My research focuses on data science, statistical learning and deep learning, especially on network data, biological data, brain data and imaging genetics. I am interested in developing novel and powerful statistical methodology, establishing deep statistical theory, and analyzing complex real data.
Network Data Analysis, Brain Data, Imaging Genetics, Deep Learning (GNN, Attention, Diffusion), Large Models
Machine Learning & Large Models:
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:
J. Hu, Li, T., X. Wang. (2025+). 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. (2022) Community Detection in Sparse Networks Using the Symmetrized Laplacian Inverse Matrix (SLIM). Statistica Sinica.
Jing, B. Y., Li, T., Lyu, Z., & Xia, D. (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:
M. Che, Li, T., W. Pan, X. Wang, H. Zhang. Ball Impurity: Measuring Heterogeneity in General Metric Spaces. Journal of the American Statistical Association. Major Revision.
D. Huang', J. Huang, Li, T., G. Shen. Conditional Stochastic Interpolation for Generative Learning. Annals of Statistics. Major Revision. arXiv:2312.05579.
Jing, B-Y, Li, T., Wang, J., Wang, Y. 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. 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).
Journal of the American Statistical Association; Annals of Applied Statistics; Science China Mathematics; Journal of Computational and Graphical Statistics; Biometrics; The Canadian Journal of Statistics; Computational Statistics and Data Analysis; Journal of Statistical Planning and Inference; Statistics and Its Interface; International Journal of Data Mining and Bioinformatics; The World Journal of Biological Psychiatry