Collaborative Research: Novel and Unified Statistical Learning Procedures for Massive Dynamic Multiple-Input, Multiple-Output Networks

该项目旨在开发一种创新、统一和灵活的方法来对大规模多输入多输出(MIMO)网络中的输入输出转换进行建模。随着现代技术收集大量高维和非均匀数据的能力，传统的技术已经变得不足以或不能根据数据对底层的MIMO系统进行建模。需要新的统计途径和方法来从复杂、大型、非均匀的网络数据集中提取知识。该项目寻求开发新的建模技术、统计学习工具和计算方法，以便从网络数据的不同方面获得知识。这一发展始于一种新的功能动态模型，该模型考虑了网络数据的内在特征，如节点延迟、与网络耦合相关的动力学等。接下来，调查人员将开发统计推断工具，以测试和识别潜在的内在网络连通性。此外，调查人员将开发用于网络状态识别和变化点检测的统计学习技术。该项目将通过提供另一种数据驱动的方法，极大地丰富分析海量网络数据的工具包。预计这些新工具将为现有问题提供新的解决方案，并为未解决的问题提供新颖和创造性的解决方案。这些研究主题解决了许多基本问题，例如，当维度(例如，节点数量)和样本大小增加时，复杂MIMO网络的动态建模、简约网络表示和网络学习。它将增强对大数据背景下现有技术的理解。更重要的是，先进的建模方法和数据分析工具将与快速实施的算法一起开发，使研究人员和从业者能够探索、理解复杂网络，并最终对其进行反向工程。研究人员将与来自不同领域的研究人员互动，以推导和验证假说，并改进实验设计，为潜在的科学发现提供对系统内部的更深层次的洞察。

This project is to develop an innovative, unified, and flexible methodology to model the input-output transformations in massive multiple-input, multiple-output (MIMO) networks. With the ability of modern technology to gather large volume of high-dimensional and inhomogeneous data, traditional techniques have become inadequate or incapable of modeling the underlying MIMO systems from the data. Novel statistical approaches and methods are needed for extracting knowledge from complex, large, inhomogeneous network data sets. This project seeks to develop new modeling techniques, statistical learning tools, and computational approaches geared towards gaining knowledge arising from different aspects of network data. This development starts with a novel functional dynamic model that accounts for inherent characteristics of network data, such as node latencies, dynamics associated with network coupling, etc. Next, the investigators will develop statistical inference tools to test and identify underlying inherent network connectivity. Furthermore, the investigators will develop statistical learning techniques for network state identification and change-point detection. This project will significantly enrich the toolkit for the analysis of massive network data by providing an alternative data-driven approach. These new tools are expected to provide new solutions to the existing problems and novel and creative solutions to unsolved problems. The research topics address many fundamental problems, for example dynamic modeling, parsimonious network representation, and network learning, for complex MIMO networks when the dimensionality (e.g., the number of nodes) and the sample size grow. It will enhance understanding of existing techniques in the big data context. More importantly, advanced modeling methodology and data analytical tools will be developed with fast-to-implement algorithms, which allow researchers and practitioners to explore, understand, and eventually reverse-engineer complex networks. The investigators will interact with researchers from different fields to derive and validate hypotheses and refine the design of experiments to provide deeper insight into system internals for potential scientific discovery.