姓 名：胡祺睿
职 称：助理教授
研究方向：函数型数据，非参数统计，时间序列，变点分析，差分隐私
教授课程：《统计学》，《机器学习》，《统计理论与方法》
E - mail：huqirui@mail.shufe.edu.cn
电话：
研究领域
我目前的研究领域是复杂数据类型的统计推断理论，同时面向数据驱动的交叉应用。具体而言，一般是以下一个或几个方向的融合：
对函数型数据的基本成分（如均值、协方差、函数型主成分等）及基于函数型数据构建的模型，进行相应的统计推断（在 $L^2$ 或 $L^\infty$ 度量下）。现阶段关注结合非参数方法与高斯强逼近技术，建立从稀疏到稠密取样情形的一致推断理论，例如构造同时置信带、检验平稳性、检验可分性等。
时间序列与空间序列的预测、变点分析及非精确检验。当前关注函数型时间序列（平稳/非平稳）的变点检验与检测问题，例如检验突变、缓变、不规则跳变，以及序贯变点检验等。
复杂区域上的非参数问题：在特殊区域（二维/三维不规则域、球面等）上的函数型数据与非参数回归；二维图像与三维点云数据的应用；以及一般流形上的统计推断。现阶段希望在二元、三元、球面样条等方面发展更多统计理论性质。
中心/局部差分隐私框架下非线性统计量的推断问题，例如构建置信区间或同时置信序列。当前目标是在高度异质性场景（分布式、联邦学习、去中心化学习等）中，为常见非线性统计量设计合适的差分隐私机制，并给出可实施的推断程序。
此外，许多学识渊博、友善的同行带我认识了统计学其他丰富而有趣的子领域；在这些方向上我仍处于起步阶段。若您对我的研究感兴趣，或认为我的研究可用于您的工作，亦或愿意提供有趣的问题让我入门学习并参与合作，欢迎通过电子邮件联系我。我欢迎任何形式（理论、应用）的合作。
如果您希望在我指导下与我一起学习或开展科研：理论方向希望您至少熟练掌握概率论、随机过程等理论（其他数学知识多多益善）；应用方向希望您至少熟练掌握一门编程语言（R 或 C++），并最好具备数据收集与数据清洗的经验。
教育经历
2016.9-2020.6 北京师范大学，统计学院， 统计学，学士
2020.9-2025.6 清华大学，统计与数据科学系，统计学，博士
工作经历
2025.7-至今 上海财经大学，统计与数据科学学院，数理统计系，助理教授
研究成果
⋆indicates corresponding author, (α,β) indicates contributed equally
Publication (Updated on 2025.9.1):
Peer-reviewed journal:
[1] Hu, Q. and Yang, L. (2025+). Statistical inference for functional data over multi-dimensional domain. Statistica Sinica, DOI: 10.5705/ss.202024.0344.
[2] Cai, L. and Hu, Q.⋆(2025+). Simultaneous inference for the distribution of FPC scores of functional data. Statistica Sinica, DOI: 10.5705/ss.202023.0246.
[3] Hu, Q. (2025). Testing relevant hypotheses in functional variance function via self-normalization. Scandinavian Journal of Statistics, 52(3), 1301–1329.
[4] Hu, Q. and Li, J. (2025). Simultaneous inference for covariance function of next-generation functional data. Electronic Journal of Statistics, 19 (2) 3188 - 3232.
[5] Cai, L. and Hu, Q.⋆(2025). From sparse to dense functional data: phase transitions from a simultaneous inference perspective. Statistics and Computing, 35, 146.
[6] Cai, L. and Hu, Q.⋆(2025). Global inference and test of eigen-systems of image data over complicated domain. Journal of Computational and Graphical Statistics, 34(2), 729–745.
[7] Hu, Q. (2024). Change point test and detection of functional variance function with stationary error. Journal of Multivariate Analysis, 202, 105311. IMS Hannan Graduate Student Travel Award 2024.
[8] Cai, L. and Hu, Q.⋆(2024). Simultaneous inference and uniform test for eigen[1]systems of functional data. Computational Statistics and Data Analysis,192, 107900.
[9] Hu, Q. and Li, J. (2024). Statistical inference for mean function of longitudinal imaging data over complicated domains. Statistica Sinica, 34, 955-982.
[10] Tian, Z., Wu, P., Yang, Z., Cai, D., and Hu, Q.⋆(2023). Robust nonparametric estimation of average treatment effects: a propensity score-based varying coefficient approach. Stat 12, e637.
[11] Li, J.,Hu, Q.⋆ and Zhang, F.(2022). Multi-step-ahead prediction interval for locally stationary time series with application to air pollutants concentration data. Stat, 11, e411. ISI Jan Tinbergen Awards 2021.
[12] Xu, N., Wu, P., Ma, G., Hu, Q., Hu, X., Wu, R., Wang, Y. Xu, H., Chen, L. and Zhang, P. (2022). In-flight spectral response function retrieval of a multi-spectral radiometer based on the functional data analysis technique. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-10.
[13] Wu, P., Hu, Q., Tong, X. and Wu, M.(2020). Learning causal effect using machine learning with application to china’s typhoon. Acta Mathematicae Applicatae Sinica, English Series, 36, 702-713.
Peer-reviewed conference:
1] Liu,Y., Hu, Q., Ding, L and Kong, L (2023). Online local differential private quantile inference via self-normalization. Proceedings of the 40th International Conference on Machine Learning (ICML 2023).
[2] Liu, Y.(α,β), Hu, Q.(α,β) and Kong, L. (2024). Tuning-free estimation and inference of cumulative distribution function under local differential privacy. Proceedings of the 41th International Conference on Machine Learning (ICML 2024).
[3] Ding, L., Hu, Y., Denier, N., Shi, E., Zhang, J., Hu, Q., Hughes, D., Kong, L. and Jiang, B. (2024). Probing social bias in labor market text generation by chatgpt: a masked language model approach. Advances in Neural Information Processing Systems (NeurIPS 2024).
Manuscripts under review or revision:
[1] Cai, L. and Hu, Q.⋆(2025+). Simultaneous inference for long-run covariance function of functional time series. Computational Statistics and Data Analysis. Revision Invited.
[2] Sun, S., Cai, L. and Hu, Q.⋆(2025+). Statistical inference for mean function of partially observed functional time series. Biometrics. Revision Invited.
[3] Bai, L.(α,β), Hu, Q.(α,β) and Wu, W. (2025+). Inference for structural changes in nonstationary functional time series with partial measurement error. Submitted.
[4] Cai, L. and Hu, Q.⋆(2025+). From sparse to dense functional time series: phase transitions of detecting structural breaks and beyond. Submitted. arxiv.org/abs/2412.20858
[5] Cai, L. and Hu, Q.⋆(2025+). Unified theory of testing relevant hypothesis in functional time series. Submitted. arxiv.org/abs/2508.18624
[6] Cai, L., Hu, Q.⋆, Sun, J. and Wu, S. (2025+). Time-uniform and asymptotic confidence sequence of quantile under local differential privacy. Submitted.
[7] Cai, L., Hu, Q.⋆ and Wu, S. (2025+). Privacy-aware data integration for enhanced quantile inference. Submitted.
[8] Cai, L., Hu, Q.⋆ and Wu, S. (2025+). Federated learning of quantile inference under local differential privacy. Submitted.
[9] Cai, L, Sun, S. and Hu, Q.⋆(2025+). Simultaneous inference for partially observed functional data. Submitted.
[10] Hu, Q. and Liu, Y. (2025+). Censoring with plausible deniability: asymmetric local privacy for multi-category CDF estimation. Submitted.
[11] Liu, Y.(α,β), Hu, Q.(α,β) and Kong, L. (2025+). Advancing quantile estimation under local differential privacy: non-asymptotic bounds and algorithmic
performance. Submitted.
[12] Xu, M., Cai, L. and Hu, Q.⋆(2025+). Uniform inference for principal components of functional time series with applications to financial data. Submitted.
Pantent:
[1] Simultaneous confidence surface acquisition and systems for spatial regions, Li,J., Hu, Q. and Yang, L. (2024,Set,17). CN113934980B.
奖励、荣誉
IMS Hannan Graduate Student Travel Award, April, 2024.
ISI Jan Tinbergen Awards, Division A - First Prize, May 2021
社会工作
匿名审稿人，包括Annals of statistics(2), Statistica Sinica(3) ，Stochastic Environmental Research and Risk Assessment(1)，Stat(1)等期刊，ICML(2025),NeurIPS(2024,2025),ICLR(2025), AISTATS(2025,2026)等会议。
