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.

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.