姓 名：李婷
职 称：副教授
研究方向：函数型数据分析，高维数据分析，分位数回归
教授课程：
E - mail：tingli@mail.shufe.edu.cn
电话： 021-65901238
序号 | 项目名称 | 项目编号 | 项目来源 | 起止时间 | 项目经费 |
| 1 | 函数型因变量回归模型的统计推断及应用研究 | 12101388 | 国家自然科学基金青年项目 | 2022.1-2024.12 | 30万 |
函数型数据分析，高维数据分析，分位数回归
2014.9-2020.6 复旦大学统计学系 博士
2017.9-2018.9 德州大学MD安德森癌症中心生物统计系 联合培养
2010.9-2014.6 华东师范大学统计学系 学士
2023.7-至今 上海财经大学 副教授
2020.8-2023.6 上海财经大学 助理研究员
2019.7-2019.9 香港中文大学 研究助理
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.
