Jun Yu (虞俊)
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Associate professor, |
About me
I am currently an associate professor at Beijing Insititute of Technology. I served as a visiting scholar at the department of statistics of the University of Georgia during 2019.12—2020.12. I was superivsed by professors Ping ma and Wenxuan Zhong. Before that, I received my Ph.D. degree in the School of Mathematics Science from Peking University in 2019. My advisor is professor Mingyao Ai.I obtained my B.S. degree in July 2014 from Nankai University.
Research Interests: sampling on massive data, design of experiments，data reduction and applied statistics.
Educations
2014.09–2019.07 Peking University Ph.D. in Statistics
2010.09–2014.07 Nankai University B.S. in Applied Mathematics、and B.E. in Finance
Grants
A study on optimal design and optimal subsampling method
PI; 2021.01-2023.12
NSFC (Young scholar program), No.12001042
Teaching
Advanced Theory of Statistics (2021 Spring) [Syllabus]
Advanced Theory of Statistics (2022 Spring) [Syllabus]
Applied Multivariate Analysis Moduel II （2022 Spring） This course is joint supervised together with Kong, Xiangshun; Wang, Dianpeng; and Wang, Jinjuan.
Advanced Theory of Statistics (2023 Spring) [Syllabus]
Bayesian data analysis: theory and examples (2023 Spring) [Syllabus]
Advanced Theory of Statistics (2024 Spring) [Syllabus]
Bayesian data analysis: theory and examples (2024 Spring) [Syllabus]
Advanced Theory of Statistics (2025 Spring) [Syllabus]
Bayesian data analysis: theory and examples (2025 Spring) [Syllabus]
Data analysis for applied statistics (2025 Spring)
Mathematics Statistics（MOOC） This course is built up together with Kong, Xiangshun; Tian, Yubin; Wang, Dianpeng; Zhao, Ying. [Course Link]
Note： For more courses information, please visit Lexue(BIT)，the instructors are alphabetic ordered.
Publications
Deng, Jiayi, Xiaodong Yang, Jun Yu, Jun Liu, Zhaiming Shen, Danyang Huang, and Huimin Cheng. Network Tight Community Detection., International Conference on Machine Learning (ICML 2024),2024.
Li, Tao, Cheng Meng, Hongteng Xu, and Jun Yu. Hilbert curve projection distance for distribution comparison., IEEE Transactions on Pattern Analysis and Machine Intelligence,2024. 46(7): 4993-5007.
Li, Mengyu, Jun Yu, Tao Li, and Cheng Meng. Importance Sparsification for Sinkhorn Algorithm., Journal of Machine Learning Research,2023. 24: 1-44.
Lv, Shurui, Jun Yu, Yan Wang, and Jiang Du. Fast calibration for computer models with massive physical observations., SIAM/ASA Journal on Uncertainty Quantification,2023. 11: 1069-1104.
Ye, Zhiqiang, Jun Yu, and Mingyao Ai. Optimal subsampling for multinomial logistic models with big data., Statistica Sinica,2023. DOI: 10.5705/ss.202022.0277.
Han, Yixin, Jun Yu, Nan Zhang, Cheng Meng, Ping Ma, Wenxuan Zhong, and Changliang Zou. Leverage classifier: another look at support vector machine., Statistica Sinica,2023. DOI: 10.5705/ss.202023.0124.
Diao, Huaimin,Mengtong Ai, Yubin Tian, and Jun Yu. Efficient basis selection for smoothing splines via rotated lattices., STAT,2023. 12:e581.
Miao, Zhuoyi, and Jun Yu. A Robust Learning Framework for Smart Grids in Defense of False Data Injection Attacks., ACM Transactions on Sensor Networks,2023. DOI: 10.5705/ss.202022.0277.
Yu, Jun, Jiaqi Liu, and Haiying Wang. Information-based optimal subdata selection for non-linear models., Statistical Papers,2023. 64:1069–1093.
Yu,Jun, Mingyao Ai, and Zhiqiang Ye. A review on design inspired subsampling for big data, Statistical Papers,2023.
Ai, Mingyao, Zhiqiang Ye, and Jun Yu. Locally D-optimal designs for hierarchical response experiments. Statistica Sinica, 2023.33:381-399
Li, Mengyu, Jun Yu, Hongteng Xu, and Cheng Meng. Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification, Journal of Computational and Graphical Statistics,2023.
Li, Tao, Jun Yu, and Cheng Meng. Scalable model-free feature screening via sliced-Wasserstein dependency, Journal of Computational and Graphical Statistics,2023.
Yu,Jun, Xiran Meng, and Yaping Wang. Optimal designs for semi-parametric dose-response models under random contamination, Computational Statistics & Data Analysis,2022.
Yu, Jun, and HaiYing Wang. Subdata selection algorithm for linear model discrimination. Statistical Papers, 2022.
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, 2022. 117(537): 265-276
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, 2022, 9(15):13862-13875.
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.
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.
Ai, Mingyao, Jun Yu, Huiming Zhang, and HaiYing Wang. Optimal subsampling algorithms for big data regressions. Statistica Sinica, 2021. 31:749-772.
Ai, Mingyao, Fei Wang, Jun Yu, and Huiming Zhang. Optimal subsampling for large-scale quantile regression. Journal of Complexity, 2021. 62:101512.
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, 2021. 211:402-413.
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, 2021. 125(34):9660-9667.
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), 2021. pp. 13-18.
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, 2020. 33:4015-4028.
Ai, Mingyao, Yimin Huang, and Jun Yu. Data-Based Priors for Bayesian Model Averaging. In Contemporary Experimental Design, Multivariate Analysis and Data Mining, Springer, Cham.2020. pp. 357-372.
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.2018. 127: 217-228.
Yu, Jun, Mingyao Ai, and Yaping Wang. Optimal designs for linear models with Fredholm-type errors. Journal of Statistical Planning and Inference.2018. 194:65-74.
Graduate Students Supervision
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2022：
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Master Students in Applied Statistics：
Yang, Xiaodan. Thesis：Analysis on the Influencing Factors of Commercial Insurance Purchasing Behavior
Zhang, Yangyang. Thesis：Graph Clustering for Competitive Product Analysis –-Based on the Auto Industry
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2023：
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Master Students in Applied Statistics：
Cao, Haozhe. Thesis：Elastic Pricing Strategy Based on Causal Machine Learning
Lei, Kaifeng. Thesis：Research on Prediction Model of ICU Patients’ Mortality Based on Integration of Feature Selection
Li, Hengli. Thesis：Recommendation System Based on Knowledge Tag
Liu, Zhengfu. Thesis：Design of Sensitivity Experiments with Active and Inactive Factors
Liu, ZHihan. Thesis：Improved A/B testing based on e-value
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2024：
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Master Students in Applied Statistics：
Peng, Hanying. Thesis：A fast community detection method based on subsampling
Yao, Yunbao. Thesis：Optimal Designs for Multi-Index Regression Models
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Welcome to join our research group.