周昉 - 华东师范大学 - 数据科学与工程学院

个人简介

教育经历 2008-2012，芬兰赫尔辛基大学，博士工作经历 2013-2014，英国诺丁汉大学宁波分校，Research fellow 2015-2018，美国天普大学，Postdoc 2018.12-2022.8，华东师范大学，副研究员 2022.9-至今，华东师范大学，副教授

研究领域

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

近期论文

查看导师新发文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

Miao Y., Zhou F.*, Pavlovski M., Qian W., “Learning Legal Text Representations via Disentangling Elements”, Expert Systems With Applications, 2024. 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. 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. Wei X., 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-2022), April 2022. 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-2022), April 2022. 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 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. 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. 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. Fang Zhou, Qiang Qu, Hannu Toivonen, “Summarisation of Weighted Networks”, Journal of Experimental & Theoretical Artificial Intelligence, 2017, 29(5): 1023-1054. Vujicic, T., Glass, J., Zhou, F., Obradovic, Z. “Gaussian Conditional Random Fields Extended for Directed Graphs,” Machine Learning. 2017, 106(9-10): 1271-1288. Martin Pavlovski, Fang Zhou, Ivan Stojkovic, Ljupco Kocarev, Zoran Obradovic, “Adaptive Skip-Train Structured Regression for Temporal Networks”, ECML-PKDD 2017, pp 305-321.