兰州大学数学与统计学院

数学院李周平教授

研究方向
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极值统计、统计机器学习
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个人简历

        李周平，理学博士，兰州大学数学与统计学院教授、博士生导师，主要研究方向为极值统计、统计机器学习、复杂时空数据分析、贝叶斯推断及其在天气预报、电力现货市场预测、风险分析及化学等领域的应用。相关成果发表于Journal of American Statistical Association, Statistica Sinica, Journal of Hydrology, Journal of Chemometrics等国际权威期刊及人工智能顶会ICML。主持国家自然科学基金项目、中央高校基本科研费项目以及横向课题。获得兰州大学隆基教学新秀奖、华为奖教金。兼任中国现场统计研究会多元分析应用专委会副理事长、中国现场统计研究会贝叶斯统计分会副理事长、全国工业统计学教学研究会理事等。

教学及指导学生情况

教学情况：
        本科生：《概率论》、《数理统计》、《多元统计分析》等；
        研究生：《高等概率统计》等。

指导学生情况：
        已培养毕业硕士研究生60余名，毕业去向为：百度、网易、腾讯、顺丰科技、字节跳动、京东、华为、阿里、科大讯飞、中国移动、中国银行、上海银行、美团、华润集团、普华永道、中广核集团、统计局、学校以及读博深造。
        指导本科生：完成国家级、校级大学生创新项目多项，“莙政学者”项目一项；获得美国大学生数学建模竞赛M奖、全国大学生数学竞赛甘肃赛区数学A类一等奖；指导研究生多次获得数学建模竞赛奖项，包括：“华为杯”第二十届中国研究生数学建模竞赛全国一等奖，第六届全国应用统计专业学位研究生案例大赛二、三等奖，第七届全国应用统计专业学位研究生案例大赛一等奖，第十届全国大学生统计建模大赛国家级二等奖，等。

发表论文及专著

部分论文：
[1]Qin, Z. and Li, Z. (2026). Forest-based Conformal Prediction Approach for Spatial Data. Statistics and Computing, 36, 219.
[2]Dong, C., Lin, Y., Wang, W. and Li, Z. (2026). Decoding Property Synergies in Nanocomposite Ultrafiltration Membranes via Interpretable Machine Learning.  Journal of Environmental Chemical Engineering, 14(5), 124809.
[3]Qin, M. and Li, Z. (2026). F-KAN: A Novel Framework for Functional Data Based on Kolmogorov-Arnold Networks. Computational Statistics, 41, 114.
[4]Liu, Q. and Li, Z. (2026). Differentially Private Distributed Inference Based on U-Statistics for Massive Data. Stat, 15(3), e70170.
[5]Guo, J. and Li, Z. (2026). SigFPLS: A Signatures-Based Approach for Scalar-on-Function Regression Model. Australian & New Zealand Journal of Statistics, 68 (3), e70061.
[6]Xu, J. and Li, Z. (2026). Split Empirical Likelihood via Universal Inference for Bounded Means. Statistics and Probability Letters, 238, 110877.
[7]Xu, J. and Li, Z. (2026). LORD-GoF: A Robust Online Detection Approach for LLM Watermarks in Sparse and Mixed Streams. Forty-third International Conference on Machine Learning, ICML 2026.
[8]Wu, J., Qin, M. and Li, Z. (2026). TS-K-means: An Improved Test-Based Functional Data Clustering Approach Using Shrinkage Estimation. Journal of Statistical Computation and Simulation,96 (14), 3513-3528.
[9]Hao, S., Li, Z. and Jing, B.-Y. (2026). A multi-label classification approach for functional data based on conformal prediction bands. The Canadian Journal of Statistics, 54 (3), e70053.
[10]Liu, Q. and Li, Z. (2025). Distributed empirical likelihood inference with privacy guarantees. Statistics and Computing, 35, 88.
[11]Bao, S., Guo, J. and Li, Z. (2024). SFMD-X: A New Functional Data Classifier Based on Shrinkage Functional Mahalanobis Distance. Journal of Chemometrics, 38, e3615.
[12]Luo, Y., Wei, Y., Li, Z. and Jing, B.-Y. (2024). Incorporating relative error criterion to conformal prediction for positive data. Communications in Mathematics and Statistics, 12, 157-186.
[13]He, S., Li, Z. and Liu, X. (2023). An improved GEV boosting method for imbalanced data classification with application to short-term rainfall prediction. Journal of Hydrology, 617, 128882. 
[14]Li, Z., Xu, J., Zhao, N. and Zhou, W. (2023). Penalized jackknife empirical likelihood in high dimensions. Statistica Sinica, 33, 1219-1232. 
[15]Liu, Q. and Li, Z. (2023). Distributed estimation via empirical likelihood. The Canadian Journal of Statistics, 51, 375-399. 
[16]Wei, Y., Li, Z. and Dai, Y. (2022). Unified smoothed jackknife empirical likelihood tests for comparing income inequality indices. Statistical Papers, 63, 1415-1475. 
[17]Tao, Z. and Li, Z. (2022). Adaptive singular value shrinkage estimate for low rank tensor denoising. Random Matrices: Theory and Applications,11 (04), 2250038.
[18]Ji, Z., Wei, Y. and Li, Z. (2020). SURE estimates for high dimensional classification. Statistical Analysis and Data Mining,13 (5), 423-436. 
[19]Jing, B.-Y., Li, Z., Pan, G. and Zhou, W. (2016). On SURE-type double shrinkage estimation. Journal of the American Statistical Association, 111 (516), 1696-1704. 
[20]Li, Z., Xu, J. and Zhou, W. (2016).On Nonsmooth Estimating Functions Via Jackknife Empirical Likelihood. Scandinavian Journal of Statistics, 43, 49-69.
[21]Li, Z., Lin, Y., Zhou, G. and Zhou, W. (2014). Empirical likelihood based on least absolute relative error estimation.TEST, 23, 86-99.
[22]Jing, B.-Y., Li, Z., Qin, J. and Zhou, W. (2012). Jackknife empirical likelihood method for case-control studies with gene environment independence on controls. Statistics and Its Interface, 5, 293-302.
[23]Li, Z. and Peng, L. (2012). Bootstrapping endpoint. Sankhya Series A, 74, 126-140.
[24]Li, Z., Gong, Y. and Peng, L. (2011). Empirical likelihood intervals for conditional Value-at-Risk in heteroscedastic regression models. Scandinavian Journal of Statistics, 38, 781-787.
[25]Gong, Y., Li, Z. and Peng, L. (2010). Empirical likelihood intervals for conditional Value-at-Risk in ARCH/GARCH models. Journal of Time Series Analysis, 31, 65-75.
[26]Li, Z., Gong, Y. and Peng, L. (2010). Empirical likelihood methods for intermediate quantiles. Statistics and Probability Letters,80, 1022-1029.

项目成果

荣誉，获奖

        兰州大学隆基教学新秀奖，兰州大学毕业论文（设计）优秀指导教师，兰州大学学生创新创业行动计划优秀指导教师，第六届全国应用统计专业学位研究生教育教学成果奖二、三等奖，第七届全国应用统计专业学位研究生教育教学成果奖一等奖。

社会工作

其他信息

社会工作：        
        中国现场统计研究会多元分析应用专业委员会副理事长、中国现场统计研究会贝叶斯统计分会副理事长、中国青年统计学家协会常务理事，全国工业统计学教学研究会理事，甘肃省统计学会常务理事，中国商业统计学会理事，中国现场统计研究会数据科学与人工智能分会理事等;  并担任多个统计与数据科学领域国内外期刊审稿人。