李博宇，必威betway西汉姆助理教授，2018 年毕业于北京邮电大学，获电信工程学士学位；2020 年毕业于悉尼大学，获计算机科学硕士学位；2025 年毕业于悉尼科技大学，获计算机科学博士学位 。2025 年任悉尼科技大学博士后研究员。主要研究方向包括机器学习，推荐系统、图神经网络、大语言模型以及人工智能在交通与金融领域的应用。曾担任悉尼地铁、悉尼水务局等多个工业级科研项目的技术负责人。在 WSDM, AAAI, ECML-PKDD, IEEE IoT Journal等国际顶级或权威的会议及期刊上发表多篇学术论文。

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.

主要研究领域包括机器学习，图神经网络，大语言模型，推荐系统，智能交通系统等。

欢迎偏爱理论研究以及热衷应用落地的同学加入。让我们一起在不确定性中，寻找那个更优的解法。

Finalist of AWA NSW Water Awards 2023

Finalist of IAWARDS NSW 2023

Finalist of ITS Australia National Award 2022

澳大利亚政府-企业联合研究项目，基于深度学习的股市交易异常检测，2023-2025（参与）

澳大利亚政府科研资助项目，关键基础设施属性建模与风险最小化，2023-2025（参与）

澳大利亚政府-企业联合研究项目，道路中断事件下替代公交服务的规划与优化，2025（参与）

澳大利亚企业联合研究项，基于机器学习的交通监测系统优化，2021-2022（参与）

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.