个人资料
- 部门： 统计学院
- 性别： 男
- 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：
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
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 Statistics, 17(2), 1521-1542. [6]. Zhang, S., Chen, Y. (2022). Computation for Latent Variable Model Estimation: A Unified Stochastic Proximal Framework. Psychometrika, 87(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）
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