刘秉辉，东北师范大学，教授、博导，统计系主任；入选国家级青年人才计划、国家天元数学东北中心优秀青年学者、吉林省拔尖创新人才；主要从事统计机器学习和网络数据分析方面的研究；在统计学、计算机&人工智能、计量经济学领域期刊发表学术论文三十余篇，部分成果发表在：J AM STAT ASSOC、ANN STAT、ANN APPL STAT，ARTIF INTELL、J MACH LEARN RES，J ECONOMETRICS、J BUS ECON STAT等；主持国家自然科学基金项目多项；担任中国现场统计研究会因果推断分会副理事长、中国现场统计研究会统计交叉科学研究分会副理事长等。

招生方向：
统计学博士（1-2人），具备扎实的数学基础或PyTorch编程能力，从事网络数据分析、高维数据分析、深度学习（图神经网络、大型语言模型）等方面的科学研究；

统计学学硕（2-3人），具备扎实的数学基础，从事网络数据分析、高维数据分析等方面的科学研究，且有明确的读博意愿；应用统计专硕（2-3人），具备熟练的计算机编程能力，进行深度学习（图神经网络、大型语言模型）等方面的应用研究。

学术论文：

1. Wang, J., Wu, M., Liu, Y., Liu, B.* and Guo, J. Joint community detection in random effects stochastic block models via the split-likelihood method, Journal of Computational and Graphical Statistics, 2025+, accepted.

2. Wang, J., Liu, B.*, Jing, B. and Guo, J.* Understanding asymptotic consistency and its unique advantages in large sample statistical inference, Journal of Multivariate Analysis, 2025+, accepted.

3. Feng, L., Liu, B.* and Ma, Y. Testing for high-dimensional white noise, Statistica Sinica, 2025+, in press.

4. Wang, H., Liu, B.*, Feng, L.* and Ma, Y. Fishers combined probability test for cross-sectional independence in panel data models with serial correlation, Statistica Sinica, 2025+, in press.

5. Hu, K., Li, D.* and Liu, B. Reproducible learning of Gaussian graphical models via graphical lasso multiple data splitting, Acta Mathematica Sinica, English Series, 2025, 41(2), 553-568.

6. Yuan, Q., Liu, B.*, Li D.* and Ma, Y. Community extraction of network data under stochastic block models, Statistica Sinica, 2025, 35, 1-21.

7. Wang, H., Liu, B., Feng, L.* and Ma, Y.* Rank-based max-sum tests for mutual independence of high-dimensional random vectors, Journal of Econometrics, 2024, 238, 105578.

8. Feng, L., Liu, B.* and Ma, Y. A one-sided refined symmetrized data aggregation approach to robust mutual fund selection, Journal of Business & Economic Statistics, 2024, 42(1), 257-271.

9. Feng, L., Jiang, T., Li, X.* and Liu, B. Asymptotic independence of the sum and maximum of dependent random variables with applications to high-dimensional tests, Statistica Sinica, 2024, 34, 1745-1763.

10. Wang, J., Zhang, J., Liu, B., Zhu, J.* and Guo, J.* Fast network community detection with profile-pseudo likelihood methods, Journal of the American Statistical Association, 2023,118(542), 1359-1372.

11. Liu, B., Feng, L.* and Ma, Y. High-dimensional alpha test of the linear factor pricing models with heavy-tailed distributions, Statistica Sinica, 2023, 33, 1389-1410.

12. Huang, X., Liu, B., Zhou, Q. and Feng, L.* A high-dimensional inverse norm sign test for two-sample location problems, Canadian Journal of Statistics, 2023, 51(4), 1004-1033.

13. Liu, B. and Guo, J.* Decomposition of covariate-dependent graphical models with categorical data, Communications in Mathematical Research, 2023, 39(3), 414-436.

14. Feng, L., Jiang, T., Liu, B. and Xiong, W.* Max-Sum tests for cross-sectional independence of high-dimensional panel data, Annals of Statistics, 2022, 50(2), 1124-1143.

15. Feng, L., Lan, W., Liu, B.* and Ma, Y. High-dimensional test for alpha in linear factor pricing models with sparse alternatives, Journal of Econometrics, 2022, 229(1), 152-175.

16. Feng, L., Zhang, X. and Liu, B.* High-dimensional proportionality test of two covariance matrices and its application to gene expression data, Statistical Theory and Related Fields, 2022, 6(2), 161-174.

17. Ding, Y., Liu, B. and Feng, L.* Rank-based test for slope homogeneity in high dimensional panel data models, Metrika, 2022, 85, 605-626.

18. Feng, L., Liu B.* and Ma Y. An inverse norm sign test of location parameter for high-dimensional data, Journal of Business & Economic Statistics, 2021, 39(3), 807-815.

19. Wang, H., Feng, L.*, Liu, B. and Zhou, Q. An inverse norm weight spatial sign test for high-dimensional directional data, Electronic Journal of Statistics, 2021, 15(1), 3249-3286.

20. Wang, J., Guo, J.* and Liu, B.* A fast algorithm for integrative community detection of multi-layer networks, Stat, 2021, 10(1), e348.

21. Wang, J., Liu, B. and Guo, J.* Efficient split likelihood-based method for community detection of large-scale networks, Stat, 2021, 10(1), e349.

22. Yuan, Q. and Liu, B.* Community detection via an efficient nonconvex optimization approach based on modularity, Computational Statistics & Data Analysis, 2021, 157, 107163.

23. Feng, L., Zhao, P., Ding, Y. and Liu, B.* Rank-based tests of cross-sectional dependence in panel data models, Computational Statistics & Data Analysis, 2021, 153, 107070.

24. Feng, L., Ding, Y. and Liu, B.* Rank‐based tests for cross‐sectional dependence in large (N, T) fixed effects panel data models, Oxford Bulletin of Economics & Statistics, 2020, 82(5), 1198-1216.

25. Feng, L., Zhang, X. and Liu, B.* Multivariate tests of independence and their application in correlation analysis between financial markets, Journal of Multivariate Analysis, 179, 2020, 104652.

26. Feng, L., Zhang, X. and Liu, B.* A high-dimensional spatial rank test for two-sample location problems, Computational Statistics & Data Analysis, 2020, 144, 106889.

27. Liu, B., Wu, C., Shen, X. and Pan, W.* A novel and efficient algorithm for de novo discovery of mutated driver pathways in cancer, Annals of Applied Statistics, 2017, 11(3): 1481-1512.

28. Feng, L. and Liu, B.* High dimensional rank tests for sphericity. Journal of Multivariate Analysis, 2017, 155: 217-233.

29. Liu, B., Shen, X.T. and Pan, W.* Integrative and regularized principal component analysis of multiple sources of data, Statistics in Medicine, 2016, 35: 2235-2250.

30. Liu, B., Shen, X.T. and Pan, W.* Nonlinear joint latent variable models and integrative tumor subtype discovery, Statistical Analysis & Data Mining, 2016, 9(2): 106-116.

31. Pan, W.*, Shen, X. and Liu, B. Cluster analysis: unsupervised learning via supervised learning with a non-convex penalty, Journal of Machine Learning Research, 2013, 14(7): 1865-1889.

32. Liu, B. and Guo, J.* Collapsibility of conditional graphical models. Scandinavian Journal of Statistics, 2013, 40(2): 191-203.

33. Liu, B., Shen, X. and Pan, W.* Semi-supervised spectral clustering with application to detect population stratification, Frontiers in Genetics, 2013, 4: 215.

34. Liu, B., Guo, J.* and Jing, B. A note on minimal d-separation trees for structural learning, Artificial Intelligence, 2010, 174: 442-448.