个人简介：

李挺，南方科技大学统计与数据科学系研究员，副教授。主持国家自然科学基金青年项目1项，获得国家级高层次人才项目（青年）。2014年本科毕业于浙江大学竺可桢学院数学英才班，2019年博士毕业于香港科技大学数学系，2019年8月至2021年5月于耶鲁大学生物统计系从事博士后研究。2021年6月-2025年12月在香港理工大学担任助理教授。长期从事复杂数据的统计研究以及大模型与生成模型，主要包括网络结构数据研究，复杂表型的基因相关性分析以及人脑图像数据分析。已在Annals of Statistics、JASA、Annals of Applied Statistics、Genome Research、Human Brain Mapping、ICML 等统计、生物统计核心期刊和机器学习顶会上发表论文19篇。

研究方向

复杂数据分析

大模型与生成模型

生物统计

人工智能

教育背景

2016.09 - 2019.07    香港科技大学   博士    数学（统计）

2014.09 - 2016.07    香港科技大学   硕士    数学（统计）

2010.09 - 2014.07    浙江大学          本科    数学与应用数学

工作经历

2026.01 –                  南方科技大学    统计与数据科学系     副教授

2021.07 – 2025.12    香港理工大学    应用数学系               助理教授

2019.08 - 2021.05     耶鲁大学          生物统计系               博士后研究员

Publications and Preprint (^ equal contribution, * corresponding author, 'supervised student)

Machine Learning & Large Models:

• Zhang, N., Xie, J., Yan, X., Jiang, B., Li T.* and      Kong, L.*, (2026+). Renewable l 1-regularized linear support vector      machine with high-dimensional streaming data. Journal of Computational and      Graphical Statistics, Posted online.

• Zhang C., Han Y., Wang Y., Yan X., Kong L., Li T.*,      Jiang B.*. (2025). Differentially Private Analysis for Binary Response      Models: Optimality, Estimation, and Inference. ICML 2025

• Niu, S., Miao, F.', Li, T.*, Huang, J.*. (2025).      Enhancing Brain Tumor Segmentation Generalizability Using Mean Teacher and      Optimal Transport. MICCAI 2024 Challenges proceedings.

• D. Huang', Li, T.*, J. Huang*. (2024). Bayesian      Power Steering: An Effective Approach for Domain Adaptation of Diffusion      Models. ICML 2024.

Network Data & Complex Data Analysis:

• M. Che, Li, T., W. Pan, X. Wang, H. Zhang.      (alphabetical).  (2026+). Ball Impurity: Measuring      Heterogeneity in General Metric Spaces.  Journal of the American      Statistical Association. Posted online.

• J. Hu,  Li, T., X.      Wang.  (alphabetical).  (2026+). Aggregated      Projectetion Method: A New Approach for Group Factor Model. Journal of the      American Statistical Association. Posted online.

• Ma, Y., Lan, W., Leng, C., Li, T., Wang,      H. (2025). Supervised Centrality via Sparse Spatial      Autoregression.  Annals of Applied Statistics,  19(2):      1734-1752.

• Li, T., Ying, N., Yu, X., Jing, B.      Y. (2024). Semi-supervised learning in unbalanced and heterogeneous      networks. arXiv:1901.01696. Statistics and Its Interface.

• Jing, B., Li, T., Ying, N., & Yu,      X. (alphabetical).  (2022) Community Detection in      Sparse Networks Using the Symmetrized Laplacian Inverse Matrix (SLIM).      Statistica Sinica.

• Jing, B. Y., Li, T., Lyu, Z., & Xia,      D. (alphabetical).   (2021). Community detection on      mixture multi-layer networks via regularized tensor decomposition. Annals      of Statistics. Code

• Yu, X., Li, T., Jing, B., Ying,      N. (2021). Collaborative Filtering with the Awareness of Social      Networks. Journal of Business and Economic Statistics. 

Brian Imaging & Biostatistics :

• S. Su, Z. Li, L. Feng, Li, T. (2025).  A      General Framework of Brain Region Detection and Genetic Variants Selection      in Imaging Genetics. Annals of Applied Statistics, 19(2):      1533-1552. 

• J. Lu, ... Li, T., ... D. Shum. (2025). An      electronic health record-linked machine learning tool for diabetes risk      assessment in adults with prediabetes. The Innovation      Medicine, 3:100106.

• S. Wang, Li, T., B. Zhao, W. Dai, Y. Yao, C. Li, T.      Li, H. Zhu, H. Zhang. (2024). Identification and Validation of      Super-variants Reveal New Loci for Human White Matter Microstructure.      Genome Research, 34 (1), 20-33. Cover Paper.

• J. Lu, ... Li, T., ... D. Shum.      (2024). Development and validation of an electronic health      record-linked machine learning tool for assessing type 2 diabetes risk in      adults with prediabetes. The Innovation Medicine. 

• Dai, W., Li, C., Li, T., Hu, J., Zhang, H. (2022).      Super-taxon in human microbiome are identified to be associated with      colorectal cancer. BMC Bioinformatics. 23, 243 (2022). 

• Li, T., Hu, J., Wang, S., &      Zhang, H. (2021). Super‐variants identification      for brain connectivity. Human brain mapping, 42(5), 1304-1312.

• Hu, J., Li, C., Wang, S., Li, T., & Zhang, H.      (2021). Genetic variants are identified to increase risk of COVID-19      related mortality from UK Biobank data. Human genomics, 15(1), 1-10.

• Hu, J., Li, T., Wang, S., & Zhang, H. (2020).      Supervariants identification for breast cancer. Genetic Epidemiology,      44(8), 934-947.

Preprints:

• D. Huang', J. Huang, Li, T., G.      Shen. (alphabetical).  Conditional Stochastic Interpolation      for Generative Learning. JRSSB. Under      review. arXiv:2312.05579. 

• Jing, B-Y, Li, T., Wang, J., Wang,      Y. (alphabetical).  Two-way Node Popularity Model for      Directed and Bipartite Networks. Journal of Machine Learning      Research. Under Revision. arXiv:2412.08051.

• Gao, Z., Huang, J., Li, T., & Wang,      X. (alphabetical).  DeepSuM: Deep Sufficient and Efficient      Modality Learning Framework. arXiv:2503.01728. 

• Niu, S., Miao, F.', Li, T.*, Huang, J.*. From      Ground to Precision: Solving Heterogeneous Semi-Supervised Volumetric      Medical Image Segmentation through Phased Learning. 

• Z. Lyu^, Li, T.^, D. Xia. Optimal Clustering of      Discrete Mixtures: Binomial, Poisson, Block Models, and Multi-layer      Networks. arXiv:2311.15598. 

• Jiang, H.', Zhang, W., Yang, L., Li, T.*, Tang,      J.*  Scalable Graph Classification without Pooling. 

• Li, T., Lyu, Z., Ren, C.', Xia, D.      rMultiNet: An R Package For Multilayer Networks Analysis. Code

• Li, T., Yu, X., Jing, B. Y..      Measuring the Clustering Strength of a Network via the Normalized      Clustering Coefficient. arXiv preprint arXiv:1908.00523.

• Li, T., Jing, B. Y. , Ying, N., Yu,      X.. Adaptive      Scaling. arXiv:1709.00566. https://doi.org/10.48550/arXiv.1709.00566 (Mphil      thesis).