个人简介：

陈捷，福州大学计算机与大数据学院副教授，博士毕业于复旦大学，福建省引进生。主要研究方向为深度学习，表征学习，图神经网络，大模型与智能体，相关研究成果在CVPR、NeurIPS、KDD、TNNLS等重要国际会议、期刊上发表。同时强调技术落地，与工业界保持紧密联系与合作，曾入选腾讯技术大咖、字节筋斗云等人才计划。

招生信息：

目前硕士招生专业：计算机科学与技术、电子信息等。欢迎对人工智能研究充满热情，想解决实际问题，做出有意义的科研成果，同时对包括但不限于深度学习，表征学习，图神经网络，智能体等方向感兴趣的研究生和本科生与我联系。

联系邮箱：jiechen202@fzu.edu.cn

代表性科研成果：

1) Jie Chen, Zilong Li, Yin Zhu, Junping Zhang, Jian Pu. From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm (CVPR), 2023. (CCF A)

2) Jie Chen, Shouzhen Chen, Junbin Gao, Zengfeng Huang, Junping Zhang, Jian Pu. Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily (TNNLS), 2023. (SCI-1, CCF B)

3) Jie Chen*, Shouzhen Chen*, Mingyuan Bai, Jian Pu, Junping Zhang, Junbin Gao. Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification (TNNLS), 2022. (SCI-1, CCF B)

4) Jie Chen, Shouzhen Chen, Mingyuan Bai, Junbin Gao, Junping Zhang, Jian Pu. SA-MLP: Distilling graph knowledge from GNNs into structure-aware MLP (TMLR), 2024.

5) Jie Chen, Weiqi Liu, Jian Pu. Memory-based Message Passing: Decoupling the Message for Propagation from Discrimination (ICASSP), 2022. (CCF B)

6) Rong Ma, Jie Chen*, Xiangyang Xue, Jian Pu. Automated Label Unification for Multi-Dataset Semantic Segmentation with GNNs (NeurIPS), 2024. (CCF A)

7) Shoumeng Qiu, Jie Chen, Xinrun Li, Ru Wan, Xiangyang Xue, Jian Pu. Make a Strong Teacher with Label Assistance: A Novel Knowledge Distillation Approach for Semantic Segmentation (ECCV), 2024. (CCF B)

8) Zhizhong Huang, Jie Chen, Junping Zhang, Hongming Shan. Learning representation for clustering via prototype scattering and positive sampling (TPAMI), 2022. (SCI-1, CCF A)