Basic Information:
Dr. Boyu Li is an Assistant Professor at the School of Statistics and Data Science, Southeast University. He graduated from Beijing University of Posts and Telecommunications in 2018 with a Bachelor#39;s Degree in Telecommunication Engineering; graduated from the University of Sydney in 2020 with a Master#39;s Degree in Computer Science; and graduated from the University of Technology Sydney in 2025 with a Doctoral Degree in Computer Science. He served as a Postdoctoral Research Fellow at the University of Technology Sydney in 2025. His main research interests include machine learning, recommendation systems, graph neural networks, large language models, and the applications of artificial intelligence in transportation and finance. He has served as the technical lead for multiple industrial-scale research projects such as Sydney Trains and Sydney Water Corporation. He has published numerous academic papers in top-tier or authoritative international conferences and journals including WSDM, AAAI, ECML-PKDD, and IEEE IoT Journal.
Research area:
His main research areas include machine learning, graph neural networks, large language models, recommender systems, and intelligent transportation systems.
Selected Papers:
Journal Papers:
[1] Guo, T., Li, B., Stenfors, A., Hewage, K., Mere, P., Chen, F. (2025). Shadow trading detection: A graph-based surveillance approach. Finance Research Letters, 108524.
[2] Li, B., Guo, T., Li, R., Wang, Y., Gandomi, A. H., Chen, F. (2023). Self-adaptive predictive passenger flow modeling for large-scale railway systems.IEEE Internet of Things Journal, 10(16), 14182-14196.
[3] Li, B., Guo, T., Li, R., Wang, Y., Ou, Y., Chen, F. (2021). Delay propagation in large railway networks with data-driven Bayesian modeling. Transportation Research Record, 2675(11), 472-485.
Conference Papers:
[1] Zhang, Y., Li, B., Ling, Z., Zhou, F. (2024, March). Mitigating label bias in machine learning: Fairness through confident learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 15, pp. 16917-16925).
[2] Li, B., Guo, T., Zhu, X., Wang, Y., Chen, F. (2023, September). Congcn: Factorized graph convolutional networks for consensus recommendation. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 369-386). Cham: Springer Nature Switzerland.
[3] Li, B., Guo, T., Zhu, X., Li, Q., Wang, Y., Chen, F. (2023, February). SGCCL: Siamese graph contrastive consensus learning for personalized recommendation. In Proceedings of the sixteenth ACM international conference on web search and data mining (pp. 589-597).
[4] Li, B., Guo, T., Wang, Y., Gandomi, A. H., Chen, F. (2021, May). Adaptive graph co-attention networks for traffic forecasting. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 263-276). Cham: Springer International Publishing.
Book:
[1] Li, B., Guo, T., Wang, Y., Chen, F. (2021). The Future of Transportation: How to Improve Railway Operation Performance via Advanced AI Techniques. In Humanity Driven AI: Productivity, Well-being, Sustainability and Partnership(pp. 85-110). Cham: Springer International Publishing.
[2] Li, B., Guo, T., Wang, Y., Chen, F. (2023). Data‐driven delay analysis with applications to railway networks.Advances in Data Science and Analytics: Concepts and Paradigms, 115-143.
