Yu Jun Job title: Assistant Professor E-mail: yujunbeta@bit.edu.cn; yujunbeta@163.com
Research interests include experimental design, various data analysis techniques inspired by experimental design, and the application of statistics to solve real-world research problems. If you are interested in my research direction, please contact me. For more details, please visit: https://junyubeta.github.io/yujunbeta.github.io/index.html < br > More

Experimental design, sampling theory and techniques, data reduction and applied statistics.

2014.09 -- 2019.07 Peking University, Doctor of Statistics, Supervisor: Professor Ai Ming Yao
2010.09 -- 2014.07 Nankai University, Bachelor of Mathematics and Applied Mathematics
2010.09-2014.07 Nankai University, Bachelor of Finance (double degree)

2019.09-present Assistant Professor, Department of Statistics, School of Mathematics and Statistics, Beijing Institute of Technology
2019.12-2020.01 Visiting Scholar, Department of Statistics, Georgia State University, Tutors: Professor Ma Ping, Professor Zhong Wenxuan

1. Jun Yu, Xiran Meng, Yaping Wang, Optimal designs for semi-parametric dose-response models under random contamination,Computational Statistics & Data Analysis,2022,

2. Yu, Jun, and HaiYing Wang. "Subdata selection algorithm for linear model discrimination." Statistical Papers (2022): 1-24.

3. Yu, Jun, Huimin Cheng, Jinan Zhang, Qi Li, Shushan Wu, Wenxuan Zhong, Jin Ye, Wenzhan Song, and Ping Ma. "CONGO²: Scalable Online Anomaly Detection and Localization in Power Electronics Networks." IEEE internet of things journal 9, no. 15 (2022): 13862-13875.

4. Zhang, Jingyi, Cheng Meng, Jun Yu, Mengrui Zhang, Wenxuan Zhong, and Ping Ma. "An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation." Journal of Computational and Graphical Statistics (2022): 1-26.

5. Meng, Cheng, Jun Yu, Yongkai Chen, Wenxuan Zhong, and Ping Ma. "Smoothing splines approximation using Hilbert curve basis selection." Journal of Computational and Graphical Statistics (2022): 1-11.

6. Ai, Mingyao, Jun Yu, Huiming Zhang, and HaiYing Wang. "Optimal subsampling algorithms for big data regressions." Statistica Sinica, 31 （2021）:749-772.

7. Ai, Mingyao, Fei Wang, Jun Yu, and Huiming Zhang. "Optimal subsampling for large-scale quantile regression." Journal of Complexity 62 (2021): 101512.

8. Ai, Mingyao, Yimin Huang, and Jun Yu. "A non-parametric solution to the multi-armed bandit problem with covariates." Journal of Statistical Planning and Inference 211 (2021): 402-413.

9. Cheng, Huimin, Jun Yu, Zhen Wang, Ping Ma, Cunlan Guo, Bin Wang, Wenxuan Zhong, and Bingqian Xu. "Details of Single-Molecule Force Spectroscopy Data Decoded by a Network-Based Automatic Clustering Algorithm." The Journal of Physical Chemistry B 125, no. 34 (2021): 9660-9667.

10. Wang, Sili, Shengjie Min, Jun Yu, Huimin Cheng, Zion Tse, and Wenzhan Song. "Contact-less Home Activity Tracking System with Floor Seismic Sensor Network." In 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), pp. 13-18. IEEE, 2021.

11. Yu, Jun, HaiYing Wang, Mingyao Ai, and Huiming Zhang. "Optimal distributed subsampling for maximum quasi-likelihood estimators with massive data." Journal of the American Statistical Association 117, no. 537 (2022): 265-276.

12. Meng, Cheng, Jun Yu, Jingyi Zhang, Ping Ma, and Wenxuan Zhong. "Sufficient dimension reduction for classification using principal optimal transport direction." Advances in Neural Information Processing Systems 33 (2020): 4015-4028.

13. Ai, Mingyao, Yimin Huang, and Jun Yu. "Data-Based Priors for Bayesian Model Averaging." In Contemporary Experimental Design, Multivariate Analysis and Data Mining, pp. 357-372. Springer, Cham, 2020.

14. Yu, Jun, Xiangshun Kong, Mingyao Ai, and Kwok Leung Tsui. "Optimal designs for dose–response models with linear effects of covariates." Computational Statistics & Data Analysis 127 (2018): 217-228.

15. Yu, Jun, Mingyao Ai, and Yaping Wang. "Optimal designs for linear models with Fredholm-type errors." Journal of Statistical Planning and Inference 194 (2018): 65-74.