CAREER: Robust Brain Imaging Genomics Data Mining Framework for Improved Cognitive Health

这个CAREER项目的目标是识别和建立一个新的健壮的数据挖掘框架，以便更好地建模、理解和分析脑成像基因组学数据，该框架结合了稀疏性诱导学习模型的概念和新的、更有效的计算算法。该项目提出的研究具有创新性，不仅对促进新数据挖掘技术的发展至关重要，而且对解决脑成像基因组学中出现的科学问题也至关重要，并极大地支持了最近由美国政府公布并成为国家目标的brain计划。与该项目的研究相结合的教育目标是创建和广泛传播新的课程和K-12外展材料，这些材料既关注大规模、异构模态和高维数据处理的挑战，也关注缓解这些挑战的强大数据挖掘技术背后的原则。该项目侧重于设计有原则的数据挖掘算法，用于分析多模态脑成像基因组数据，以产生从基因到脑功能和表型结果的机制理解。特别感兴趣的是(1)具有线性收敛算法的大规模非凸稀疏学习模型，(2)线性计算成本多任务多维数据集成算法，以及(3)大规模脑成像基因组学研究中的评估和验证。该项目的研究将使新的计算应用在大量的研究领域。作为该项目的一部分开发的教育材料将使K-12学生体验到机器学习和数据挖掘领域的一些有趣主题，同时向学生传达他们的数学和科学课程与工程未来的相关性。

The goal of this CAREER project is to identify and establish a new robust data mining framework for better modeling, understanding and analyzing brain imaging genomics data that combine the concepts of sparsity-induced learning models and new and more efficient computational algorithms. The proposed research in this project is innovative and crucial not only to facilitating the development of new data mining techniques, but also to addressing emerging scientific questions in brain imaging genomics, and to greatly supporting the BRAIN Initiative which has recently been unveiled by the U.S. Government and become a national goal. Integrated with the research in this project are the educational goals to create and broadly disseminate new curricular and K-12 outreach materials that focus both on the challenges of large-scale, heterogeneous-modal and high dimensional data processing and on the principles behind the robust data mining techniques for alleviating them.This project focuses on designing principled data mining algorithms for analyzing multi-modal brain imaging genomics data to yield mechanistic understanding from gene to brain function and to phenotypic outcomes. Of particular interests are (1) large-scale non-convex sparse learning models with linear convergence algorithms, (2) linear computational cost multi-task multi-dimensional data integration algorithms, and (3) evaluation and validation in large-scale brain imaging genomics studies. The research in this project will enable new computational applications in a large number of research areas. The educational materials developed as part of this project will give K-12 students a taste of some of the many fascinating topics in the machine learning and data mining fields while communicating to students the relevance of their mathematics and science classes to futures in engineering.