Collaborative Research: New Statistical Learning for Complex Heterogeneous Data

该项目专注于几个重要且具有挑战性的问题，涉及从医学成像和社交网络中产生的复杂的异质数据。主要目标是开发强大和创新的统计机器学习方法和工具，能够灵活地对图像中的信号异质性进行建模，将成像数据与多模式和空间分布的数据相结合，并解决网络数据的异质性。研究和教育的综合计划将在许多不同的领域产生重大影响，如生物医学研究、基因组研究、环境研究、公共卫生研究以及社会和政治科学等。该项目还将促进跨学科研究和与来自不同领域的科学家的合作。该项目将通过利用普通人群的个体差异和整合多种成像信息来源来提高疾病诊断和治疗结果的预测准确性，从而在异质性学习和建模方面取得实质性进展。此外，该项目通过利用节点协变量信息来分析异质网络数据，开发了创新的无监督学习方法。研究计划的每个组成部分都包含广泛的主题，从方法学和计算发展到在现实世界问题中的应用。具体地说，PI研究对象不同的标量-图像回归模型，以纳入脑成像数据的异质性差异、乳腺癌成像数据的多维张量学习方法、网络数据的灵活高斯图形模型，以及通过网络链接的异质数据的新的集群框架。此外，先进的优化技术、算法和计算技术的发展将适用于大规模异质数据产生的许多实际问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.