Name: Liu Xu
Title: Professor
Research directions: Deep learning and artificial intelligence, non binary parametric statistics, financial risk, gene environment interaction, high-dimensional data
Teaching courses: Mathematical Statistics; Advanced mathematical statistics; Data analysis and statistical modeling; Computer Programming (C Language)
E - mail： liu.xu@sufe.edu.cn
Phone: 021-6590-1156Personal
homepage: https://xliusufe.github.io/
Research Project
Number | Name | Project Number | Source | Time | Funds |
1 | Semi parametric modeling and statistical inference of complex disease gene data | 11771267 | National Natural Science Foundation of China General Project | 2018.1-2021.12 | 480,000 |
2 | High dimensional statistical inference integrating complex networks and its application in genomics data | 12271329 | National Natural Science Foundation of China General Projec | 2023.1-2026.12 | 460,000 |
3 | Statistical analysis methods, theories, and applications of incomplete data in the context of big data | 72331005 | Key Project of National Natural Science Foundation of China (Sub project Leader) | 2024.1-2028.12 | 165,000 |
4 | 2024111003 | Shanghai Municipal Education Commission | 2024.12-2025.12 | 200,000 | |
5 | Fundamentals of Modern Linear Models | 2024150048 | Graduate Teaching Program | 2024.6-2026.5 | |
6 | Statistical inference of high-dimensional data | 2024120079 | Undergraduate Teaching Program | 2024.5-2026.4 | |
| 7 | Intelligent Decision Driven Linear Modeling | 2025120149 | Undergraduate Teaching Program | 2025.8-2026.7 | |
| 8 | regression analysis | 2025120073 | Undergraduate Teaching Program | 2025.7-2026.6 | |
| 9 | Analysis methods, theories, and applications of complex functional data | Shanghai University of Finance and Economics Innovation Team Building (Participation) | 2022.9-2027.8 | ||
10 | Joint Inspection and Its Application | 2017110079 | Research Project | 2016.9-2019.8 |
Research Field
1. Generative learning: Mainly applies generative learning to study statistical modeling and inference of complex data, improving the estimation accuracy, prediction accuracy, and statistical inference efficiency of traditional statistical methods for complex big data.
2. Transfer learning: When complex big data sources have multiple heterogeneous situations, research how to use existing data sources to improve the statistical prediction and inference efficiency of the target dataset, including transferability testing, deep transfer learning of high-dimensional non parametric models, etc.
3. High dimensional testing: This mainly studies the testing of high-dimensional regression coefficients, including the testing of individual coefficients, high-dimensional global testing, especially when redundant parameters are high-dimensional or non parametric functions.
Educational Background
2007-2011 PhD, Yunnan University (jointly trained with the Chinese Academy of Sciences), Department of Statistics
Work Experience
July 2024 present Tenured Professor, School of Statistics and Management, Shanghai University of Finance and Economics
2022.7-2024.6 Tenured Associate Professor, School of Statistics and Management, Shanghai University of Finance and Economics
Associate Professor, School of Statistics and Management, Shanghai University of Finance and Economics, July 2019 June 2022
2016-2019.8 Assistant Professor, School of Statistics and Management, Shanghai University of Finance and Economics
2013-2016 Postdoctoral Fellow, Department of Statistical Probability, Michigan State University
2011-2013 Postdoctoral Fellow, Department of Statistics, Northwestern University, USA
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.
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.
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 Rpackage 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.
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.
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., 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., 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]
Monograph
Liu Xu, Tan Xiangyong (2025). High dimensional data analysis and statistical inference, published by Science and Technology Press (monograph) ISBN 978-7-03-083320-4.
Rewards, Honors
Shanghai Oriental Talent Program (Youth)
Shanghai Natural Science Second Prize
Second Prize of the 16th Shanghai Philosophy and Social Sciences Outstanding Achievement Award
Third Prize in the 17th "Challenge Cup" Shanghai Science and Technology Works Competition
The title of "Advanced Worker" of Shanghai University of Finance and Economics
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.
