This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)
Objective: The objective of this program is to develop a new framework for studying distributed inference with dependent observations. A broad range of issues will be addressed under this program, ranging from distributed detection, distributed estimation, robust inference, to asymptotic performance in a large system setting.
Intellectual merit: The intellectual merit is in the unifying nature of the proposed framework. Existing results in distributed inference with dependent observations are rather fragmented. The proposed framework unifies these isolated studies and provides intuition behind many of the observations reported in existing literature. Such intuition, as well as the framework itself, can be applied and adapted to various distributed inference in complex systems. The transformative nature of the proposed research lies in its potential to connect existing isolated studies, and to identify and resolve distributed inference problems that were never before addressed, thereby providing clear design principles for distributed inference systems under realistic dependent data models.
Broader impacts: The broader impacts are multifaceted. The study sheds light on the fundamental cause of difficulty in dealing with dependent observations in distributed systems; it provides useful insights that may lead to research advances in areas beyond that of distributed inference. The research results are to be disseminated to both the research community through publications and tutorial presentations and to graduate students through curriculum development. Encouraging and facilitating graduate student participation in professional meetings and conferences and recruiting undergraduate students in research projects which will help instill enthusiasm and foster their interest in scientific activities.
