重庆市工业与应用数学学会第五、六届理事会理事；教育部学位中心学位论文通讯评审人、重庆市自然科学基金面上项目通讯评审人、国际数理统计学会(Institute of Mathematical Statistics, IMS)会员。

超高维数据分析、变量选择、稳健估计。

数理统计、应用回归分析、概率论、统计预测与决策、市场调查、统计分析软件(研究生)、实用回归分析(研究生)。

1. Guo Chaohui(郭朝会) and Li Jialiang*. Homogeneity and Structure Identification in Semiparametric Factor Models. Journal of Business & Economic Statistic(计量经济学顶级期刊)，2022，40:1, 408–422 (SCI).

2. Guo Chaohui(郭朝会), Lv Jing*, Yang Hu, Tu Jingwen and Tian Chenxiao. Semiparametric Model Averaging for Ultrahigh-Dimensional Conditional Quantile Prediction. Acta Mathematica Sinica, English Series, 2023, 39, 1171–1202 (SCI).

3. Guo Chaohui(郭朝会), Zhang Wenyang*. Model Averaging based Semiparametric Modelling for Conditional Quantile Prediction. SCIENTIA SINICA Mathematica, 2023, accepted, https://doi.org/10.1007/s11425-022-2205-1, (SCI).

4. Guo Chaohui(郭朝会), Lv Jing* and Wu Jibo. Composite quantile regression for ultra-high dimensional semiparametric model averaging. Computational Statistics & Data Analysis，2021，160:107231 (SCI).

5. Tu,  Jingwen, Yang, Hu, Guo Chaohui*(郭朝会) Lv  Jing, 2021. Model averaging marginal regression for high dimensional  conditional quantile prediction. Statistical Papers, 62:2661–2689 (SCI).

6. Lv Jing, Guo Chaohui*(郭朝会),Wu Jibo,  2019. Subject-wise empirical likelihood  inference for robust joint  mean-covariance model with longitudinal  data. Statistics and Its Interface, 12: 617–630. (SCI)

7. Lv  Jing, Guo Chaohui*(郭朝会), Wu Jibo,  2019. Smoothed empirical likelihood inference  via the  modified Cholesky decomposition for quantile varying  coefficient models with  longitudinal data. TEST, 28:999–1032. (SCI)

8. Lv  Jing, Guo Chaohui*(郭朝会),  2019. Quantile estimations via modified Cholesky  decomposition for  longitudinal single-index models. Annals of the institute of statistical mathematics, 71:1163–1199. (SCI)

9. Lv Jing, Guo Chaohui*(郭朝会), Li Tingting，Hao Yuanyuan, Pan  Xiaolin, 2018. Adaptive robust  estimation in joint mean–covariance regression model for bivariate  longitudinal data [J]. Statistics, 52:64-83. (SCI)

10.吕晶，郭朝会*，杨虎，李婷婷，2018. 纵向数据的有效秩推断基于修正的Cholesky分解. 数学学报中文版，61: 549-568.

11. Guo Chaohui*(郭朝会), Yang Hu and Lv Jing. Two step estimations for a single-index varying-coefficient model with longitudinal data. Statistical Papers, 2018, 59:957–983 (SCI).

12.Lv Jing, Guo Chaohui*(郭朝会), Yang Hu, Li Yalian,  2017. A moving average Cholesky factor model in covariance modeling for  composite quantile  regression with longitudinal data [J]. Computational  Statistics and Data  Analysis, 112: 129-144. (SCI)

13.Lv  Jing, Guo Chaohui*(郭朝会), 2017. Efficient parameter estimation via modified Cholesky  decomposition for  quantile regression with longitudinal data [J].   Computational Statistics, 32: 947-975. (SCI)

14.Guo  Chaohui(郭朝会), Yang Hu, Lv  Jing*, 2017. Robust variable selection in high-dimensional varying  coefficient  models based on weighted composite quantile regression. STATISTICAL PAPERS, 58(4): 1009-1033. (SCI)

15.Guo Chaohui(郭朝会), Yang Hu, Lv Jing*, 2017. Robust variable selection for generalized linear models  with a  diverging number of parameters. COMMUNICATIONS IN STATISTICS-THEORY   AND METHODS, 46(6): 2967-2981. (SCI)

16.Guo  Chaohui(郭朝会), Yang Hu, Lv Jing*,  Wu, Jibo, 2016. Joint estimation  for single index mean-covariance models with  longitudinal  data. JOURNAL  OF THE KOREAN STATISTICAL SOCIETY, 45(4): 526-543. (SCI)

17.Guo Chaohui*(郭朝会), Yang Hu, Lv Jing, 2016. Generalized varying index   coefficient models. JOURNAL  OF COMPUTATIONAL AND APPLIED  MATHEMATICS, 300(1): 1-17. (SCI)

18.Yang  Hu, Guo Chaohui *(郭朝会), Lv Jing, 2016. Variable selection for generalized   varying coefficient models with longitudinal data. STATISTICAL  PAPERS, 57:115–132. (SCI)

19.Yang  Hu, Guo Chaohui *(郭朝会), Lv Jing, 2015.SCAD penalized rank regression  with a  diverging numberof parameters. Journal of Multivariate Analysis, 133:  321–333. (SCI)

20.Yang  Hu, Guo Chaohui *(郭朝会), Lv Jing, 2014. A robust and efficient  estimation  method for single-index varying-coefficient models. Statistics and   Probability Letters, 94 :119–127.(SCI)

1. 国家自然科学基金青年项目，两类半参数因子模型的稳健估计与结构识别(12201091)，2023/01－2025/12，30万元，主持；

2. 国家社会科学基金青年项目，面板数据下分位数回归模型的高维变量选择及应用研究(17CTJ015)，2017/07－2020/06，20万元，主持；

3. 重庆市基础研究与前沿探索项目，纵向数据下变指标系数模型的统计推断及其应用(cstc2018jcyjA0659)，5万元， 2018/07－2021/06，主持；

4. 重庆市自然科学基金面上项目，超高维复杂数据的条件分位数预测(CSTB2022NSCQ-MSX0852)，2022/08—2025/07, 主持；

5. 全国统计科学研究项目，超高维面板数据的统计预测及在金融大数据中的应用(2022LY019)，2022/07—2024/07，主持；

6. 重庆市教委科学技术研究项目， 大数据背景下的分位数预测(KJQN201900511)，2019/10—2022/10，4万元，主持；

7. 重庆市教委科学技术研究项目， 超高维生物大数据的特征筛选与模型平均预测研究(KJQN202100526)，2021/10—2024/10，4万元，主持；

8. 国家自然科学基金面上项目，复杂统计数据的参数和半参数模型选择及在金融大数据中的应用(11671059)，2017/01－2020/12，48 万元，主研；

9. 重庆市教委科学技术研究项目，纵向单指标模型的稳健变量选择及在金融大数据中的应用(KJ1703054)，2017/01—2018/12，3 万元，主持。

2018年入选澳门十大正规网投平台（第五批）青年拔尖人才培育计划，10万元， 2019/01—2022/01；2020年度考核优秀；2018-2019年度本科生优秀导师。