creation in the ICV sector. To address these challenges, this study develops a value co-creation evolutionary game model for the ICV industry's innovation ecosystem, considering automotive enterprises, intelligent automotive solution providers, and the government as the key players. By applying prospect theory, the model optimizes the evolutionary game among these stakeholders, aiming to enhance value co-creation by analyzing the co-evolution mechanisms of their strategies. The findings reveal that: (1) The ideal evolutionary stable strategy involves positive cross-border cooperation between automotive enterprises and intelligent automotive solution providers, with the government gradually phasing out subsidies; (2) Government subsidies should be appropriately managed, as excessive subsidies can diminish the marginal incentive effect; (3) Higher levels of trust and resource complementarity between automotive enterprises and intelligent automotive solution providers can boost cross-border integration willingness, while penalties and opportunity losses can effectively deter negative integration behaviors; (4) Higher risk attitude and risk aversion coefficients among automotive enterprises and intelligent automotive solution providers contribute positively to the value co-creation process. |
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| Assoc. Prof. Zuo feiShenyang Aerospace University, ChinaResearch Area: Project risk management. Introduction：Dr. Fei Zuo is an Associate Professor at Shenyang Aerospace University and a Postdoctoral Researcher at the Polytechnic University of Milan. He holds a Ph.D. in Management Science and Engineering from Northeastern University, China, where he also earned a master's degree in the same field. His research expertise includes project risk management, risk-related resource allocation, and the application of deep learning and optimization methods in artificial intelligence. Dr. Zuo has led several significant research projects, such as a study on secondary risk prevention resources funded by the National Natural Science Foundation of China and a project on dynamic governance methods for economic construction risks under AI contexts funded by the Liaoning Province Social Sciences Association. His work has been published in top-tier journals like the International Journal of Project Management and Reliability Engineering & System Safety, and he has presented at prominent international conferences including the International Conference on System Reliability and Safety and the European Safety and Reliability Conference. Speech Title：Managing secondary risks with optimal risk response strategy and risk-related resource scheduling Abstract：Managing secondary risks, which emerge from the implementation of risk response plans, is critical to achieving project objectives. Despite their significance, research on effectively scheduling resources to address these risks remains limited. This presentation introduces a pioneering mixed-integer optimization model alongside a tailored meta-heuristic solution algorithm, specifically designed to determine optimal strategies for both primary and secondary risk responses. To validate our model, we developed a synthetic data generation framework capable of producing relevant scenarios based on specific metrics. By applying the model and algorithm to a comprehensive numerical experiment constructed using the synthetic data generation framework, we derive key managerial insights into secondary risks and their potential impact on project objectives. The findings of this study provide project managers with valuable strategies for more effectively managing both primary and secondary risks, ultimately enhancing project success. |
