Academic Report of School of Mathematical Sciences [2026] No. 096

(Series Report for High-Level University Construction No. 1355)

Title：Analysis of UK Biobank data through distributed regression with fused structure identifies the occipital pole and temporal lobe for cognition

Speaker：Associate Professor Li Ting (Southern University of Science and Technology)

Time：14:00-15:00, Oct. 20, 2026

Location：Room 633, Huiyan Building, Yuehai Campus

Abstract: Assessing the relationship between whole brain imaging and neurological phenotypes is crucial to understanding human behaviors. However, the large size and complexity of whole brain data present unique challenges. In an effort to understand how brain regions react to cognitive demands through the analysis of large-scale UK Biobank whole brain data of over 50,000 individuals, we introduce distributed tensor regression via fused structure. This method effectively reduces the computational load through distributed computing, while preserving the intrinsic piece wise smoothness structure in the analysis of neuroimaging data. A novel objective function and communication strategy between distributed machines are constructed to ensure that local smoothness in voxel coefficients is maintained in distributed computing. Our data analysis consistently identifies the role of the areas in the occipital pole and temporal lobe in three touch screen cognitive tasks -- time to complete pair-matching round, duration to complete numeric path and numeric memory. While similar findings have been reported from fMRI studies, it is the first time such results have been identified using MRI with a large sample size.

Speaker Profile：Li Ting is a Research Fellow and Associate Professor in the Department of Statistics and Data Science at Southern University of Science and Technology. He has led General Projects and Young Investigator Projects funded by the National Natural Science Foundation of China and has been awarded a national-level high-level talent program (Young category). He received his bachelor’s degree from Zhejiang University and his Ph.D. from the Department of Mathematics at the Hong Kong University of Science and Technology, and completed his postdoctoral research in the Department of Biostatistics at Yale University. He previously served as an Assistant Professor at the Hong Kong Polytechnic University. He has long been engaged in statistical research on complex data, as well as large-scale and generative models, focusing primarily on network-structured data, genetic association analysis of complex phenotypes, and analysis of human brain imaging data. He has published 20 papers in core journals in statistics and biostatistics—including Annals of Statistics, JASA, JMLR, Annals of Applied Statistics, Genome Research, Human Brain Mapping, and ICML—as well as at leading machine learning conferences.

Faculty and students are welcome to attend!

Invited by: Hu Xianghong

School of Mathematical Sciences

October 19, 2026