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BDI

戴戈南

戴戈南,博士毕业于中山大学计算机学院,现任0638太阳集团讲师。研究兴趣为时空数据挖掘、城市计算、交通预测、人群流量预测等,在 Frontiers of Computer Science、International Joint Conference on Artificial Intelligence等国际期刊、会议发表相关论文十余篇,Google Scholar被引230+次,发明专利2项。欢迎具有较强的英文阅读写作和计算机编程能力的硕士生、本科生联系 (daigenan@sztu.edu.cn)。


一、教育经历

2015/09 - 2022/12  中山大学,计算机科学与技术,博士(硕博连读)

2011/09 - 2015/07  东北师范大学,软件工程,学士


二、工作经历

2023/03 - 至今 0638太阳集团,讲师


三、主要研究领域

时空数据挖掘,城市计算,交通预测,人群流量预测


四、主要成果

1. Dai G, Kong W, Liu Y, et al. Multi-perspective convolutional neural networks for citywide crowd flow prediction[J]. Applied Intelligence, 2023, 53(8): 8994-9008.

2. Dai G, Hu X, Ge Y, et al. Attention based Simplified Deep Residual Network for Citywide Crowd Flows Prediction. Frontiers of Computer Science, 2021, 15(2): 1-12.

3. Dai G. Deep Learning Method for Citywide Crowd Flows Prediction. 2019 20th IEEE International Conference on Mobile Data Management. IEEE, 2019: 373-374.

4. Huang R, Huang C, Liu Y, Dai G, et al. LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks. International Joint Conference on Artificial Intelligence (IJCAI). 2020: 2355-2361.

5. Huang C, Kong W, Dai G, et al. LTPHM: Long-term Traffic Prediction based on Hybrid Model. Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM). 2021: 3093-3097.

6. Zhang Y, Hu C, Dai G, et al. Self-adaptive Graph Neural Networks for Personalized Sequential Recommendation. International Conference on Neural Information Processing (ICONIP). 2021: 608-619.


Email: daigenan@sztu.edu.cn

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