施思
发布时间: 2026-03-17

 ▎基本信息

  • 职称:无

  • 电子邮箱:sishi1126@scut.edu.cn

  • 办公地址:

 ▎研究方向 

工程管理,智能建造                       

 ▎教育与工作经历  

教育经历                            

20209月~20258月,澳门理工大学,计算机应用技术(人工智能方向),博士                       

20129月~20153月,东华大学,企业管理(公司理财方向),硕士                         

20089月~20126月,东华大学,会展经济与管理,学士                            

工作经历                                            

20163月~20191月,上海杉达学院,管理学院,助理教授                            

20154月~201511月,中国工商银行上海市分行,高级柜员                          

 ▎研究成果  

                                    

科研兴趣:智能建造,视觉语言接地,大语言模型,深度学习

科研项目:  

  1. 意大利政府博洛尼亚大学European S3 LiBER项目(参与)

  2. 澳门理工大学Edge Sensing and Computing: Enabling Human-centric (Sustainable)Smart Cities项目(参与)

  3. 澳门理工大学Big Data - Driven Intelligent Computing项目(参与)       

代表性成果   

             

  1. Shi, S., Yuan, H., Li, H., Luo, W., & Pau, G. (2025). Large Language Model-Guided Credit Scoring. In 2025 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 927-936). IEEE.      

  2. Shi, S., Luo, W., & Pau, G. (2025). An attention-based balanced variational autoencoder method for credit card fraud detection. Applied Soft Computing, 113190.

  3. He, L., Shi, S., Zhang, D., Luo, W., (2025). ST-RLNet: Spatio-Temporal Representation Learning for Multi-Step Traffic Flow Prediction. Neurocomputing, 131020.

  4. Li, H., Yu, Y., Shi, S., Hu, A., Huo, J., Lin, W., ... & Luo, W. (2025). Value Decomposition-Based Multi-Agent Learning for Anesthetics Collaborative Control. IEEE Journal of Biomedical and Health Informatics.

  5. Shi, S., Luo, W., Tse, R., & Pau, G. (2024). SparseGraphSage: A Graph Neural Network Approach for Corporate Credit Rating. In Proceedings of the 2024 13th International Conference on Software and Computer Applications (pp. 124-129). ACM.

  6. Shi, S., Luo, W., Tse, R., & Pau, G. (2022). Attention-LGBM-BiLSTM: An Attention-Based Ensemble Method for Knowledge Tracing. In 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) (pp. 306-309). IEEE.

  7. Shi, S., Tse, R., Luo, W., D’Addona, S., & Pau, G. (2022). Machine learning-driven credit risk: a systemic review. Neural Computing and Applications, 34(17), 14327-14339.