Name: Li Ting
Title: Associate Professor
Research Interests: Functional data analysis, high-dimensional data analysis, quantile regression
Courses Taught:
E - mail：tingli@mail.shufe.edu.cn
Phone: 021-65901238
Research Project
Serial number | Project Name | Project Number | Project Source | Start and End Time | Project Funding |
1 | Statistical Inference and Application Research of Functional Response Variable Regression Models | 12101388 | National Natural Science Foundation Youth Project | 2022.1-2024.12 | 300,000 |
Research Field
Functional data analysis, high-dimensional data analysis, quantile regression
Education Background
2014.9-2020.6 Fudan University Department of Statistics Doctorate
2017.9-2018.9 University of Texas MD Anderson Cancer Center Department of Biostatistics Joint Training
2010.9-2014.6 East China Normal University Department of Statistics Bachelor's Degree
Work Experience
2023.7-Present Shanghai University of Finance and Economics Associate Professor
2020.8-2023.6 Shanghai University of Finance and Economics Assistant Researcher
2019.7-2019.9 The Chinese University of Hong Kong Research Assistant
Research Achievements
Selected Publications
Caihong Qin, Jinhan Xie, Ting Li and Yang Bai (2025 ) An adaptive transfer learning framework for functional classification. Journal of American Statistical Association, 125(550), 1201-1213.
Xiangkun Wu*, Ting Li*, Gholamali Aminian, Armin Behnamnia, Hamid R. Rabiee, and Chengchun Shi (2025) Pessimistic data integration for policy evaluation.(*Co-first authors). Advances in Neural Information Processing Systems, 39.
Chengfei Gu, Qiangqiang Zhang, Ting Li†, Jinhan Xie†, Niansheng Tang (2025) Online robust locally differentially private learning for nonparametric regression. († Co-Corresponding authors). Advances in Neural Information Processing Systems, 39.
Qiangqiang Zhang, Chengfei Gu, Xinwei Feng, Jinhan Xie†, and Ting Li† (2025) Online locally differentially private conformal prediction via binary inquries. († Co-Corresponding authors). Advances in Neural Information Processing Systems, 39.
Qiangqiang Zhang*, Ting Li*, Xiwei Feng, and Jinhan Xie (2025) Differentially private conformal prediction for uncertainty quantification. (*Co-first authors). In Forty-secondInternational Conference on Machine Learning (ICML).
Ting Li, Chengchun Shi, Zhaohua Lu, Yi Li and Hongtu Zhu (2024). Evaluating dynamic conditional quantile treatment effects with application in ridesharing. 119(547), 1201-1213.Journal of American Statistical Association.
Ting Li, Yang Yu, Xiao Wang, J.S. Marron, and Hongtu Zhu (2024+). Semi-nonparametric varying coefficients models for imaging genetics. Statistica Sinica (Accepted).
Yang Bai, Ting Li and Yang Sui (2024+). Generalized tensor regression with internal variation regularization. Statistica Sinica (Accepted, Alphabetical order).
Ting Li, Chengchun Shi, Qianglin Wen, Yang Sui, and Hongtu Zhu (2024) Combining experimental and historical data for policy evaluation. In Forty-firstInternational Conference on Machine Learning (ICML).
Ting Li, Yang Yu, J.S. Marron, and Hongtu Zhu (2024). A partially functional linear modelling framework integrating genetic, imaging and clinical data. Annals of Applied Statistics, 18(1), 704-728.
Ting Li, Chengchun Shi, Jianing Wang, Fan Zhou and Hongtu Zhu (2023). Optimal dynamic treatment allocation for efficient policy evaluation in sequential decision making. Advances in Neural Information Processing Systems, 36.
Ting Li, Huichen Zhu, Tengfei Li, and Hongtu Zhu (2023). Asynchronous functional linear regression models for longitudinal data in reproducing kernel Hilbert space. Biometrics,79(3),1880-1895.
Ting Li, Tengfei Li, Zhongyi Zhu, and Hongtu Zhu (2022). Regression analysis of asynchronous longitudinal functional and scalar data. Journal of the American Statistical Association, 117(539): 1228-1242.
Ting Li, Xinyuan Song, Yingying Zhang, Hongtu Zhu and Zhongyi Zhu (2021). Clusterwise functional linear regression models. Computational Statistics & Data Analysis, 158, 107192.
Ting Li and Zhongyi Zhu (2020). Inference for generalized partial functional linear regression. Statistica Sinica, 20, 1379-1397.
Haiqiang Ma, Ting Li, Hongtu Zhu, and Zhongyi Zhu (2019). Quantile regression for functional partially linear model in ultra-high dimensions. Computational Statistics & Data Analysis, 129, 135-147.
