周昉

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；

研究方向主要包括：

• 数据挖掘、大规模图分析；

• 深度学习、机器学习、时间序列数据分析、结构回归；

• 以及在金融数据、社交媒体数据、电子病历数据中的应用。

开设课程

1. 数据挖掘（本科选修课）

2. 专业英语（本科必修课）

• 智慧金融关键技术研究 (校企联合项目)

• 面向多源多视图数据的结构化预测模型研究 (国自然)

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

代表论文：

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

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

3. 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.

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

5. Miao Y., Chen Z., Zhou F.* “What if User Preferences Shifts: Causal Disentanglement for News Recommendation”, Proc. 29^th 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 

7. 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. 

8. 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. 

9. 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. 

10. Polychronopoulou, A., Zhou, F. Obradovic, Z.,“Cosine Similarity for Multiplex Network Summarization,” Proc. 2021 IEEE/ACM Int’l Conf. on Advances in Social Networks Analysis and Mining, Nov. 2021

11. Zhou, F., Gillespie, A., Gligorijevic, Dj., Gligorijevic, J., Obradovic, Z. (2020) “Use of Disease Embedding Technique to Predict the Risk of Progression to End-Stage Renal Disease,” Journal of Biomedical Informatics, vol. 105, 103409, 2020.

12. 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.

13. 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. 

14. Fang Zhou, Qiang Qu, Hannu Toivonen, “Summarisation of Weighted Networks”, Journal of Experimental & Theoretical Artificial Intelligence, 2017, 29(5): 1023-1054. 

15. Vujicic, T., Glass, J., Zhou, F., Obradovic, Z. “Gaussian Conditional Random Fields Extended for Directed Graphs,” Machine Learning. 2017, 106(9-10): 1271-1288.

16. Martin Pavlovski, Fang Zhou, Ivan Stojkovic, Ljupco Kocarev, Zoran Obradovic, “Adaptive Skip-Train Structured Regression for Temporal Networks”, ECML-PKDD 2017, pp 305-321.