Recent advances in computing and measurement technologies have steadily placed network structures at the center of many disciplines in science and engineering. In many applications, researchers are dealing with multiple networks; either as the result of temporal evolution of the data generating process, or as the result of a mixture in the data generating process. This project aims to develop effective solutions to novel problems arising in the analysis of multiple and time evolving network structures. To accomplish this objective, (a) new statistical models are introduced, together with (b) a systematic investigation of the computational issues involved in estimating their dynamics and structures. Particular emphasis is placed on new theoretical techniques and computational tools for network problems. The research targets open problems in many fields, including biomedical and social science research, where network modeling and analysis plays an exceedingly important role.
This research aims to develop statistical models and address related computational issues for large complex data sets with network structure that would aid researchers in the biomedical and social sciences to gain the necessary insights and form hypotheses to follow-up. Further, the developed methodology will be integrated into the curriculum of statistics at the UM and form a key component for dissertation projects. The results will be disseminated through an open software tool and presentations at conferences and workshops from the investigators.
