卢芳，女，博士，讲师

教育背景：

2007.09-2011.07   周口师范学院   数学与应用数学系 学士

2011.09-2016.07   重庆大学44118太阳成城集团 运筹学与控制论专业 硕博连读

工作经历：

2017.01-至今   44118太阳成城集团 数学系 讲师

教学育人：

本科课程：运筹学、高等数学

科学研究：

研究领域：最优性理论与方法、KKT条件、随机优化

科研项目：

1. 国家自然科学基金青年项目，11801169，随机向量变分不等式问题的解及其收敛性分析，19万，2019.01-2021.12，主持

2. 湖南省自然科学基金青年项目，2019JJ50378，随机向量变分不等式问题的解及其收敛性分析，5万，2019.01-2021.12，主持

代表性论文（*为通讯作者）：

1. Yang Jing, Tian Guoliang, Lu Fang* and Lu Xuewen (2020). Single-index modal regression via outer product gradients. Computational Statistics & Data Analysis, 144: 106867.

2. Yang Jing, Lu Fang* and Lu Xuewen (2020). Robust check loss-based inference of semiparametric models and its application in environmental data. Journal of Computational and Applied Mathematics, 365, 112267

3. Yang Jing*, Lu Fang and Yang Hu (2019). Local Walsh-average based estimation and variable selection for the single-index models. Science China Mathematics, 62, 1977-1996.

4. Yang Jing*, Lu Fang, Tian Guoliang, Lu Xuewen and Yang Hu (2019). Robust variable selection of varying coefficient partially nonlinear model based on quantile regression. Statistics and Its Interface, 12, 397-413.

5. Yang Jing*, Lu Fang and Yang Hu (2018). Statistical inference on asymptotic properties of two estimators for the partially linear single-index models. Statistics, 52: 1193-121.

6. Yang Jing*, Lu Fang and Yang Hu (2018). Quantile regression for robust inference on varying coefficient partially nonlinear models. Journal of the Korean Statistical Society, 47: 172-184.

7. Yang Jing*, Lu Fang and Yang Hu (2017). Quantile regression for robust estimation and variable selection in partially linear varying-coefficient models. Statistics, 51: 1179-1199.

8. Lu Fang, Shengjie Li* and Shengkun Zhu (2016). Exact penalization and strong Karush-Kuhn-Tucker conditions for nonsmooth multiobjective optimization problems. Pacific Journal of Optimization, 12(2): 245-261.

9. Lu Fang and Shengjie Li* (2016). Convexificators and Strong Karush-Kuhn-Tucker Conditions for Nonsmooth Multiobjective Optimization Problems. Pacific Journal of Optimization, 12(4): 699-715.

10. Lu Fang, Shengjie Li* and Jing Yang (2015). Convergence Analysis of Weighted Expected Residual Method for Nonlinear Stochastic Variational Inequality Problems. Mathematical Methods of Operations Research, 82(2): 229-242.

11. Lu Fang and Shengjie Li* (2015). Method of Weighted Expected Residual for Solving Stochastic Variational Inequality Problems. Applied Mathematics and Computation, 269: 651-663.

12. Lu Fang and Chunrong Chen* (2014). Newton-Like Methods for Solving Vector Optimization Problems. Applicable Analysis, 93(8): 1567-1586

联系方式：lf@hunnu.edu.cn