EAGER-DynamicData: Generative Statistical Modeling for Dynamic and Distributed Data

该项目将为分散数据开发一种新的生成建模范式。在大数据时代，庞大的数据量、高种类和高速度对技术提出了新的挑战。在受分布式数据收集、通信网络和分散计算平台限制的统计学习领域，可以得到实质性加强。例如，在许多工程应用中，数据的大小可能如此之大，以至于一台计算机无法处理。然而，典型的学习方法期望训练数据是静态的，并且可以由一台计算机处理。生成建模框架已被证明在整合先验知识和捕获驻留在结构化域的数据之间的统计依赖性方面是有效的，例如，信号的时间序列和图像的空间网格。这些优点非常适合自然现象产生的数据和工程系统的需要。该项目通过推进由数据级学习层和模型级学习层组成的多尺度统计建模，解决了存储和通信容量的限制，以及实时分析的速度要求。支持2名博士生进行工程与统计学交叉研究。他们将开发核心方法，以及在广泛的工程学科中有用的实用算法和工具。该项目的目标是为受通信网络和计算平台分散架构约束的分布式和动态数据提供统计学习的新方法。特别是，从分布式和动态数据中学习的多尺度统计建模将被推进。在数据级，建模是在分散的计算站点上执行的。这些模型作为高度紧凑的数据描述，保留了学习的关键信息。为了巩固在分布式站点上获得的模型，只有这些模型被传送到一个主计算机节点。在主节点，直接对模型进行学习，而不重新生成数据。将对数据与CPU、存储等各种计算资源之间的权衡进行综合调查。这个项目是变革性的，因为问题的基本性质，问题的不同寻常的表述，以及跨学科的方法。通常直接从数据中学习的模式转变为多尺度学习，统计模型本身成为学习对象。将开发和提供一套综合统计学和工程学方法的工具。

This project will develop a novel paradigm of generative modeling for decentralized data. In the big data era, the enormous volume, and high variety and velocity of data raise new technical challenges. What can be substantially strengthened is in the area of statistical learning under the limitations imposed by distributed data collections, communication networks, and decentralized computing platforms. As an example, the size of the data can be so large in many engineering applications that a single computer cannot handle. Typical learning methods, however, expect training data to be static and can be handled by one computer. The generative modeling framework has been shown to be effective in incorporating prior knowledge and capturing statistical dependence among data residing on structured domains, e.g., time sequences for signals and spatial grids for images. These advantages suit well with data arising from natural phenomena and the needs of engineering systems. The project addresses constraints in storage and communication capacity, as well as the speed requirement of real-time analysis by advancing multi-scale statistical modeling consisting of a layer of data-level learning and a layer of model-level learning. Two doctoral students will be supported to conduct research at the interface of engineering and statistics. They will develop core methodologies, as well as practical algorithms and tools useful in a wide range of engineering disciplines.The goal of this project is to propose new approaches in statistical learning for distributed and dynamic data subject to constraints of communication networks and the decentralized architecture of computing platforms. In particular, multi-scale statistical modeling for learning from distributed and dynamic data will be advanced. At the data-level, modeling is performed at decentralized computing sites. These models serve as a highly compact description of the data, retaining key information for learning. To consolidate the models acquired at distributed sites, only the models are communicated to a primary computer node. At the primary node, learning is performed directly on the models without regenerating data. An integrated investigation will be conducted on trade-offs between data and various computing resources such as CPU and storage. This project is transformative because of the fundamental nature of the problems, the unusual formulation of problems, and the interdisciplinary approaches. The usual paradigm of learning directly from data is transformed to multi-scale learning where statistical models become learning objects themselves. A suite of tools integrating methodologies in statistics and engineering will be developed and made available.