统计系 jiaminliu9@ustb.edu.cn
一、基本信息
姓名：刘佳敏
性别：女
职称：副教授
所在系所：统计系
所在梯队：统计与信息处理梯队
办公地点：理化楼209
办公电话：
电子邮件：jiaminliu9@ustb.edu.cn
本科生课程：统计模型与计算、概率论与数理统计
研究生课程：高等数理统计
研究领域：非参数统计，假设检验，分布式计算
二、教育经历
2013.09-2017.06 山西财经大学统计学院 经济统计学 学士
2017.09-2022.06 中国人民大学统计学院 统计学 博士
2020.09-2023.06 香港城市大学 数学系 联合培养博士
三、工作经历
2022.09-2026.07 北京科技大学 数理学院统计系 讲师
2022.09至今 北京科技大学 数理学院统计系 副教授
四、科研业绩
1. Liu, J., & Lian, H. (2026). Kernel-based Adaptive Huber Mean Regression with Heavy Tails and Contamination. Journal of the American Statistical Association, doi: 10.1080/01621459.2026.2696486.
2. Liu, J., Liu, X., Lian, H., & Xu, W. (2026). Fixed effects Bayesian testing in high-dimensional linear mixed models. Scandinavian Journal of Statistics, 53(1), 442–481.
3. Liu, J., & Lian, H. (2025). Sample efficient reinforcement learning via low-rank regularization. Knowledge-Based Systems, 327, 114176.
4. Liu, J., Wang, L., & Lian, H. (2025). Improved analysis of supervised learning in the RKHS with random features: Beyond least squares. Neural Networks, 184, Article 107091.
5. Liu, J., Gao, J., & Lian, H. (2025). Kernel-based regularized learning with random projections: Beyond least squares. SIAM Journal on Mathematics of Data Science, 7(1), 253–273.
6. Liu, J., & Lian, H. (2025). Kernel-based decentralized policy evaluation for reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, 36(6), 10371–10380.
7. Xu, W., Liu, J., & Lian, H. (2024). Distributed estimation of support vector machines for matrix data. IEEE Transactions on Neural Networks and Learning Systems, 35(5), 6643–6653.
8. Liu, J., Xu, W., Wang, Y., & Lian, H. (2023). Value iteration for streaming data on a continuous space with gradient method in an RKHS. Neural Networks, 166, 437–445.
9. Liu, J., Xu, W., Zhang, F., & Lian, H. (2023). Properties of standard and sketched kernel Fisher discriminant. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8), 10596–10602.
10. Lian, H., & Liu, J. (2023). Decentralized learning over a network with Nyström approximation using SGD. Applied and Computational Harmonic Analysis, 66, 373–387.
11. Liu, J., & Lian, H. (2023). On optimal learning with random features. IEEE Transactions on Neural Networks and Learning Systems, 34(11), 9536–9541.
12. Liu, J., Ma, S., Xu, W., & Zhu, L. (2022). A generalized Wilcoxon-Mann-Whitney type test for multivariate data through pairwise distance. Journal of Multivariate Analysis, 190, 104946.
