Date and Time: 01/31/2025, 15:00--16:00

Location: https://gsumeetings.webex.com/gsumeetings/j.php?MTID=mcbe9f3f93f34c162dc524b9004aa2e72

Statistics Seminar: Distribution-on-scalar Single-index Quantile Regression Model for Handling Tumor Heterogeneity

Speaker: Dr. Chao Huang, University of Georgia

Speaker's website: https://chaohuang.uga.edu/

Title: Distribution-on-scalar Single-index Quantile Regression Model for Handling Tumor Heterogeneity

Abstract: This talk introduces a distribution-on-scalar single-index quantile regression modeling framework to investigate the relationship between cancer imaging responses and scalar covariates of interest while tackling tumor heterogeneity. Conventional association analysis methods assume the imaging responses are well-aligned after some preprocessing steps. However, this assumption is often violated in practice due to imaging heterogeneity. Although some distribution-based approaches are developed to deal with this heterogeneity, major challenges have been posted due to the nonlinear subspace formed by the distributional responses, the unknown nonlinear association structure, and the lack of statistical inference. Our method can successfully address all the challenges. We establish estimation and inference procedures for the unknown functions in our model. The asymptotic properties of both estimation and inference procedures are systematically investigated. The finite-sample performance of our proposed method is assessed by using both Monte Carlo simulations and a real data example on brain cancer images from TCIA-GBM collection.

Speaker's biography: Dr. Chao Huang is Assistant Professor in  Department of Epidemiology & Biostatistics, College of Public Health, University of Georgia. He received  his PhD in Biostatistics  from University of North Carolina at Chapel Hill  in 2019. Dr. Chao Huang's research interests mainly focus on statistical learning of large-scale biomedical data including clinical, imaging, and genomic data. His research aims to develop novel statistical methods and machine learning (deep learning) algorithms for analyzing data with complex structures, including high dimensional data, functional data, manifold data, and data with heterogeneity. These statistical methods and computational tools can help us understand the disease progression and improve clinical trials for treatment and early prevention. Some projects that he is currently working on are big data integration, manifold data analysis, functional data analysis, imaging heterogeneity, imaging genetics, and deep learning. Chao is also interested in some public datasets including Alzheimer’s Disease Neuroimaging Initiative [ADNI], The Osteoarthritis Initiative [OAI], UK Biobank [UKB], Adolescent Brain Cognitive Development [ABCD], and Human Connectome Project [HCP].

Host: Yichuan Zhao (yichuan@gsu.edu)