姓 名：刘旭
职 称：教授
研究方向：深度学习与人工智能、非参半参统计，金融风险，基因环境互作，高维数据
教授课程：数理统计；高等数理统计；数据分析与统计建模；计算机编程（C语言）
E - mail：liu.xu@sufe.edu.cn；电话：021-6590-1156
个人主页：https://xliusufe.github.io/
序号 | 项目名称 | 项目编号 | 项目来源 | 起止时间 | 项目经费 |
1 | 复杂疾病基因数据的半参数建模及统计推断 | 11771267 | 国家自然科学基金面上项目 | 2018.1-2021.12 | 48万 |
2 | 整合复杂网络的高维统计推断及其在基因组学数据上的应用 | 12271329 | 国家自然科学基金面上项目 | 2023.1-2026.12 | 46万 |
3 | 大数据背景下不完全数据的统计分析方法、理论和应用 | 72331005 | 国家自然科学基金重点项目（子项目负责人） | 2024.1-2028.12 | 165万 |
4 | 2024111003 | 上海市教委 | 2024.12-2025.12 | 20万 | |
5 | 现代线性模型基础 | 2024150048 | 研究生教学项目 | 2024.6-2026.5 | |
6 | 高维数据统计推断 | 2024120079 | 本科生教学项目 | 2024.5-2026.4 | |
| 7 | 智能决策驱动的线性建模 | 2025120149 | 本科生教学项目 | 2025.8-2026.7 | |
| 8 | 回归分析 | 2025120073 | 本科生教学项目 | 2025.7-2026.6 | |
| 9 | 复杂函数型数据的分析方法、理论及其应用 | 上海财经大学创新团队建设（参与） | 2022.9-2027.8 | ||
10 | 联合检验及其应用 | 2017110079 | 科研项目 | 2016.9-2019.8 |
1.生成式学习：主要应用生成式学习研究复杂数据的统计建模、统计推断，提升传统统计方法对复杂大数据的估计精度、预测准确率、统计推断的效率。
2.迁移学习：当复杂大数据源存在多元异质情形下，研究如何利用现有的数据源，提高目标数据集的统计预测和推断效率，包括可迁移性检验、高维非参模型的深度迁移学习等。
3.高维检验：主要研究高维回归系数的检验问题，包括单个系数的检验、高维整体检验，特别是当冗余参数为高维或非参函数时的高维检验问题。
2024.7-至今 常任(tenured)教授，上海财经大学，统计与管理学院
2022.7-2024.6 常任(tenured)副教授，上海财经大学，统计与管理学院
2019.7-2022.6 副教授，上海财经大学，统计与管理学院
2016-2019.8 助理教授，上海财经大学，统计与管理学院
2013-2016 博士后，密歇根州立大学，统计概率系
2011-2013 博士后，美国西北大学，统计系
Xu, C., Su, H., Liu, X. and You, J.* (2026). Detecting Structural Breaks In High-Dimensional Functional Time Series Factor Models. Statistica Sinica. DOI:10.5705/ss.202025.0014.
Liu, X., Huang, J., Zhou, Y., Zhang, F. and Ren, P.* (2025). Subgroup testing in change-plane models and its applications to medical data. Statistica Sinica.
Zhang, F., Ma, Y., Liu, X. and Zhou, X.* (2025). Revisiting the hedging and safe haven roles of gold: Evidence from quantile-on-quantile approach. North American Journal of Economics and Finance. 80, 102516.
Zhang, X., Ren, P., Shi, X. Ma, S. and Liu, X.* (2025). Subgroup testing in the change-plane Cox model. Statistics in Medicine, 44:e70179.
Hu, J., Li, T., Liu, X. and Liu, X.* (2025). Random projection-based response best-subset selector. Journal of Multivariate Analysis. 210, 105465.
Ren, P., Liu, X., Zhang, X. Zhan, P. and Qiu, T.* (2024). Integrative analysis of regional differences in elder support with high-dimensional quantile regression. Journal of Applied Statistics.
An R-package “qfabs” is available at https://github.com/PanpanRen/qfabs.
Tan, X., Zhang, X., Cui, Y. and Liu, X.* (2024). Uncertainty quantification in high-dimensional linear models incorporating graphical structures with applications to gene set analysis. Bioinformatics. Published online.
An R-package “gcdl” is available at https://github.com/XiaoZhangryy/gcdl.
Bar-Lev, S. K., Liu, X.*, Ridder, A., and Xiang, Z. (2024). Generalized Linear Model Applications for the Exponential Dispersion Model Generated by the Landau Distribution. Mathematics. 12.
An R-package TBEinf is available at https://github.com/xliusufe/TBEinf.
Liu, X., Lian, H.* and Huang, J. (2024). More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization. Journal of Machine Learning Research. Published online.
An R-package tensorMAM is available at https://github.com/xliusufe/tensorMAM.
Feng X., Gao, Y., Huang, J., Jiao, Y. and Liu, X.* (2024). Relative Entropy Gradient Sampler for Unnormalized Distributions. Journal of Computational and Graphical Statistics. Published online.
DOI:https://doi/full/10.1080/10618600.2024.2340523
Chen, Z., Cheng, X., and Liu, X.* (2024). Hypothesis testing on high dimensional quantile regression. Journal of Econometrics. Published online.DOI: https://doi.org/10.1016/j.jeconom.2023.105525
Zhang, X., Shi X., Liu, Y., Liu, X., and Ma, S. (2023). A general framework for identifying hierarchical interactions and its application to genomics data. Journal of Computational and Graphical Statistics. Published. DOI: 10.1080/10618600.2022.2152034. An R-package HierFabs is available at https://github.com/xliusufe/HierFabs
Li, X., Feng, X. and Liu, X.* (2023). Heritability estimation for a linear combination of phenotypes via ridge regression. Bioinformatics. DOI:10.1093/bioinformatics/btac587.
An R-package “MultiRidgeVar” is available at https://github.com/xg-SUFE1/MultiRidgeVar.
Zhang, X., Liu, X., and Shi X.* (2023). Model Selection for Varying Coefficient Nonparametric Transformation Model. The Econometrics Journal. Accepted. An R-package “GFabs” is available at https://github.com/xliusufe/GFabs.
Hu, J., Huang, J., Liu, X. and Liu, X.* (2022). Response Best-subset Selector for Multivariate Regression with High-dimensional Response Variables. Biometrika. DOI:10.1093/biomet/asac037.
An R-package “rbs” is available at https://github.com/xliusufe/rbs.
Feng X., Gao, Y., Huang, J., Jiao, Y. and Liu, X.* (2021). Relative Entropy Gradient Sampler for Unnormalized Distributions. Submitted to ICLR2022. Manuscript is available at http://arxiv.org/abs/2110.02787.
Cheng, C., Feng, X., Huang, J. and Liu, X.* (2020+). Regularized projection score estimation of treatment effects in high-dimensional quantile regression. Statistica Sinica. Published online DOI: 10.5705/ss.202019-0247.
An R-package “pqr” is available at https://github.com/xliusufe/pqr.
Liu, X., Zheng, S. and Feng, X. (2020). Estimation of error variance via ridge regression. Biometrika. 107, 481-488.
An R-package “RidgeVar” is available at https://github.com/xliusufe/RidgeVar, and a Python package “ridgevar” is available https://github.com/xliusufe/RidgeVarpy. DOI: 10.1093/biomet/asz074.
Gao, B., Liu, X. , Li, H. and Cui, Y. (2020). Integrative analysis of genetical genomics data incorporating network structures. Biometrics. 75, 1063-1075. (The first two authors contributed equally to this work) DOI: 10.1111/biom.13072.
An R-package “IVGC” is available at https://github.com/xliusufe/IVGC.
Liu, X., Zhong, P. S. and Cui, Y. (2020). Joint test of parametric and nonparametric effects in partial linear models for gene-environment interaction. Statistica Sinica. 30, 325-346. DOI:10.5705/ss.202017.0039
Liu, X., Gao, B. and Cui, Y. (2017). Generalized partially linear varying multi-index coefficient model for gene-environment interactions. Statistical Applications in Genetics and Molecular Biology. 16, 59-74.
Liu, X., Wang, H. and Cui, Y. (2016). Statistical identification of gene-gene interactions triggered by nonlinear environmental modulation. Current Genomics. 17, 388-395. [PDF]
Liu, X., Cui, Y. and Li, R. (2016). Partially linear varying multi-index coefficient model for integrative gene-environment interactions. Statistica Sinica. 26, 1037-1060. [PDF] [Codes]
Liu, X., Song, X., Xie, S. and Zhou, Y. (2016). Variable selection for gamma frailty transformation models with application to diabetic complications. Canadian Journal of Statistics. 44, 375-394. [PDF]
Liu, X., Jiang, H. and Zhou, Y. (2014). Local empirical likelihood inference for varying-coefficient density-ratio models based on case-control data. Journal of the American Statistical Association. 109,635-646. [PDF]
Liu, X., Liu, P. and Zhou, Y. (2011). Distribution estimation with auxiliary information for missing data. Journal of Statistical Planning and Inference. 141, 711-724.
Liu, X. and Ishifaq, A. (2011). Distribution estimation with smoothed auxiliary information. ACTA Mathematicae Applicatae Sinica. 27, 167-176.
专著
刘旭，谭祥勇. (2025). 高维数据分析与统计推断，科学技术出版社（专著）. ISBN 978-7-03-083320-4.
上海市东方英才计划（青年）
上海市自然科学二等奖
上海市第十六届哲学社会科学优秀成果奖二等奖第十七届“挑战杯”上海市科技作品竞赛三等奖
上海财经大学“校先进工作者”称号
Associate Editor of the “Journal of Statistical Theory and Applications”
Associate Editor of the “International Journal of Organizational and Collective Intelligence”
Guest Editor of the Special Issue "Recent Developments in Mathematical and Statistical Finance" in the "Axioms"
Invited talk at Hangzhou International Conference on Frontiers of Data Science on May 26, 2019. Title “Inference on covariate effects under ridge regression for high dimensional data”.
Invited talk at Northest Normal University on November 6, 2018. Title “A tensor estimation approach to multivariate additive models”.
Invited talk at Shanghai University of International Business and Economics on October 25, 2018. Title “Inference on covariate effects under ridge regression for high dimensional data”.
Invited Talk at Qingdao (ICSA 2018) on July 4, 2018. Title “Joint test of parametric and nonparametric effects in partial linear models for gene-environment interaction”.
Invited talk at Zhongnan University of Economics and Law on March 25, 2017. Title “Integrative Analysis of Genetical Genomics Data Incorporating Network Structures”.
ICSA 2015 Applied Statistics Symposium, invited presenter.
ICSA 2012 Applied Statistics Symposium.
The 2009 International Symposium on “Statistics and Management Science”.
2010 International Conference of Statistics and Management Science.
Nonlinear Time Series: Threshold Modelling and Beyond an International Conference in Honour of Professor Howell Tong.
4thth International Forum on Statistics Renmin University of China,
5thth International Symposium on Frontier of Statistics Science.
