Name: Feng Xingdong
Title: Professor
Research Areas: Dimensionality reduction, robust estimation, quantile regression and its applications, distributed statistical computing
Courses Taught: Mathematical Statistics, Applied Survival Analysis, Advanced Survival Analysis, Computational Statistics, Distributed Statistical Computing, Data Processing and Visualization
Personal Homepage: ddgene.github.io
Research Project
Serial Number | Project Name | Project Number | Project Source | Start and End Time | Project Funding |
1 | Reinforcement Learning Based on Quantile Regression | 12371270 | National Natural Science Foundation General Project | 2024-2027 Year | 435,000 |
2 | Spatio-temporal data analysis based on semiparametric quantile regression model | 11971292 | National Natural Science Foundation General Program | 2020-2023 Year | 490,000 |
3 | Estimation of Quantile Regression Process and Its Applications | 11571218 | National Natural Science Foundation General Project | 2016-2019 Years | 450,000 |
4 | Dimension test of the mathematical expectation of the matrix and other statistics. | 11101254 | National Natural Science Foundation of China Youth Project | 2012-2014 Years | 220,000 |
5 | Variable Selection in High-Dimensional Data and Its Applications in Finance and Biology | 13PJC048 | Shanghai Pujiang Talent Program Team Project | 2013-2015 Years | 300,000 |
6 | Multi-Agent Reinforcement Learning Methods and Their Theoretical Foundations | Shanghai Municipal Science and Technology Commission Project |
Research Field
Quantile regression theory and its applications, distributed statistical computing methods, matrix data dimension reduction theory and algorithms, etc.
Educational Background
2009 University of Illinois at Urbana-Champaign Ph.D. in Statistics
2004 Year Canada York University Master of Statistics
2002 Renmin University of China Master's Degree in Economics
1999 Nanjing University Bachelor of Science
Work Experience
2015 to Present Shanghai University of Finance and Economics School of Statistics and Management Professor (Full professor with tenure)
2014-2015 Shanghai University of Finance and Economics, School of Statistics and Management, Associate Professor with Tenure
2012-2014 Shanghai University of Finance and Economics, School of Statistics and Management, Associate Professor (Associate professor without tenure)
2011-2012 Shanghai University of Finance and Economics, School of Statistics and Management, Assistant Professor
2009-2011 National Institute of Statistical Sciences, USA Postdoc
Research Achievements
| Methodology (Journal) |
| Feng, X., Li, Q.#, Qin, X.*, Wu, M. and Yu, L.# (2026+). Structured nonlinear cure model with deep neural networks for high-dimensional survival analysis. Statistics in Medicine to appear. |
| Ma, H.#, Sang, P., Feng, X., and Liu, X.* (2026). A robust mixed functional classifier with adaptive large margin loss. Journal of Multivariate Analysis 213, 105563. |
| Bi, J.#, Feng, X., and Liu, J.* (2025). Conditional dependence learning with high-dimensional conditioning variables. SCIENCE CHINA Mathematics 68(8), 1779-1806. |
| Cheng, C.#, Ma, H.#, Zhong, Y., Uhlemann, A.-C., Feng, X., and Hu, J.* (2025). Biomarker detection for disease classification in longitudinal microbiome data. The Annals of Applied Statistics 19(2), 943-966. |
| Su, H.#, Wei, J.#, Li, T., You, J., and Feng, X.* (2025). Influence on Stock Market Yield from Perspective of Industry Heterogeneity. China Journal of Econometrics 5(2), 333-361. |
| Feng, X., Gao, Y.#, Huang, J., Jiao, Y., and Liu, X.* (2025). Relative entropy gradient sampler for unnormalized distributions. Journal of Computational and Graphical Statistics 34(1), 211-221. |
| Feng, X., He, X.*, Jiao, Y., Kang, L.*, and Wang, C.# (2024). Deep nonparametric quantile regression under covariate shift. Journal of Machine Learning Research 25(385), 1-50. |
| Ge, Y.#, Li, T., Feng, X., Wu, M.*, and Liu, H.* (2024). Structured feature ranking for genomic marker identification accommodating multiple types of networks. Biometrics ujae158. |
| Wang, C.#, Li, T., Zhang, X.#, Feng, X., and He, X.* (2024). Communication-efficient nonparametric quantile regression via random features. Journal of Computational and Graphical Statistics 33, 1175-1184. |
| Feng, X.*, Li, W.#, and Zhu, Q. (2024). Estimation and bootstrapping under spatiotemporal models with unobserved heterogeneity. Journal of Econometrics 238, 105559. |
| Feng, X., Jiao, Y., Kang, L.*, Zhang, B. and Zhou, F.* (2023). Over-parameterized deep nonparametric regression for dependent data with its applications to reinforcement learning. Journal of Machine Learning Research 24(383), 1-40. |
| He, X., Ge, Y.#, and Feng, X.* (2023). Structure learning via unstructured kernel-based M-estimation. Electronic Journal of Statistics 17, 2386-2415. |
| Feng, X., Li, W.#, and Zhu, Q.* (2023). Spatial-temporal model with heterogeneous random effects. Statistica Sinica 33, 2613-2641. |
| Yu, A.#, Zhong, Y., Feng, X.*, and Wei, Y. (2023). Quantile regression for nonignorable missing data with its application of analyzing electronic medical records. Biometrics 79, 2036-2049. |
| Liu, Y.#, Feng, X.*(2023). Clustering ambulatory missing data with applications to hypertension diagnostics (in Chinese). Journal of Applied Statistics and Management 42, 218-228. |
| Feng, X.*, Liu, Q.#, and Wang, C.# (2023). A lack-of-fit test for quantile regression process models. Statistics and Probability Letters 192, 109680. |
| Li, X.#, Feng, X., and Liu, X.* (2022). Heritability estimation for a linear combination of phenotypes via ridge regression. Bioinformatics 38, 4687-4696. |
| Cheng, C.#, Feng, X., Li, X., and Wu, M.* (2022). Robust analysis of cancer heterogeneity for high-dimensional data. Statistics in Medicine 41, 5448-5462. |
| Zhang, L., Zhu, Z.#, Feng, X., and He, Y.* (2022). Shrinkage quantile regression estimation for panel data models with multiple structural breaks. Canadian Journal of Statistics 50, 820-859. |
| Liu, Q.#, Feng, X.*(2022). Specification test of polynomials under partially linear additive quantile regression(in Chinese). Journal of Applied Statistics and Management 41, 294-308. |
| Zhang, S.# and Feng, X.* (2022). Distributed identification of heterogeneous treatment effects. Computational Statistics 37, 57-89. Online Link |
| Cheng, C.#, Feng, X.*, Huang, J., Jiao, Y., and Zhang, S.# (2022).-regularized high-dimensional accelerated failure time model. Computational Statistics and Data Analysis 170, 107430. Online Link |
| Cheng, C.#, Feng, X., Huang, J. and Liu, X.* (2022). Regularized projection score estimation of treatment effects in high-dimensional quantile regression. Statistica Sinica 32, 23-41. |
| Dong, C.#, Ma, S., Zhu, L., Feng, X.*(2021). Estimation and inference for non-crossing multiple-index quantile regression(in Chinese). SCIENTIA SINICA Mathematica 51, 631-658. |
| Liu, X., Zheng, S. and Feng, X.*(2020). Estimation of error variance via ridge regression. Biometrika 107, 481-488. |
| Dong, C.#, Li, G. and Feng, X.* (2019). Lack-of-fit tests for quantile regression models. Journal of the Royal Statistical Society B 81, 629-648. |
| Wang, H., Feng, X.* and Dong, C.# (2019). Copula-based quantile regression for longitudinal data. Statistica Sinica 29, 245-264. |
| Wu, M., Zhu, L., and Feng, X.* (2018). Network-based feature screening with applications to genome data. The Annals of Applied Statistics 12, 1250-1270. |
| Feng, X. and He, X.* (2017). Robust low-rank data matrix approximations. SCIENCE CHINA Mathematics 60, 189-200. |
| Feng, X. and Zhu, L.* (2016). Estimation and testing of varying coefficients in quantile regression. Journal of the American Statistical Association 111, 266-274. |
| Yi, Y., Feng, X., and Huang, Z.* (2014). Estimation of extreme value-at-risk: an EVT approach for quantile GARCH model. Economics Letters 124, 378-381. |
| Feng, X.*, Sedransk, N., and Xia, J.Q. (2014). Calibration using constrained smoothing with applications to mass spectrometry data. Biometrics 70, 398-408. |
| Feng, X., Feng, Y., Chen, Y.* and Small, D. S. (2014). Randomization inference for the trimmed mean of effects attributable to treatment. Statistica Sinica 24, 773-797. |
| Feng, X. and He, X.* (2014). Statistical inference based on robust low-rank data matrix approximation. The Annals of Statistics 42, 190-210. |
| Wang, H. and Feng, X.* (2012). Multiple imputation for M regression with censored covariates. Journal of the American Statistical Association 107, 194-204. |
| Feng, X., He, X. and Hu, J.* (2011). Wild bootstrap for quantile regression. Biometrika 98, 995-999. |
| Feng, X. and He, X.* (2009). Inference on low-rank data matrices with applications to microarray data. The Annals of Applied Statistics 3, 1634-1654. |
| Wang, X., Liang, D*, Feng, X. and Ye, L. (2007). A derivative free optimization algorithm based on conditional moments. Journal of Mathematical Analysis and Applications 331, 1337-1360. |
| Methodology (Conference) |
| Wang, C.# and Feng, X.* (2024). Optimal kernel quantile learning with random features. International Conference on Machine Learning 2024, Vienna, Austria. (Spotlight) |
| Feng, X., He, X., Wang, C.*#, Wang, C.# and Zhang, J. (2023). Towards a unified analysis of kernel-based methods under covariate shift. Neural Information Processing Systems 2023, New Orleans, USA. |
| Zhou, F., Wang, J.#, and Feng, X.* (2020). Non-crossing quantile regression for deep reinforcement learning. Neural Information Processing Systems 2020, Vancouver, Canada. |
| Wu, S.#, Feng, X., and Zhou, F.* (2020). Metric learning by similarity network for deep semi-supervised learning. 14th International FLINS Conference on Robotics and Artificial Intelligence (FLINS/ISKE2020), Cologne, Germany. |
| Interdisciplinary Studies |
| Li, X.#, Zhou, T.#, Feng, X.*, Yau, S.-T.*, Yau, S. S.-T.* (2024). Exploring geometry of genome space via Grassmann manifolds. The Innovation 5(5), 100677. (Impact factor: 33.2) Online Link |
| Abbatiello, S., Mani, D., Schilling, B., Maclean, B., Zimmerman, L., Feng, X. etc. (2013). Design, Implementation, and Multi-Site Evaluation of a System Suitability Protocol for the Quantitative Assessment of Instrument Performance in LC-MRM-MS. Molecular & Cellular Proteomics 12, 2623-2639. (Impact factor: 7.38) |
| Xia, J.Q., Sedransk, N. and Feng, X. (2011). Variance component analysis of a multi-site study aiming at multiple reaction monitoring measurements of peptides in human plasma. Public Library of Science One 6, e14590. (Impact factor: 3.75) |
| Broglio, S., Schnebel, B., Sosnoff, J., Shin, S. Feng, X., He, X. and Zimmerman, J. (2010). The biomechanical properties of concussions in high school football. Medicine and Science in Sports and Exercise 42, 2064-2071. (Impact factor: 6.29) |
| Feng, X., Huang, S., Shou, J., Liao, B., Yingling, J. M., Ye, X., Lin, X., Gelbert, L. M., Su, E. W., Onyia, J. E. and Li, S. (2007). Analysis of pathway activity in primary tumors and NCI60 cell lines using gene expression profiling data. Genomics Proteomics and Bioinformatics 5, 15 - 24. (Impact factor: 9.5) |
| Xia, Y., Campen, A., Rigsby, D. , Guo, Y., Feng, X., Su, E.W., Dalakal, M. and Li, S. (2007). A Microarray Gene Expression Database for Primary Human Disease Tissues. Molecular Diagnosis and Therapy 11, 145-149. (Impact factor: 3.91) |
| Note: '#' and '*' refer to students and corresponding authors, respectively. |
Rewards, Honors
Shanghai Oriental Talent Program Leading Project
Social Work
Annals of Applied Statistics, Associate Editor of Statistica Sinica, Editorial Board Member of Statistical Research.
Elected Member of the International Statistical Institute
Eighth Committee of the National Textbook Editing Committee for Statistics Professional Members (Data Science and Big Data Technology Application Group)
Vice President of the Tenth Council of the National Industrial Statistics Teaching and Research Association
Executive Director of the Probability and Statistics Branch of the Chinese Mathematical Society
Academic Reports
THE 5th Institute of Mathematical Statistics Asia Pacific Rim Meeting, June 2018, Singapore, Invited Talk, Lack-of-fit tests for quantile regression models.
2017 1st International Conference on Econometrics and Statistics, June 2017, Hong Kong, Invited Talk, Lack-of-fit tests for quantile regression models.
2016 Applied Statistics Symposium of International Chinese Statistical Association, June 2016, Atlanta, Georgia, USA, Invited Talk, Non-crossing quantile surfaces.
Joint Statistical Meetings 2015, August 2015, Seattle, Washington, USA, Topic Contributed Talk, Copula-based quantile regression for longitudinal data.
The 10th National Conference on Probability and Statistics, Probability and Statistics Society, October 2015, Shandong University, Conference Report, Estimation and testing of varying coefficients in quantile regression.
The 59th World Statistics Congress, August 2013, Hong Kong, Invited Talk, Estimation and testing of varying coefficients in quantile regression.
The 9th ICSA international conference, December 2013, Hong Kong, Invited Talk, Efficient estimation of treatment effects in high-dimensional quantile regression.
IMS-APRM 2012, July 2012, Tsukuba, Lbaraki, Japan, Invited Talk, Wild bootstrap for M-estimators of linear regression.
ENAR 2011, March 2011, Miami, Florida, USA, March 2011, Contributed Talk, Calibration using constrained smoothing with applications to mass spectrometry data.
Joint Statistical Meetings 2011, August 2011, Miami Beach, Florida, USA, Contributed Talk, Wild bootstrap for quantile regression.
Joint Statistical Meetings 2010, August 2010, Vancouver, BC, Canada, Topic Contributed Talk, Constrained smoothing of scatterplots with applications to mass spectrometry data.
Joint Statistical Meetings 2009, August 2009, Washington DC, USA, Topic Contributed Talk, Estimation of variance of least absolute deviation regression estimator with applications to protein lysate arrays.
