EAGER: Algorithm-Hardware Co-Design for Multivariate Data Analysis

这个项目的目标是开发基于统计和比较数据模式频率的多变量数据的无监督学习的新方法。将使用递归测试方法来推断多变量分布。研究人员将使用硬件-算法联合设计，在计算时间以及可处理的最大数据维度和样本大小方面实现对现有方法的定性改进。还将调查广泛使用这一方法的经济可行性。这项研究的动机是“大数据”分析的挑战，在这种分析中，高维和极大的样本量使得应用传统统计方法变得不可行。该项目中开发的新方法将应用于视频分析、下一代测序数据和微博等几个“大数据”应用。通过开发这种分析的统计方法以及定制的计算资源，使这些方法可扩展到极大的数据集，这项研究将能够更有效地利用这些数据中嵌入的丰富信息。最后，整合统计、计算和硬件专业知识的多学科方法非常适合培养下一代数据科学家。

The goal of this project is to develop new methods for unsupervised learning from multivariate data based on counting and comparing frequencies of data patterns. A recursive testing approach will be used to infer the multivariate distribution. The investigator will use hardware-algorithm co-design to achieve qualitative improvement over existing methods in computational time as well as in the maximum data dimension and sample size that can be handled. The economic feasibility of making this methodology widely available will also be investigated. This research is motivated by the challenge of "Big Data" analysis where the high dimensionality and extremely large sample size had made it infeasible to apply traditional statistical methods. The new methods developed in this project will be applied to several "big data" applications such as the analysis of videos, next generation sequencing data and microblogs. By developing the statistical methods for such analyses as well as customized computing resources to make these methods scalable to extremely large data sets, this research will enable more effective use of the rich information embedded in these data. Finally, the multidisciplinary approach integrating statistical, computational and hardware expertise is well suited for the training of next generation data scientists.