This project focuses on several important and challenging issues concerning complex heterogeneous data that arise from medical imaging and social networks. The major goals are to develop powerful and innovative statistical machine learning methods and tools that are able to flexibly model signal heterogeneity across images, integrate imaging data with multimodal and spatially distributed data, and tackle heterogeneity of network data. The integrated program of research and education will have significant impacts in many different fields such as biomedical studies, genomic research, environmental studies, public health research, and social and political sciences, among others. The project will also stimulate interdisciplinary research and collaboration with scientists from disparate fields.
This project will lead to substantial advancement in heterogeneity learning and modeling through exploiting individual variation from the general population, and integration of multiple sources of imaging information to enhance prediction accuracy for disease diagnoses and treatment outcomes. In addition, this project develops innovative unsupervised learning methods through utilizing node covariate information for analyzing heterogeneous network data. Each component of the research plan contains a broad range of topics, from methodological and computational development to applications in real world problems. Specifically, the PIs study subject-variant scalar-on-image regression models to incorporate the heterogeneity variation for brain imaging data, multi-dimensional tensor learning methods for breast cancer imaging data, flexible Gaussian graphical models for network data, and a novel clustering framework for heterogeneous data that are linked by networks. Furthermore, the development of advanced optimization techniques, algorithms and computational technologies will be applicable to many practical problems arising from large-scale heterogeneous data.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
