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周昉

数据科学与工程学院      

个人资料

  • 部门: 数据科学与工程学院
  • 毕业院校: 芬兰赫尔辛基大学
  • 学位: 博士
  • 学历: 博士
  • 邮编:
  • 联系电话:
  • 传真:
  • 电子邮箱: fzhou@dase.ecnu.edu.cn
  • 办公地址: 中北校区地理馆209
  • 通讯地址:

教育经历

2008-2012,芬兰赫尔辛基大学,博士


工作经历

2022.9-至今,华东师范大学,副教授,博士生导师

2018.12-2022.8,华东师范大学,副研究员

2015-2018,美国天普大学,Postdoc;

2013-2014,英国诺丁汉大学宁波分校,Research fellow;


个人简介

社会兼职

研究方向

研究方向主要包括:

  • 数据挖掘、机器学习、深度学习;

  • 异常检测 (Anomaly detection)

  • 开放集识别 (Open-set Recognition),领域泛化( OOD Generalization),持续学习( Continual learning),适用于复杂开放场景下的检测算法

  • 大模型应用;


欢迎本科生、研究生、博士生加入实验室!

招生与培养

开授课程

开设课程

  1. 数据挖掘(本科选修课)

  2. 专业英语(本科必修课)


科研项目

纵向研究课题

  1. 面向多源多视图数据的结构化预测模型研究 (国自然)

  2. 面向区块链数据的内容管控技术研究 (上海市科技创新行动计划)


校企合作项目

  1. 收钱吧商户贷款及交易行为预测

  2. 收钱吧商户交易行为异常检测

  3. 上海银行内部账号资金异动智能检测

  4. 可研报告自动编制研究








学术成果

详细信息请见个人主页: https://sites.google.com/view/fangzhou


代表论文:

12. Shou H., Lu G., Pavlovski M., Zhou F.*, “READ: Robust and Efficient Anomaly Detection under Data Contamination and Limited Supervision”, Proc. 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD ’25), 2025

11. Wei R., He Z., Pavlovski M., Zhou F.*,  “GAD: A Generalized Framework for Anomaly Detection at Different Risk Levels”, Proc. 33rd Int’l Conf. on Information and Knowledge Management (CIKM), 2024.

10. Miao Y., Zhou F.*,  Pavlovski M., Qian W., “Learning Legal Text Representations via Disentangling Elements”, Expert Systems With Applications, 2024. 

9. Lu G., Zhou F.*, Pavlovski M., Zhou C., Jin C., “A Robust Prioritized Anomaly Detection when Not All Anomalies are of Primary Interest”, Proc. 40th International Conference on Data Engineering (ICDE), 2024.

8. Miao Y., Pavlovski M., Chen Z., Zhou F.*, “Multi-Aspect Matching between Disentangled Representations of User Interests and Content for News Recommendation”, Proc. 29th Int’l Conf. on Database Systems for Advanced Applications (DASFAA), July, 2024

7. Miao Y., Chen Z., Zhou F.* “What if User Preferences Shifts: Causal Disentanglement for News Recommendation”, Proc. 29th Int’l Conf. on Database Systems for Advanced Applications (DASFAA), July, 2024

6. Zhou F.*, Gao S., Ni L., Pavlovski M., Dong Q., Obradovic Z., Qian W., “Dynamic Self-paced Sampling Ensemble for Highly Imbalanced and Class-overlapped Data Classification,” Data Mining and Knowledge Discovery. 2022 

5. Roychoudhury, S. Zhou, F.*, Obradovic, Z., “Leveraging Dependencies among Learned Temporal Subsequences,” Proc. 22nd SIAM Int’l Conf. Data Mining (SDM 2022), Alexandria, VA, May 2022. 

4. Zong W., Zhou F.*, Pavlovski M., Qian W., “Peripheral Instance Augmentation for End-to-End Anomaly Detection using Weighted Adversarial Learning”, Proc. 27th Int’l Conf. on Database Systems for Advanced Applications (DASFAA), April 2022. 

3. Li X., Pavlovski M., Zhou F.*, Dong Q., Qian W., Obradovic Z., “Supervised Multi-view Latent Space Learning by Jointly Preserving Similarities across Views and Samples,” Proc. 27th Int’l Conf. on Database Systems for Advanced Applications (DASFAA), April 2022. 

2. Shoumik Roychoudhury*, Fang Zhou*, Zoran Obradovic.  Leveraging Subsequence-orders for Univariate and Multivariate Time-series Classification, Proc. 19th SIAM Int’l Conf. Data Mining(SDM), Calgary, Canada, May 2019.

1. Martin Pavlovski, Fang Zhou, Nino Arsov, Ljupco Kocarev, Zoran Obradovic, “Generalization-Aware Structured Regression towards Balancing Bias and Variance”, Proc. 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018, pp. 2616-2622. 



荣誉及奖励

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