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

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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.