教育经历
2009年，香港中文大学，计算机科学与工程专业，获博士学位
2007-2008年，密西根州立大学，访问学者
工作经历
现任复旦大学人工智能创新与产业研究院教授，上海科学智能研究院兼职研究员；
曾在哈工大深圳、电子科技大学、普度大学、德国马普计算机所/萨尔大学等高校从事科研与教学工作；
长期担任NeurIPS、AAAI、IJCAI、ACL、EMNLP、ICML、KDD、ICLR等国际会议的领域主席、资深程序委员会成员、审稿人等。
研究方向
机器学习及应用领域，重点关注科学人工智能与可信人工智能，包括多模态学习、时间序列分析、图神经网络、张量神经网络、大语言模型、可信联邦学习、隐私计算等
主要成果
在重要国际期刊(TPAMI、TNNLS、TKDE等)和重要国际会议（NeurIPS、ICML、AAAI、IJCAI等）上发表文章200 余篇。谷歌学术引用 10800多次，H指数为54。主要研究获得国家自然基金委、科技部重点研发计划、深圳市基础研究重点项目及企业联合研究支持。
IEEE和INNS资深会员。因在多源数据分析方面的贡献获得亚太神经网络协会（APNNS）2016年青年学者奖，AAAI2015 最佳学生论文奖提名，ACML2016 最佳学生论文奖亚军，ICONIP 2023最佳论文候选。
招生专业
依托计算机科学技术学院招生，招生专业为计算机科学与技术。本年度招收学术/工程博士生 2-3名，招生方向为可信人工智能、联邦学习、智能体、时间序列分析、金融和医疗多模态大模型等方向。
发表论文
近5年发表论文节选：
1. Liangjian Wen, Xiasi Wang, Jianzhuang Liu, Zenglin Xu: MVEB: Self-Supervised Learning with Multi-View Entropy Bottleneck, IEEE Trans. on Pattern Analysis and Machine Intelligence, 2024.
2. Yifei Zhang, Dun Zeng, Jinglong Luo, Xinyu Fu, Guanzhong Chen, Zenglin Xu, Irwin King: A Survey of Trustworthy Federated Learning: Issues, Solutions, and Challenges. ACM Transactions on Intelligent Systems and Technology, 2024
3. Langzhang Liang, Zenglin Xu, Zixing Song, Irwin King, Yuan Qi, Jieping Ye: Tackling Long-Tailed Distribution Issue in Graph Neural Networks via Normalization. IEEE Trans. Knowl. Data Eng, 2024
4. Yu Pan, Ye Yuan, Yichun Yin, Zenglin Xu, Lifeng Shang, Xin Jiang, Qun Liu: Reusing Pretrained Models by Multi-linear Operators for Efficient Training. NeurIPS 2023
5. Langzhang Liang, Xiangjing Hu, Zenglin Xu, Zixing Song, Irwin King: Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily. NeurIPS 2023
6. Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu: FedLab: A Flexible Federated Learning Framework, Opensource Software, Journal of Machine Learning Research, 2023, 24 (100), 1-7.
7. Zhuo Zhang, Yuanhang Yang, Yong Dai, Qifan Wang, Yue Yu, Lizhen Qu, Zenglin Xu: FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models. ACL (Findings) 2023
8. Shujiong Tang, Yue Yu, Hui Wang, Guiliang Wang, Wuhui Chen, Zenglin Xu, Song Guo, Wen Gao: A Survey on Scheduling Techniques in Computing and Network Convergence. IEEE Commun. Surv. Tutorials, 2024
9. Jing Xu, Yu Pan, Xinglin Pan, Steven Hoi, Zhang Yi, Zenglin Xu. RegNet: Self-regulated network for image classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023
10. Qingzhong Ai, Lirong He, Shiyu Liu, Zenglin Xu. Bype-vae: Bayesian pseudocoresets exemplar vae[J]. Advances in Neural Information Processing Systems(NeurIPS), 2021
11. Xu Luo, Longhui Wei, Liangjian Wen, Jinrong Yang, Lingxi Xie, Zenglin Xu*, Qi Tian*. Rectifying the shortcut learning of background: Shared object concentration for few-shot image recognition. Advances in Neural Information Processing Systems(NeurIPS), 2021
12. Zhao Kang, Haiqi Pan, Steven C. H. Hoi, Zenglin Xu*. Robust graph learning from noisy data. IEEE transactions on cybernetics, 2019
13. Zenglin Xu, Bin Liu, Shandian Zhe, Haoli Bai, ZihanWang, Jennifer Neville. Variational random function model for network modeling. IEEE transactions on neural networks and learning systems, 2019, 30(1): 318-324.
14. Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu. Learning compact recurrent neural networks with block-term tensor decomposition, Proceedings of the IEEE conference on computer vision and pattern recognition. 2018
15. Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu. Superneurons: Dynamic GPU memory management for training deep neural networks, Proceedings of the 23rd ACM SIGPLAN symposium on principles and practice of parallel programming. 2018
更多文章请参考：Google Scholar，DBLP。
