亓颢博，男。2023年毕业于北京大学光华管理学院，获经济学博士学位，现为北京师范大学统计学院数理统计系师资博士后。研究方向为统计优化、大规模数据统计分析、网络数据分析与深度学习中的统计理论。在 Journal of Computational and Graphical Statistics、Neurocomputing、Computational Statistics & Data Analysis 等期刊发表多篇论文。

一、教育背景和工作经历

2023.7 — 至今 北京师范大学统计学院，师资博士后（合作导师：郭旭教授）

2019.9 — 2023.7 北京大学光华管理学院，经济学博士（导师：王汉生教授）

2015.9 — 2019.7 北京师范大学统计学院，理学学士

二、联系方式：

工作单位：北京师范大学统计学院

通讯地址：北京市海淀区新街口外大街19号；邮编100875.

电子邮箱：haobo4869@bnu.edu.cn

三、学术成果

[1] Li, Y., Qi, H.*, and Wang, H. (2024). A Note about Why Deep Learning is Deep: A Discontinuous Approximation Perspective, Stat, 13(1), e654.

[2] Qi, H., Zhu, X.*, and Wang, H. (2024). A random projection method for large-scale community detection, Statistical and Its Interface, 17(2), 159-172.

[3] Qi, H., Wang, F.*, and Wang, H. (2023). Statistical Analysis of Fixed Mini-Batch Gradient Descent Estimator, Journal of Computational and Graphical Statistics, 32(4), 1348-1360.

[4] Qi, H., Cao, J., Chen, S., and Zhou, J.* (2023). Compressing Recurrent Neural Network Models through Principal Component Analysis, Statistical and Its Interface, 16(3), 397-407.

[5] Qi, H., Zhou, J.*, and Wang, H. (2022). A Note on Factor Normalization for Deep Neural Network Models, Scientific Reports, 12, 5909.

[6] Zhou, J., Qi, H.*, Chen, Y., and Wang, H. (2021). Progressive Principle Component Analysis for Compressing Deep Convolutional Neural Networks, Neurocomputing, 440, 197-206.

[7] Gao, Y., Zhu, X.*, Qi, H., Li, G., Zhang, R., and Wang, H. (2023). An Asymptotic Analysis of Random Partition Based Minibatch Momentum Methods for Linear Regression Models, Journal of Computational and Graphical Statistics, 32(3), 1083-1096.

[8] Wang, F., Zhu, Y., Huang, D.*, Qi, H., and Wang, H. (2021). Distributed One-step Upgraded Estimation for Non-uniformly and Non-randomly Distributed Data, Computational Statistics & Data Analysis, 162, 107265.

四、开设课程

本科生：《深度学习》、《数理统计（上机）》