李博宇助理教授
办公地址:
邮箱:domilay1031@gmail.com
基本信息

Finalist of AWA NSW Water Awards 2023

Finalist of IAWARDS NSW 2023

Finalist of ITS Australia National Award 2022


研究领域

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。

奖励与荣誉

Finalist of AWA NSW Water Awards 2023

Finalist of IAWARDS NSW 2023

Finalist of ITS Australia National Award 2022


项目经历

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)


代表论文成果

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.

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