主要研究方向为高频、高维数据统计、经济金融计量、机器学习等。在Journal of the American Statistical Association（JASA、Journal of Econometrics、Biometrika、Journal of Business and Economic Statistics等统计学相关领域的核心期刊上发表论文20余篇。

主要研究方向为高频、高维数据统计、经济金融计量、机器学习等。在Journal of the American Statistical Association（JASA、Journal of Econometrics、Biometrika、Journal of Business and Economic Statistics等统计学相关领域的核心期刊上发表论文20余篇。

1.第一届统计科学技术进步奖一等奖、2021、第一完成人

2.教育部高校优秀科技成果奖（人文社科）三等奖、2024、论文第一作者

1.  国家自然科学基金重点项目子课题、202501-202912、70万、在研

2. 国家自然科学基金重点专项项目、202401-202712、199万、在研

3. 国家自然科学基金面上项目、202001-202312、50万、已结题

4. 国家自然科学基金面上项目、201601-201912、39万、已结题

5. 国家自然科学基金青年项目、201301-201512、22万、已结题

1. Yong He, Xinbing Kong, Long Yu, Lorenzo Trapani. Online change-point detection for matrix-valued time series with latent two-way factor structure, Annals of Statistics, 2024, Published Online.

2. Yong He, Xinbing Kong, Long Yu, Xinsheng Zhang and Changwei Zhao. Matrix factor anlysis: from least squares to iterative projection, Journal of Business and Economic Statistics, 2024,42,322-334.

3. Xin-Bing Kong, Jin-Guan Lin, Cheng Liu, Guang-Ying Liu. Discrepancy between global and local principal component analysis on large-panel high-frequency data, Journal of the American Statistical Association, 2023, 118,1333-1344.

4. Yong He, Xinbing Kong, Lorenzo Trapani, Long Yu. One-way or two-way factor model for matrix sequences? Journal of Econometrics, 2023, 235,1981-2004.

5. Yong He, Xinbing Kong, Long Yu, Xinsheng Zhang. Large-dimensional factor analysis without moment constraints, Journal of Business and Economic Statistics, 2022, 40, 302-312.

6. Long Yu, Yong He, Xinbing Kong*, Xinsheng Zhang. Projected estimation for large-dimensional matrix factor model, Journal of Econometrics, 2022, 229, 201-217.

7. Xinbing Kong. A random-perturbation-based rank estimator of the number of factors, Biometrika, 2020, 107, 505-511.

8. Xinbing Kong, Jiangyan Wang, Jinbao Xing, Chao Xu and Chao Ying. Factor and idiosyncratic empirical processes,Journal of the American Statistical Association, 2019, 114, 1138-1146.

9. Xin-Bing Kong, Zhi Liu, Wang Zhou. A rank test of the number of factors with high-frequency data, Journal of Econometrics, 2019, 211, 439-460.

10. Xin-Bing Kong and Cheng Liu. Testing against constant factor loading matrix with large panel high-frequency data, Journal of Econometrics, 2018, 204, 301-319.

11. Xin-Bing Kong. On the systematic and idiosyncratic volatility with large panel high-frequency data, Annals of Statistics, 2018, 46, 1077-1108.

12. Zhi Liu, Xin-Bing Kong, Bing-Yi Jing. Estimating the integrated volatility using high-frequency data with zero durations，Journal of Econometrics, 2018，204, 18-32.

13. Donggyu Kim, Xinbing Kong, Cuixia Li, Yazhen Wang. Adaptive thresholding estimator for large volatility matrix estimation based on high-frequency financial data, Journal of Econometrics, 2018, 203, 69-79.

14. Xin-Bing Kong, Shao-Jun Xu, Wang Zhou. Bootstrapping volatility functionals: a local and nonparametric perspective, Biometrika, 2018, 105, 463-469.

15. Xin-Bing Kong. On the number of common factors with high-frequency data, Biometrika, 2017, 104, 397-410.

16. Xin-Bing Kong, Zhi Liu, Bing-Yi Jing. Testing for pure-jump processes for high-frequency data, Annals of Statistics, 2015, 43, 847-877.

17. Bing-Yi Jing, Zhi Liu, Xin-Bing Kong. On the estimation of integrated volatility with jumps and microstructure noise, Journal of Business and Ecoomic Statistics, 2014, 32, 457-467.

18. Bing-Yi Jing, Xin-Bing Kong, Zhi Liu. Modeling high frequency data by pure-jump processes? Annals of Statistics, 2012, 40, 759-784.

19. Bing-Yi Jing, Xin-Bing Kong, Zhi Liu, Per A Mykland. On the jump activity index for semimartingales, Journal of Econometrics, 2012, 166, 213-223.

20. Bing-Yi Jing, Xin-Bing Kong, Zhi Liu. Estimating the jump activity index under noisy observations using high frequency data, Journal of the American Statistical Association, 2011, 106, 558-568.

1.第三届紫丁香应用统计国际会议45分钟大会报告、2021

2.第十二届全国概率统计会议1小时大会报告、2023

3.中国现场统计研究会统计学术前沿研讨会50分钟大会报告、2023