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张思亮

  • 个人资料
    • 部门: 统计学院
    • 性别:
    • 专业技术职务: 助理教授
    • 毕业院校: 复旦大学
    • 学位: 博士
    • 学历: 博士
    • 联系电话:
    • 电子邮箱: slzhang@fem.ecnu.edu.cn
    • 办公地址: 中北校区理科大楼A1508b
    • 通讯地址: 上海市普陀区中山北路3663号
    • 邮编:
    • 传真:

    工作经历

    2021.10至今,华东师范大学统计学院,助理教授

    2019.7-2021.7,伦敦政治经济学院统计系,Research Officer,合作导师:Prof. Fiona Steele, Prof. Jouni Kuha

    教育经历

    2014.09-2019.06,复旦大学,上海数学中心,理学博士(概率论与数理统计),导师:应志良教授

    2016.08-2018.08,美国哥伦比亚大学,统计系,联合培养


    个人简介

    华东师范大学统计学院助理教授,英国伦敦政治经济学院(LSE)统计系博士后,复旦大学上海数学中心和美国哥伦比亚大学统计系联合培养博士。


    主要研究方向为 潜变量建模与统计计算,病例对照关联研究统计机器学习。


    社会兼职

    Consulting Editor of: 

        British Journal of Mathematical and Statistical Psychology


    Reviewer of: 

        Psychometrika, Statistics and Computing, British Journal of Mathematical and Statistical Psychology, 

        Structural Equation Modeling: A Multidisciplinary Journal, Journal of Computational and Graphical Statistics, 

        Journal of Educational and Behavioral Statistics, etc.

    研究方向

    Large-Scale Item Response Theory

    Latent Variable Modeling and Statistical Computing

    Statistical Machine Learning


    招生与培养

    开授课程

    凸优化

    结构方程模型

    最优化方法

    科研项目

    2023年国家自然科学基金青年项目 - “多元调查数据中统计关联模式的潜变量与图建模研究”

    2022年上海市“科技创新行动计划” 启明星培育(扬帆专项)


    学术成果

    Accepted/published:

    [11]. Zhang, S., Chen, Y. (2024+). A Note on Ising Network Analysis with Missing Data.  Psychometrika. To appear.

    [10]. Zhang, S., Kuha, J., Steele, F. (2024+). Modelling Correlation Matrices in Multivariate Data, with Application to Reciprocity and Complementarity of Child-Parent Exchanges of Support.  Annals of Applied Statistics. To appear.

    [9]. Zhang, S., Chen, J., Ying, Z., Zhang, H. (2024+). Adjusting for non-confounding covariates in case-control association studies.   Statistica Sinica. To appear.

    [8]. Steele, F., Zhang, S.*, Grundy, E., & Burchardt, T. (2023). Longitudinal analysis of exchanges of support between parents and children in the UK. Journal of the Royal Statistical Society Series A: Statistics in Society, 187(2), 279-304.

    [7]. Kuha, J., Zhang, S.*, & Steele, F. (2023). Latent Variable Models for Multivariate Dyadic Data with Zero Inflation: Analysis of Intergenerational Exchanges of Family Support. Annals of Applied Statistics17(2), 1521-1542.

    [6]. Zhang, S., Chen, Y. (2022). Computation for Latent Variable Model Estimation: A Unified Stochastic Proximal Framework. Psychometrika87(4), 1473-1502.

    [5]. Chen, Y., Moustaki, I., & Zhang, S. (2022). On the Estimation of structural Equation Models with Latent Variables. Handbook of Structural Equation Modeling, 145.

    [4]. Zhang, S., Chen, Y., & Liu, Y. (2020). An Improved Stochastic EM Algorithm for Large-Scale Full-Information Item Factor Analysis. British Journal of Mathematical and Statistical Psychology, 73, 44–71, lvmcomp R package.

    [3]. Chen, Y., Li, X., & Zhang, S. (2019). Structured Latent Factor Analysis for Large- scale Data: Identifiability, Estimability, and Their Implications. Journal of the American Statistical Association, 1–15, mirtjml R package.

    [2]. Chen, Y., & Zhang, S. (2020). A Latent Gaussian process model for analysing intensive longitudinal data. British Journal of Mathematical and Statistical Psychology, 73, 237–260.

    [1]. Chen, Y., Li, X., & Zhang, S. (2019). Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis. Psychometrika, 84, 124–146.

    荣誉及奖励

    2019 复旦大学优秀毕业生

    2020 复旦大学优秀博士学位论文

    2021 LSE Class Teacher Award

    2023 Psychometrika Best Reviewer Award

    上海市领军人才(青年海外)