Min Zhou
Principal Researcher · Huawei Technologies/ 引望
I received the B.S. degree in Automation from the University of Science and Technology of China, and the Ph.D. degree from the Industrial Systems Engineering and Management Department, National University of Singapore. I worked on pattern mining and machine learning, and their applications in sequence and graph data and now focus on Continuous Data Cycle via/for Multimodal Foundation Models . I am also passionate about music, dance and various sports, e.g., diving, hiking and badminton.
Contact: zhoum1900@163.com
Research:
Google Scholar
ORCID
News
- Oct. 2024 We will organize Failure Prediction Competition and Workshop on Webconf 2025.
- Nov. 2023 We will give a Tutorial on Hardware Failure Prediction for Cloud Service Reliability on HCPA 2025.
- Nov. 2023 We will give a Tutorial on Hyperbolic graph neural networks: A tutorial on methods and applications on KDD 2023.
- Apr. 2022 We will give a Tutorial on Hyperbolic Representation Learning at ECML-PKDD 2022.
- Apr. 2022 1 dataset paper on the hierarchical network is accepted by GBL@Webconf 2022.
Publications
Conference
- W. Qin, W. Bao, J. Wang, M. Zhou. Double Queue for Constrained Online Convex Optimization: Bridging the Best-of-Two-Worlds Constraint Violations. INFOCOM, 2026. [PDF]
- L. Yan, S. Zhang, B. Li, M. Zhou, Z. Huang. Unreal: Unlabeled nodes retrieval and labeling for heavily-imbalanced node classification.NeurIPS, 2025. [PDF]
- H. Xie, M. Zhou, Q. Yu, J. Yu, Z. Sheng, H. Xie, D. Lian.M2-MFP: A Multi-Scale and Multi-Level Memory Failure Prediction Framework for Reliable Cloud Infrastructure. KDD, 2025. [PDF]
- M. Zhou, H. Xie, Q. Yu, J. Yu, Z. Sheng.SmartMem: Memory Failure Prediction Challenge at WWW 2025. Companion Proceedings of the ACM on Web Conference, 2025, 3003–3007. [PDF]
- Z. Guo, Q. Sun, H. Yuan, X. Fu, M. Zhou, Y. Gao, J. Li.GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts. AAAI, 2025. [PDF]
- J. Han, S. Feng, M. Zhou, X. Zhang, Y. S. Ong, X. Li.Diffusion Model in Normal Gathering Latent Space for Time Series Anomaly Detection. ECML-PKDD, 2024, 284–300. [PDF]
- T. Yang, J. Meng, M. Zhou, Y. Yang, Y. Wang, X. Li, Y. Tong. You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learning.CIKM, 2024. [PDF]
- K. Li, T. Yang, M. Zhou, J. Meng, et al. SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning.KDD, 2024. [PDF]
- Q. Yu, W. Zhang, M. Zhou, J. Yu, Z. Sheng, J. Bogatinovski, J. Cardoso, O. Kao. Investigating Memory Failure Prediction Across CPU Architectures.DSN, 2024. [PDF]
- M. Zhou, B. Li, S. Zhang, M. Yang, D. Lian, Z. Huang. BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction. SIGIR, 2022. [PDF]
- M. Zhou, B. Li, M. Yang, L. Pan. TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction. GLB@Webconf, 2022.
- M. Yang, M. Zhou, et al. HRCF: Enhanced Collaborative Filter via Hyperbolic Geometric Regularization. Webconf, 2022.
- J. Liu, M. Zhou, et al. Discovering Representation Attribute-stars via Minimum Description Length. ICDE, 2022.
- J. Liu, M. Yang, M. Zhou, et al. Enhancing Hyperbolic Graph Embeddings via Contrastive Learning. SSL@Neurips, 2021.
- M. Yang, M. Zhou, et al. Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning. KDD, 2021.
- Z. Huang, S. Zhang, C. Xi, T. Liu, M. Zhou. Scaling Up Graph Neural Networks via Graph Coarsening. KDD, 2021. [PDF]
Journal
- H. Qiao, S. Feng, M. Zhou, et.al. Influence Strength Estimation in Hyperbolic Space for Social Influence Maximization. TKDE, 2026.
- S. Zhang, Y. Zhang, B. Li, W. Yang, M. Zhou, Z. Huang.Graph Batch Coarsening framework for scalable graph neural networks. Neural Networks 2025. [PDF]
- C. Wu, D. Lian, Y. Ge, M. Zhou, E. Chen. Attacking social media via behavior poisoning.ACM Transactions on Knowledge Discovery from Data 18 (7), 1–27. [PDF]
- J. Liu, F. Philippe, M. Zhou, G. He. Discovering Compressing Stars in Attributed Graphs. Information Sciences, 2022.
- F.-V. Philippe, et al., M. Zhou, et al. A survey of pattern mining in dynamic graphs. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2020.
- J. Hu, M. Zhou, X. Li, Z. Xu. Online Model Regression for Nonlinear Time-varying Manufacturing Systems. Automatica, 2016.
- M. Zhou, T. N. Goh. Iterative Designed Experiment Analysis (IDEA). Quality and Reliability Engineering International, 2016.
- M. Zhou, T. N. Goh. Effects of Model Accuracy on Residual Control Charts. Quality and Reliability Engineering International, 2015.
Activities
- Section Chair/Organizer, DataFun GNN Summit 2022/2023.
- Co-organizer, MLiSE (Machine Learning for System Engineering) Workshop @ ECML-PKDD 2021.
- Mentor, SDSC 6002 Research Projects for Data Science, City University of Hong Kong, Winter 2019, 2020.
- Reviewer @ ICML,ICLR,Neurips,KDD,AAAI,ACL,ECML-PKDD,TPAMI,TKDE.
- Technical committee member and Session Chair @ Asian Network for Quality, Winter 2014.
Projects
- TBA
Links
- TBA