殷荣，博士，北航副教授。长期从事大模型微调、多模态、智能体Memory/强化学习/自演化、信息内容安全、图神经网络、智能决策、机器学习理论等方向研究，发表顶级会议和期刊论文近40篇，其中 CCF-A/中科院一区论文近30篇，涵盖 NeurIPS、ICML、AAAI、MM、IEEE TIP、IEEE TKDE、IEEE TMM、IEEE TNNLS 和 Information Fusion 等。主持/参与国家级或省部级重点项目20余项，包括国家自然科学基金委青年/面上基金、国家重点研发计划、省重大支撑、中科院特别研究助理项目等。曾入选中国科学院特别研究助理（人才项目）、中国科学院信息工程研究所优秀引进人才，担任10余个CCF-A类国际会议的程序委员，比如：ICML、NeurIPS、ICLR、ACL、MM、JMLR，担任国际期刊 MDPI Mathematics（JCR-Q1）和Cybersecurity（JCR-Q1，CCF-B）客座编辑、信息安全学报（CCF-T2）青年编委，担任 CCF 人工智能与模式识别专委会委员、中国指挥与控制学会情报与智能认知专委会委员。长期在一线科研，具有丰富科研经验，曾指导博士生3年发表8篇CCF-A/中科院一区论文，发表于NeurIPS、ICML、AAAI、TIP、TMM等，获得国家奖学金。与小米/阿里等企业长期合作，可根据需要推荐学生去企业进行科研类实习/工作。

课题组长期接收本科生实习，欢迎申硕士/博士学生提前进组学习，招满即止。

简历发至邮箱：yinrong@buaa.edu.cn。

邮件主题：本科生实习/ 硕士（保/考）/博士（保/考）申请_学校_姓名_年级_专业，附上简历。

希望学生：

1. 具有良好的英语、编程能力；

2. 具有自驱力、主动学习能力，踏实勤奋、积极沟通；

3. 能稳定投入时间，最终产出高水平论文；

4. 本科生有相关专业基础优先，研究生发表过CCF-A/B/一区论文优先；

5. 期待未来彼此信任、并肩作战、共同成长。

部分国际会议/期刊论文：

1. Feature Decoupling-Based Bilateral Adaptive Personalized Federated Graph Learning. In IEEE Transaction on Multimedia, 2026. (TMM 2026)(CCF-A, 中科院一区，影响因子：9.7)(通讯作者).

2. Sketch-Based Low-Rank Model Merging with Shared Circulant Transforms. In Proceedings of the 43th International Conference on Machine Learning. (ICML 2026) (CCF-A)(通讯作者).

3. Self-supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes. In IEEE Transactions on Neural Networks and Learning Systems, 2026. (TNNLS 2026)(中科院一区，影响因子：8.9)(通讯作者). 

4. Multi-Modal Molecular Representation Learning via Structure Awareness. In IEEE Transactions on Image Processing, 2025, 34: 3225-3238. (TIP 2025) (CCF-A，中科院一区，影响因子：13.7)(一作).

5. SSTAG: Structure-Aware Self-Supervised Learning Method for Text-Attributed Graphs. In Proceedings of Advances in Neural Information Processing Systems, 2025. (NeurIPS 2025) (CCF-A)(通讯作者).

6. AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning. In IEEE Transaction on Multimedia, 2025. (TMM 2025)(CCF-A, 中科院一区，影响因子：9.7)(通讯作者).

7. MapFusion: A novel BEV feature fusion network for multi-modal map construction. In Information Fusion, 2025. (IF 2025)(中科院一区，影响因子：14.8)(共同通讯作者).

8. SafeMap: Robust HD Map Construction from Incomplete Observations. In Proceedings of the 42th International Conference on Machine Learning. (ICML 2025) (CCF-A).

9. MSC-Bench: Benchmarking and Analyzing Multi-Sensor Corruption for Driving Perception. In IEEE International Conference on Multimedia & Expo. (ICME 2025) (CCF-B)(共同通讯作者). 

10. FedNK-RF: Federated Kernel Learning with Heterogeneous Data and Optimal Rates. In IEEE Transactions on Neural Networks and Learning Systems, 2025. (TNNLS 2025) (中科院一区，影响因子：8.9).

11. DADA++: Dual Alignment Domain Adaptation for Unsupervised Video-Text Retrieval. ACM Transactions on Multimedia Computing, Communications, and Applications, 2025. (TOMM 2025) (CCF-B，中科院二区，JCR-Q1)

12. What Really Matters for Robust Multi-Sensor HD Map Construction? In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. (IROS 2025) (CORE-A).  

13. Dual Prompt Clustering: Aligning and Adapting Multi-Views via Prompt Learning. Neurocomputing. (NC 2025) (中科院二区，影响因子：6.5，JCR-Q1).

14. Improving Mathematical Reasoning Abilities of Small Language Models via Key-Point-Driven Distillation. In Proceedings of International Joint Conference on Neural Networks. (IJCNN 2025) (CORE-A).

15. Improving Mathematical Reasoning Capabilities of Small Language Models via Feedback-Driven Distillation. In Proceedings of International Joint Conference on Neural Networks. (IJCNN 2025) (CORE-A).

16. ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network. In Proceedings of the 38th AAAI Conference on Artificial Intelligence, 2024, 38(12): 13990-13998. (AAAI 2024) (CCF-A)(通讯作者).

17. Unbiased and Augmentation-Free Self-Supervised Graph Representation Learning. In Pattern Recognition, 2024. (PR 2024) (中科院一区，影响因子：8)(通讯作者).

18. FTF-ER: Feature-Topology Fusion-Based Experience Replay Method for Continual Graph Learning. In Proceedings of the ACM Multimedia, 2024: 8336-8344. (MM 2024) (CCF-A).

19. MapDistill: Boosting Efficient Camera-based HD Map Construction via Camera-LiDAR Fusion Model Distillation. In Proceedings of European Conference on Computer Vision, 2024: 166-183. (ECCV 2024) (CCF-B).

20. Scalable Kernel k-Means with Randomized Sketching: From Theory to Algorithm. In IEEE Transactions on Knowledge and Data Engineering, 2023, 35(9): 9210-9224. (TKDE 2023) (CCF-A，中科院一区，影响因子：9.235)(一作).

21. Randomized Sketches for Clustering: Fast and Optimal Kernel k-Means. In Proceedings of Advances in Neural Information Processing Systems, 2022, 35: 6424-6436. (NeurIPS 2022) (CCF-A)(一作).

22. Distributed Randomized Sketching Kernel Learning. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, 2022, 36(8): 8883-8891. (AAAI 2022) (CCF-A)(一作).

23. Distributed Nystrom Kernel Learning with Communications. In Proceedings of the 28th International Conference on Machine Learning, PMLR, 2021: 12019-12028. (ICML 2021) (CCF-A)(一作).

24. Divide-and-Conquer Learning with Nyström: Optimal Rate and Algorithm. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020, 34(04): 6696-6703. (AAAI 2020) (CCF-A)(一作).

25. Sketch Kernel Ridge Regression using Circulant Matrix: Algorithm and Theory. In IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(9): 3512-3524. (TNNLS 2020) (中科院一区，影响因子：11.683)(一作).

26. Extremely sparse Johnson-Lindenstrauss transform: From theory to algorithm. In Proceedings of IEEE International Conference on Data Mining, 2020: 1376-1381. (ICDM 2020) (CCF-B)(一作).

27. Multi-Class Learning using Unlabeled Samples: Theory and Algorithm. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019: 2880-2886. (IJCAI 2019) (CCF-A).

28. Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019: 2887-2893. (IJCAI 2019) (CCF-A).

29. Multi-class learning: From theory to algorithm. In Proceedings of Advances in Neural Information Processing Systems, 2018. (NeurIPS 2018) (CCF-A).

30. Hashing Based Prediction for Large-Scale Kernel Machine. In Proceedings of the International Conference on Computational Science, 2020: 496-509. (ICCS 2020)(CORE-A). 

31. 基于分层状态机行为建模的高隐蔽攻击检测方法。信息安全学报2026。(CCF-T2).