虞俊 (Jun Yu)
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助理教授 |
关于我
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。
我的研究兴趣主要包括: 大数据抽样，试验设计，数据缩减以及应用统计解决其他学科的实际问题 等。
如果你对我的研究感兴趣，欢迎与我联系。
教育经历
2014.09–2019.07 北京大学 统计学博士
2010.09–2014.07 南开大学 数学与应用数学学士、金融学学士（双学位）
研究项目
最优设计与最优子抽样问题研究
负责人; 2021.01-2023.12
自然科学基金青年科学基金项目, 项目号:12001042
教学工作
高等数理统计 （2021年春）主讲 [课程大纲]
高等数理统计 （2022年春）主讲 [课程大纲]
应用多元统计分析 模块II （2022年春）与孔祥顺博士、王典朋博士、王晋娟博士联合主讲
高等数理统计 （2023年春）主讲 [课程大纲]
贝叶斯数据分析：理论与方法 （2023年春）主讲 [课程大纲]
数理统计（MOOC） 与孔祥顺博士、田玉斌教授、王典朋博士、赵颖博士联合主讲 [课程链接]
注： 更多课程内容请访问北理工乐学网站（仅限校内），联合主讲课程授课教师按拼音排序。
学术论文
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.
学生培养
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2022届：
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应用统计专硕：
杨晓丹. 毕业论文：商业保险购买行为的影响因素分析
毕业去向：国研大数据研究院，大数据分析岗位
张杨杨. 毕业论文：基于图聚类的竞品分析——以汽车行业为例
毕业去向：京东零售，算法工程师
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欢迎感兴趣的同学加入！