邹长亮 - 南开大学 - 统计与数据科学学院

个人简介

邹长亮，1981年生人，教授、博士生导师。致力于统计学理论及其与大数据领域的交叉研究和实际应用。教育经历分别于2003、2006、2008年在南开大学数学学院获得本科、硕士、博士学位。工作经历 2009年入职南开大学，2011和2013年晋升副教授和教授。教学工作讲授《高等统计I、II》、《统计大样本理论》、《统计计算》、《非参数统计》、《数据科学导论》等课程部分主持的项目： 1. 国家自然科学基金委重大项目课题, 超高维数据的统计学习与推断, 2017-01至2021-12 2. 国家自然科学基金委杰出青年科学基金项目, 大规模数据统计推断, 2020-01至2024-12 3. 国家自然科学基金委优秀青年科学基金项目, 统计过程监控与诊断, 2017-01至2019-12 4. 国家自然科学基金委青年科学基金项目, 应用于复杂数据的统计监控和诊断, 2011-01至2013-12 5. 教育部全国优秀博士学位论文作者专项, 2012-01至2016-12

研究领域

Change-Point and Outlier Detection; On-Line Learning for Streaming Data; Massive Data Analysis; High-Dimensional Inference;Statistical Process Control

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）

1.Chen, H., Ren, H., Yao, F. and Zou, C. “Data-driven selection of the number of change-points via error rate control”, JASA, to appear 2.Du, L., Guo, X., Sun, W. and Zou, C. “False Discovery Rate Control Under General Dependence by Symmetrized Data Aggregation”, JASA, Available online 3.Ren, H., Zou,C., Chen, N. and Li, R. “Large-Scale Datastreams Surveillance via Pattern-Oriented-Sampling”, JASA, Available online 4.Zou, C., Ke, Y., and Zhang, W. “Estimation of Low Rank High Dimensional Multivariate Linear Models for Multi-response Data”, JASA, Available online 5.Zou, C., Wang, G., and Li, R. “Consistent Selection of the Number of Change-Points via Sample-Splitting”, Annals of Statistics, 48, 413-439, 2020 6.Qian,C., Quoc,T-D., Fu,S., Zou,C. and Liu,Y. “Robust Multicategory Support Matrix Machine”, Mathematical Programming, 176, 429-463, 2019 7.Wang, G., Zou, C. and Yin, G. “Change-point detection in multinomial data with a large number of categories” Annals of Statistics, 46, 2020-2044, 2018 8.Ren, H., Chen, N. and Zou, C. “Projection-based outlier detection in functional data”, Biometrika, 104, 411-423, 2017 9.Feng, L., Zou, C., and Wang, Z. “Multivariate-sign-based high-dimensional tests for the two-sample location problem” JASA, 111, 721-735, 2016 10.Paynabar, K., Zou, C., and Qiu, P. “A Change Point Approach for Phase-I Analysis in Multivariate Profile Monitoring and Diagnosis”, Technometrics, 58, 191-204, 2016 (ESI Highly Cited)