January 12, 2026, CUFE Business School successfully held the 3rd session of 2026 Excellent Academic Forum in Room 615 of the Main Teaching Building at the Xueyuan South Road Campus. The lecture invited Research Fellow Li Yexin at the Beijing Institute for General Artificial Intelligence as the keynote speaker. More than ten participants, including faculty members, doctoral students, master’s students from the Business School, and scholars from other universities, attended the academic event.
The lecture was hosted by Professor Dai Hongyan from CUFE Business School. At the beginning of the event, Professor Dai introduced the academic background of Research Fellow Li Yexin to the audience. Li Yexin received her Ph.D. in Department of Computer Science and Engineering, the Hong Kong University of Science and Technology (HKUST) in 2021. She currently serves as the Researcher at the Beijing Institute for General Artificial Intelligence, and also acts as a doctoral co-supervisor under the Tong Program co-launched by Shanghai Jiao Tong University and ShanghaiTech University. Her primary research areas include multi-agent systems, reinforcement learning, large language models, and data mining. Her research findings have been published in top international conferences and journals such as the International Conference on Machine Learning (ICML) and IEEE Transactions on Knowledge and Data Engineering (TKDE), and she has also been nominated for the ACM SIGSPATIAL Ten-Year Impact Award.
The theme of Li Yexin’s lecture was “Exploration and Reflections on Spatiotemporal Intelligence Technology in the Logistics Sector.” Centered on the key question of “how to operate logistics systems intelligently,” she systematically introduced cutting-edge applications of spatiotemporal intelligence technologies across key stages of logistics operations. These applications included address standardization, delivery time efficiency prediction and address correction, as well as route planning and scheduling optimization. Through in-depth integration of artificial intelligence, large language models, and spatiotemporal data mining technologies, these approaches help enhance the accuracy, real-time performance, and operational efficiency in logistics systems.
Drawing on several practical implementation cases, Li provided an in-depth analysis of the practical challenges encountered when transitioning technologies from laboratory research to industrial applications, including data quality issues, privacy protection, real-time computing pressure, and system robustness. She also shared insights from an engineering perspective and targeted solutions developed in response to these challenges. Finally, she looked ahead to the future development of intelligent logistics, highlighting several frontier directions such as end-to-end decision-making driven by multimodal large language models, the development of city-scale spatiotemporal foundation models, and collaborative logistics ecosystems integrating humans, machines, and physical systems.
Following the lecture, participating faculty members and students engaged in vigorous and in-depth discussions with Research Fellow Li on topics including implementation challenges of spatiotemporal intelligence technologies in logistics scenarios, the applicability of large language models in real-time decision-making, the role of privacy-preserving computation and federated learning in logistics data sharing, and the localized pathway for the intelligent transformation of China’s logistics industry. The discussions further boosted intellectual exchanges at the intersection of academia and industry.
As an important platform for CUFE Business School to fulfill its mission of “contributing new management knowledge”, the “Excellent Academic Forum” is committed to focusing on cutting-edge issues in the field of business administration and Chinese enterprise management practice. It gathers global wisdom and innovative perspectives to provide theoretical support and practical paths for promoting the sustainable development of Chinese society and economy.
