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谌自奇

  • 个人资料
    • 部门: 经济与管理学部
    • 性别:
    • 专业技术职务: 研究员
    • 毕业院校: 东北师范大学
    • 学位: 博士
    • 学历: 博士研究生
    • 联系电话:
    • 电子邮箱: zqchen@fem.ecnu.edu.cn
    • 办公地址: 理科大楼A1508a
    • 通讯地址: 上海市普陀区中山北路3663号,华东师范大学统计学院
    • 邮编: 200062
    • 传真:

    工作经历

    2016.07-2019.08,MD Anderson Cancer Center (美国)生物统计系, 博士后(合作导师:朱宏图教授、胡建华教授、宁静教授)

    2012.09-2020.06, 中南大学,数学与统计学院,讲师、副教授

    2020.07-至今,华东师范大学,经济与管理学部统计学院,研究员、博士生导师




    教育经历

    2006.09-2012.07,硕博连读,东北师范大学数学与统计学院(导师:史宁中教授、高巍教授)



    个人简介

    研究方向包含生物统计、机器学习、深度学习、高维统计分析、函数型(纵向)数据分析、基于剖面似然的统计推断、生存分析等。主持国家自然科学基金面上项目2项、国家自然科学基金重点项目(子课题)1项、国家自然科学基金青年项目1项等JASA、BiometricsNeurIPS(人工智能顶会)AAAI(人工智能顶会)等期刊或者会议上发表论文20多篇。


    GoogleScholar: https://scholar.google.com/citations?user=b0q985EAAAAJ&hl=en 


    社会兼职

    研究方向

    生物统计、高维数据分析、机器学习、深度学习、剖面似然、函数型数据、生存分析

    招生与培养

    开授课程

    高等数理统计学、生物统计学

    科研项目

    [1] 国家自然科学基金面上项目, 11871477, 基于剖面似然的若干新统计推断方法研究, 2019.01-2022.12,主持。

    [2] 国家自然科学基金青年基金, 11401593, 基于剖面似然的统计推断,2015.01-2017.12,主持。

    [3]国家自然科学基金面上项目, 12271167, 高维图模型中的若干新问题研究。46万元,2023.012026.12,主持。

    [4]国家自然科学基金重点项目,72331005,大数据背景下不完全数据的统计分析方法、理论和应用,165万元,2024.012028.12,子课题负责人。

    [5]科技部, 国家重点研发计划数学与应用研究专项, 2021YFA1000100,油气管网安全运维的大数据理论、算法及应用, 2021-12  2026-11,1370万元, 研究骨干。

    [6]上海市科学技术委员会,“科技创新行动计划”基础研究领域应用数学重点项目,22JC1400800,大数据背景下航空安全管理中的关键数理问题研究, 2022-07  2025-06, 240万元, 研究骨干。


    学术成果

    代表性论文

    统计学论文:

    [1] Chen, Ziqi, Shen, Yu, Qin, Jing, Ning, Jing. Likelihood Adaptively Incorporated External Aggregate Information with Uncertainty for Survival Data. Biometrics, 80(4), ujae120, 2024. (SCI)

    [2] Zhu, Yayuan, Chen, Ziqi, Lawless, Jerald F., Semiparametric analysis of interval‐censored failure time data with outcome‐dependent observation schemes, Scandinavian Journal of Statistics, 49, 236-264, 2022. (SCI)

    [3] Chen, Ziqi, Ning, Jing, Shen, Yu, Qin, Jing. Combining Primary cohort data with external aggregate information without assuming comparability. Biometrics, 77, 1024-1036, 2021. (SCI)

    [4] Yan, Feifei, Xu, Qingsong, Tang, Man-Lai, Chen, Ziqi*, Kernel density‐based likelihood ratio tests for linear regression models, Statistics in Medicine, 40, 119-132, 2021. (SCI)

    [5] Chen, Ziqi, Hu, Jianhua, Zhu, Hongtu. Surface Functional Models. Journal of Multivariate Analysis.  180, 104664, 2020. (SCI)

    [6] Jiang, Binyan, Chen, Ziqi, Leng, Chenlei. Dynamic Linear Discriminant Analysis for High-dimensional Data. Bernoulli. 26, 1234-1268. 2020. (SCI)

    [7] Chen, Ziqi, Gao, Qibing, Fu, Bo, Zhu, Hongtu. Monotone Nonparametric Regression for Functional/Longitudinal Data. Statistica Sinica. 29, 2229-2249. 2019. (SCI)

    [8] Chen, Ziqi, Tang, Man-Lai, Gao, Wei, A profile likelihood approach for longitudinal data analysis, Biometrics, 74, 220–2282018 (SCI)

    [9] Chen, Ziqi, Leng, Chenlei, Dynamic covariance models. Journal of the American Statistical Association, 111, 1196-1207, 2016. (SCI)

    [10] Chen, Ziqi, Leng, Chenlei, Local linear estimation of covariance matrices via Cholesky decomposition, Statistica Sinica, 25, 1249-1263, 2015. (SCI)

    [11] Chen, Ziqi, Tang, Man-Lai, Gao, Wei, Shi, Ning-Zhong, New robust variable selection methods for linear regression models, Scandinavian Journal of Statistics, 41(3), 725-741, 2014. (SCI)

    [12] Chen, Ziqi, Shi, Ning-Zhong, Gao, Wei, Tang, Man-Lai, Efficient semiparametric mean-association estimation for longitudinal binary responses, Statistics in Medicine, 31(13), 1323- 1341, 2012. (SCI)

    [13] Chen, Ziqi, Shi, Ning-Zhong, Gao, Wei, Tang, Man-Lai, Efficient semiparametric estimation via Cholesky decomposition for longitudinal data, Computational Statistics and Data Analysis, 55(12), 3344-3354, 2011. (SCI)

    人工智能论文:

    [1] Shu, Hai, Shi, Ronghua, Jia, Qiran, Zhu, Hongtu, Chen, Ziqi*. mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural NetworksIJCNN 2022 (International Joint Conference on Neural Networks, CCF-C), Oral.

    [2] Dang, Yuchen, Chen, Ziqi, Li, Heng, Shu, Hai (2022).A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction. Applied Artificial Intelligence, 36, 2074129, 2022.(SCI)

    [3] Li Shuai, Chen, Ziqi*, Zhu Hongtu, Wang Dan, Wen Wang. Nearest-Neighbor Sampling Based Conditional Independence Testing. AAAI Conference on Artificial Intelligence 2023, AAAI 2023, CCF-A, Oral.

    [4] Li Shuai, Zhang Yingjie, Zhu Hongtu, Wang Dan, Shu Hai, Chen, Ziqi*, Sun Zhuoran, Yang Yanfeng, K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing. NeurIPS 2024, CCF-A.

    [5] Yang Yanfeng, Li Shuai, Zhang Yingjie, Sun Zhuoran, Shu Hai, Chen, Ziqi*, Conditional Diffusion Models Based Conditional Independence Testing. AAAI Conference on Artificial Intelligence 2025, AAAI 2025, CCF-A.


    荣誉及奖励

    2014年吉林省优秀博士学位论文