陈川、博士、中山大学计算机学院副教授、博士生导师，中山大学岭南学院双聘教授。现任Elsevier国际期刊Software Impacts副主编，担任ICLR领域主席(Area Chair)、ICML/NeurIPS/AAAI/IJCAI等多个国际学术会议的（高级）程序委员会委员(Senior PC)，中国计算机学会(CCF)人工智能与模式识别专委会委员。

主要研究方向：可信机器学习理论与应用（联邦学习，机器学习鲁棒性, LLM可靠性等），及图机器学习理论与应用 （图神经网络、社交网络分析等）。近年在ICLR、ICML、NeurIPS、IEEETNNLS、TKDE、TPDS等国际学术会议及期刊发表论文80余篇，包括中科院一区及CCF A类论文60余篇, ESI高被引论文3篇，申请及授权专利20余项。主持国家重点研发计划项目课题，国家自然科学面上项目、青年基金、广东省面上项目、CCF-腾讯犀牛鸟科研基金项目，腾讯微信犀牛鸟专项基金等项目十余项，课题组与多个企业（包括微信/微众银行/网易游戏/美图/平安银行/招联金融）等开展长期的研究合作项目，部分成果完成技术转化并在相关企业及单位中得到应用。入选2024/2025年度全球前2%顶尖科学家榜单（斯坦福大学）、获IEEE Computer Society评选2022年度最佳论文（全球18篇，所在期刊唯一入选）、广东省计算机学会科技进步二等奖、CCF-腾讯犀牛鸟科研基金优秀奖、蚂蚁隐语优秀产学合作奖等。

教学与学生指导：担任《人工智能》（中山大学一流本科课程，评教曾列全校课程前0.02%）课程负责人，获广东省高校（本科）青年教师教学大赛二等奖、广东省高校教师教学创新大赛二等奖​、中国电子学会电子信息教学成果大赛二等奖、中山大学教师教学竞赛教学赛道/创新赛道一等奖、中山大学本科教育教学成果二等奖。指导学生曾获CCF优秀大学生奖,腾讯犀牛鸟精英人才，ICDM/CIKM/SIAM/AAAI等会议Travel Grand,“隐语杯”全国高校隐私计算大赛决赛一等奖（1/74), 校级优秀毕业论文20余次。

研究领域：

联邦学习、机器学习鲁棒性、LLM可靠性、图机器学习、社交网络分析

主要学术兼职: 

• Software Impacts (Elsevier期刊) 副主编
• The International Conference on Learning Representations (ICLR) 2025 领域主席
• International Joint Conference on Artificial Intelligence (IJCAI) 2021 2024高级程序委员
• 广东省科技厅科技业务管理阳光政务平台重构专家委员会成员
• 数字广东建设专家委员会专家

代表论文：

• [J] T. Liao, Z. Xu, Q. Hu, H. Dai, H. Huang, Z. Zheng, C. Chen*, "FedBRB: A Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning", IEEE Transactions on Mobile Computing. 2025 (中科院一区, CCF-A类)
• [J] L. Fu, S. Huang, Y. Lai, C. Chen*, C. Zhang, H. Dai, Z. Zheng, "Federated Domain-Independent Prototype Learning with Alignments of Representation and Parameter Spaces for Feature Shift", IEEE Transactions on Mobile Computing. 2025 (中科院一区, CCF-A类)
• [J] L. Fu, Y. Li, S. Huang, C. Chen*, C. Zhang, Z. Zheng, "Parameter-oriented Contrastive Schema and Multi-level Knowledge Distillation for Heterogeneous Federated Learning", Information Fusion. 2025 (中科院一区)
• [J] T. Liao, L. Fu, L. Zhang, L. Yang, M. Ng, C. Chen*, H. Huang, Z. Zheng, "Privacy-Preserving Vertical Federated Learning with Tensor Decomposition for Data Missing Features", IEEE Transactions on Information Forensics & Security. 2025 (中科院一区, CCF-A类)
• [J] L. Fu, S. Huang, Y. Li, C. Chen*, C. Zhang, Z. Zheng, "Learning the Global Prompt in the Low-rank Tensor Space for Heterogeneous Federated Learning", Neural Networks. 2025 (中科院一区, CCF-B类)
• [J] Y. Liu, L. Shu, C. Chen*, Z. Zheng, "Fine-grained Semantics Enhanced Contrastive Learning for Graphs", IEEE Transactions on Knowledge and Data Engineering. 2024 (CCF-A类)
• [J] S. Zhao, T. Liao, L. Fu, C. Chen*, J. Bian, Z. Zheng, "Data-Free Knowledge Distillation via Generator-Free Data Generation for Non-IID Federated Learning", Neural Networks. 2024 (中科院一区, CCF-B类)
• [J] S. Huang, L. Fu, Y. Li, C. Chen*, Z. Zheng, H. Dai, "A Cross-client Coordinator in Federated Learning Framework for Conquering Heterogeneity", IEEE Transactions on Neural Networks and Learning Systems. 2024 (中科院一区, CCF-B类)
• [J] L. Zhang, L. Fu, C. Liu, Z. Yang，J. Yang, Z. Zheng, C. Chen*, "Towards Few-Label Vertical Federated Learning", ACM Transactions on Knowledge Discovery from Data. 2024 (CCF-B类)
• [J] Y. Hu, T. Liao, J. Chen, C. Chen*, J. Bian, Z. Zheng, "Migrate Demographic Group For Fair Graph Neural Networks", Neural Networks. 2024 (中科院一区, CCF-B类)
• [J] T. Liao, J. Yang, C. Chen*, Z. Zheng, "A Neural Tensor Decomposition Model for High-Order Sparse Data Recovery", Information Sciences. 2023 (中科院一区, CCF-B类)
• [J] J. Lai, T. Wang, C. Chen*, Z. Zheng, "Information-Aware Multi-View Outlier Detection", ACM Transactions on Knowledge Discovery from Data. 2023 (CCF-B类)
• [C] J. Chen, T. Wang, B. Deng, L. Chen, Z. Zheng, C. Chen*, "Self-Assembling Graph Perceptrons", NeurIPS 2025. (CCF-A类, Spotlight) 
• [C] S. Huang, L. FU,  F. Ye, T. Liao, B. Deng, C. Zhang, C. Chen*, "Soft-consensual Federated Learning for Data Heterogeneity via Multiple Paths", NeurIPS 2025. (CCF-A类) 
• [C] L. FU, S. Huang, T. Liao, B. Deng, C. Zhang, S. Pan, C. Chen*, "Unsupervised Federated Graph Learning", NeurIPS 2025. (CCF-A类) 
• [C] B. Deng, L. FU, S. Huang, T. Liao, J. Chen, T. Zhang, C. Chen*, "GLNCD: Graph-Level Novel Category Discovery", NeurIPS 2025. (CCF-A类) 
• [C] Y. Li, L. Fu, T. Wang, J. Lou, B. Chen, L. Yang, J. Shen, Z. Zheng, C. Chen*, "Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off", ICML 2025. (CCF-A类) 
• [C] L. Fu, B. Deng, S. Huang, T. Liao, S. Pan, C. Chen*, "Less is More: Federated Graph Learning with Alleviating Topology Heterogeneity from A Causal Perspective", ICML 2025. (CCF-A类) 
• [C] B. Deng, L. Fu, J. Chen, S. Huang, T. Liao, T. Zhang, C. Chen*, "Towards Understanding Parametric Generalized Category Discovery on Graphs", ICML 2025. (CCF-A类) 
• [C] T. Liao, B. Xie, L. Fu, S. Huang, B. Deng, C. Chen*, Z. Zheng, "Federated Domain Generalization with Decision Insight Matrix", IJCAI 2025. (CCF-A类) 
• [C] L. Fu, B. Deng, S. Huang, T. Liao, C. Zhang, C. Chen*, "Learn from Global Rather Than Local: Consistent Context-Aware Representation Learning for Multi-View Graph Clustering", IJCAI 2025. (CCF-A类) 
• [C] S. Huang, L. Fu, T. Liao, B. Deng, C. Zhang, C. Chen*, "FedBG: Proactively Mitigating Bias in Cross-Domain Graph Federated Learning Using Background Data", IJCAI 2025. (CCF-A类) 
• [C] J. Chen, B. Deng, Z. Wang, C. Chen*, Z. Zheng, "Graph Neural Ricci Flow: Evolving Feature from a Curvature Perspective", ICLR 2025. 
• [C] L. Fu, S. Huang, Y. Lai, T. Liao, C. Zhang, C. Chen*, "Beyond Federated Prototype Learning: Learnable Semantic Anchors with Hyperspherical Contrast for Domain-Skewed Data", AAAI 2025. (CCF-A类, Oral) 
• [C] B. Deng, T. Wang, L. Fu, S. Huang, C. Chen*, T. Zhang*, "THESAURUS: Contrastive Graph Clustering by Swapping Fused Gromov-Wasserstein Couplings", AAAI 2025. (CCF-A类) 
• [C] T. Liao, J. Chen, L. Fu, Z. Wang, Z. Zheng, C. Chen*, "A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm", NeurIPS 2024. (CCF-A类)