I am Yi Yu (Eve), a Young Researcher at Shanghai AI Laboratory specializing in the intersection of Artificial Intelligence and Intelligent Transportation Systems. My passion lies in leveraging cutting-edge AI technologies to solve complex urban mobility challenges and ensure the safe, efficient development of smart cities.

My research focuses on:

• Responsible AI development ensuring trustworthy and equitable smart city solutions
• Multimodal Large Language Models (MLLMs) for real-world safety applications
• AI-driven optimization of intelligent transportation systems
• Multi-agent systems and reinforcement learning for traffic control

Research Highlights

• 25+ peer-reviewed publications in top-tier journals and conferences
• Interdisciplinary & International research expertise: Bridging AI, Transportation Engineering, and Legal frameworks for comprehensive smart city solutions. Joint research partnerships with Imperial College London.
• Real-world impact & practical value: Research findings successfully implemented in urban traffic systems with measurable improvements in city-scale applications

Academic Background

I received my PhD from Zhejiang University, where I worked under the supervision of Prof. Dianhai Wang. My doctoral research focused on developing comprehensive urban traffic state evaluation systems, which have been successfully applied in real-world projects. From 2020 to 2022, I was working at Imperial College London as a visiting scholar, where I collaborated closely with Prof. Washington Ochieng, FREng. I obtained my bachelor’s degree major in Civil Engineering and minor in Law from Zhejiang University in 2017.

My interdisciplinary background allows me to approach transportation challenges from multiple perspectives, fostering innovative solutions that consider technological, legal, and societal implications. I have authored 25+ peer-reviewed publications in top-tier journals and conferences, and actively contributed to national and municipal research projects. I am passionate about contributing to the academic community and fostering the next generation of researchers, actively serving as a reviewer for prestigious journals and conferences such as IEEE Transactions on Systems, Man and Cybernetics.

Research Philosophy

As a researcher, I am committed to developing AI technologies that not only advance the field of Intelligent Transportation Systems but also contribute positively to society. I strive to create solutions that enhance urban mobility, promote sustainability, and ensure equitable access to the benefits of smart city innovations.

“It is better to light a candle than to curse the darkness.”

Education

Selected Publications

• Yu, X., Yu, Y., Liu, D., et al. (2025). EvoBench: Towards Real-World LLM-Generated Text Detection Benchmarking for Evolving Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2025. Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-acl.754.

• Wang, X., Jiang, H., Yu, Y., Yu, J., Lin, Y., Yi, P., Wang, Y., Qiao, Y., Li, L., Wang, F.-Y. (2025). Building intelligence identification system via large language model watermarking: a survey and beyond. Artificial Intelligence Review, 58(8), 1-58.

• Yang, X., Yu, Y., Feng, Y., Ochieng, W. Y. (2024). Improving the Urban Transport System Resilience Through Adaptive Traffic Signal Control Enabled by Decentralised Multiagent Reinforcement Learning. Journal of Advanced Transportation, 2024(1), 3035753.

• Zeng, J., Yu, Y., Chen, Y., Yang, D., Zhang, L., Wang, D. (2023). Trajectory-as-a-Sequence: A novel travel mode identification framework. Transportation Research Part C: Emerging Technologies, 146, 103957.

• Yu, Y., Cui, Y., Zeng, J., He, C., Wang, D. (2022). Identifying traffic clusters in urban networks based on graph theory using license plate recognition data. Physica A: Statistical Mechanics and its Applications, 591, 126750.

• Qi, H., Yu, Y., Tang, Q., Hu, X. (2022). Intersection traffic deadlock formation and its probability: A petri net-based modeling approach. IET Intelligent Transport Systems, 16(10), 1342-1363.

• Cui, Y., Yu, Y., Cai, Z., Wang, D. (2022). Optimizing Road Network Density Considering Automobile Traffic Efficiency: Theoretical Approach. Journal of Urban Planning and Development, 148(1), 04021062.

• Yu Y, Zeng J, Wang D (2021) Free-flow travel time estimation in urban roads based on a data sampling method. Journal of Zhejiang University(Engineering Science).

• Yu, Y., Yao, S., Zhou, T., Fu, Y., Yu, J., Wang, D., Wang, X., Chen, C., Lin, Y. (2024). Data on the Move: Traffic-Oriented Data Trading Platform Powered by AI Agent with Common Sense. In 2024 IEEE Intelligent Vehicles Symposium (IV), pp. 521-526.

• Yu, Y., Yao, S., Li, J., Wang, F.-Y., Lin, Y. (2023). SWDPM: A Social Welfare-Optimized Data Pricing Mechanism. In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2900-2906.