虞俊 - 北京理工大学 - 数学与统计学院

个人简介

本人2019年8月入职北京理工大学统计系担任助理教授。2019.12-2020.01访问佐治亚州立大学统计学系，合作导师为马平教授和钟文萱教授。在此之前, 我于2019年7月在北京大学数学科学学院统计学专业获得了博士学位, 导师为艾明要教授。教育背景 2014.09–2019.07北京大学，统计学博士，导师：艾明要教授 2010.09–2014.07南开大学，数学与应用数学学士 2010.09-2014.07 南开大学，金融学学士（双学位）工作经历 2019.09至今北京理工大学数学与统计学院，统计学系，助理教授 2019.12-2020.01 佐治亚州立大学统计学系，访问学者，导师：马平教授、钟文萱教授

研究领域

研究兴趣包括试验设计、受试验设计启发的各种数据分析技术以及应用统计解决现实的研究问题

近期论文

查看导师新发文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

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.