I am currently a lecturer at School of Statistics, University of International Business and Economics. I got my Ph.D. degree in Statistics from School of Statistics, Renmin University of China in 2022. My research interests include data mining, deep learning, social network analysis and high-frequency financial data analysis.

Educations

  • 2022, Ph.D., Statistics, School of Statistics, Renmin University of China
  • 2018, Master, Computer application technology, School of Computer Science and Technology, University of Chinese Academy of Sciences
  • 2015, Bachelor, Information management and information systems, School of Information, Renmin University of China
朱映秋 Yingqiu Zhu

Lecturer
School of Statistics
University of International Business and Economics
Huixin Dong Street No.10
Beijing
China

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Publications

Journal articles
  1. Qin, L., Wang, Y.*, Zhu, Y.*, Shia, B. (2025). Bayesian Dynamic Matrix Factor Models, Journal of Business & Economic Statistics. (Accepted)
  2. Zhu, Y., Huang, D., Zhang, B. (2025). A Wasserstein Distance-Based Spectral Clustering Method for Transaction Data Analysis. Expert Systems With Applications, 260, 125418.
  3. Zhu, Y., Wang, Y., Qin, L., Zhang, B., Shia, B., Chen, M. (2025). Naïve Bayes Classifier Based on Reliability Measurement for Datasets with Noisy Labels. Annals of Operations Research, 349, 259-286.
  4. Qin, L., Zhu, Y.*, Liu, S., Zhang, X., Zhao, Y. (2025). The Shapley Value in Data Science: Advances in Computation, Extensions, and Applications. Mathematics, 13(10), 1581.
  5. Zhu, Y., Bao, Y., Qin, L., Sun, Q., Shia, B., Chen, M. (2025). Resilience Analysis Based on Multi-Layer Network Community Detection of Supply Chain Network. Annals of Operations Research. https://doi.org/10.1007/s10479-024-06426-2
  6. Zhu, Y., Wang, R., Feng, M., Qin, L., Shia, B., Chen, M. (2024). Supply Chain Analysis Based on Community Detection of Multi-Layer Weighted Networks. Mathematics, 12(22), 3606.
  7. Zeng, Q., Zhu, Y.*, Zhu, X., Wang, F., Zhao, W., Sun, S., Su, M., Wang, H. (2024). Improved Naive Bayes with Mislabeled Data. Statistics and Its Interface, 17(3), 323-336.
  8. Wang, Y., Zhu, Y., Sun, Q., Qin, L. (2024). Adaptively Robust High-Dimensional Matrix Factor Analysis under Huber Loss Function. Journal of Statistical Planning and Inference. 231, 106137.
  9. Xu, K., Zhu, Y.*, Liu, Y., Wang, H. (2024). CluBear: A Subsampling Package for Interactive Statistical Analysis with Massive Data on a Single Machine. Communications in Statistics-Simulation and Computation. https://doi.org/10.1080/03610918.2023.2300747.
  10. Deng, J., Huang, D., Ding, Y., Zhu, Y., Jing, B., Zhang, B. (2024). Subsampling Spectral Clustering for Stochastic Block Models in Large-Scale Networks. Computational Statistics & Data Analysis, 189, 107835.
  11. Pan, R., Zhu, Y.*, Guo, B., Zhu, X., Wang, H. (2023). A Sequential Addressing Subsampling Method for Massive Data Analysis under Memory Constraint. IEEE Transactions on Knowledge and Data Engineering, 35(9), 9502-9513.
  12. Zhu, Y., Deng, Q., Huang, D., Jing, B., Zhang, B. (2021). Clustering Based on Kolmogorov–Smirnov Statistic with Application to Bank Card Transaction Data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 70(3), 558-578.
  13. Zhu, Y., Huang, D., Gao, Y., Wu, R., Chen, Y., Zhang, B., Wang, H. (2021). Automatic, Dynamic, and Nearly Optimal Learning Rate Specification via Local Quadratic Approximation. Neural Networks, 141, 11-29.
  14. Wang, F., Zhu, Y., Huang, D., Qi, H., Wang, H. (2021). Distributed One-Step Upgraded Estimation for Non-Uniformly and Nonrandomly Distributed Data. Computational Statistics & Data Analysis, 162, 107265.
  15. Zhu, Y., Huang, D., Xu, W., Zhang, B. (2020). Link Prediction Combining Network Structure and Topic Distribution in Large-Scale Directed Network. Journal of Organizational Computing and Electronic Commerce, 30(2), 169-185.
  16. Guo, D., Zhu, Y., Yin, W. (2018). OSCAR: A Framework to Integrate Spatial Computing Ability and Data Aggregation for Emergency Management of Public Health. GeoInformatica, 22(2), 383-410.
  17. Li, G., Yang, X., Xu, W., Zhu, Y. (2017). Social Embeddedness and Customer-Generated Content: The Moderation Effect of Employee Participation. Journal of Electronic Commerce Research, 18(3), 245.
  18. Guo, D., Zhu, Y., Xu, W., Shang, S., Ding, Z. (2016). How to Find Appropriate Automobile Exhibition Halls: Towards a Personalized Recommendation Service for Auto Show. Neurocomputing, 213, 95-101.
  19. 朱映秋, 郑畅, 张波 (2025). 金融时间序列的自适应贝叶斯在线变点检测. 统计研究, 42(01), 145-160.
  20. 秦磊, 王寅智, 朱映秋, 谢邦昌 (2025). 已知组结构下混频时间序列的潜在因子分析. 系统工程理论与实践, 45(3): 1014-1028.
  21. 朱映秋, 黄丹阳, 张波 (2024). 基于高斯混合模型的分布因子聚类方法. 统计研究, 41(06), 147-160.
  22. 陈阳, 张晓梅, 朱映秋, 秦磊 (2024). 矩阵值时间序列自回归模型的稳健估计. 应用数学学报, 47(6): 999-1026.
  23. 黄丹阳, 罗伊琳, 朱映秋* (2023). 面向第三方支付平台非结构化大数据分布特征的融合聚类算法. 经济管理学刊, 2(3), 179-208.
  24. 黄丹阳,朱映秋,南金伶,王汉生 (2023).基于交易流水的信用卡套现交易及商户识别. 数理统计与管理, 42(01), 127-144.
  25. 朱映秋, 张波 (2021). 基于已实现波动率的上证综指异常时序检测. 系统工程理论与实践, 41(3), 625-635.
  26. 黄丹阳, 毕博洋, 朱映秋 (2021). 基于高斯谱聚类的风险商户聚类分析. 统计研究, 38(6), 145-160.
  27. 杨微石, 郭旦怀, 逯燕玲, 王德强, 朱映秋, 张宝秀 (2017). 基于大数据的文化遗产认知分析方法——以北京旧城中轴线为例. 地理科学进展, 9, 1111-1118.
Conference articles
  1. Zhu, Y., Guo, D., Wang, D., Li, J. (2016). How to find environmental risk factors of zoonotic infectious disease quickly. In Proceedings of the Second ACM SIGSPATIAL International Workshop on the Use of GIS in Emergency Management (SIGSPATIAL-EMGIS) (pp. 1-7).
  2. Guo, D., Zhou, Y., Zhu, Y., Li, J. (2016). Species Distribution Modeling via Spatial Bagging of Multiple Conditional Random Fields. In International Conference on Database Systems for Advanced Applications (DASFAA) (pp. 437-450).
  3. Li, G., Yang, X., Xu, W., Zhu, Y. (2016). Customer-generated content in company social media platform: How social network works? In 2016 IEEE International Conference on Management of Innovation and Technology (ICMIT) (pp. 188-192).
  4. Zhang, D., Xu, W., Zhu, Y., Zhang, X. (2015). Can sentiment analysis help mimic decision-making process of loan granting? A novel credit risk evaluation approach using GMKL model. In 2015 48th Hawaii International Conference on System Sciences (HICSS) (pp.949-958)
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