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Nominated by the University of Washington as an outstanding Ph.D. thesis
Demonstrates how a diffusion-based network can be controlled or observed by external entities in presence of randomness in the network’s structure or the underlying observation and control
Presents results on the identification of dynamic networks, which involves observing the behavior of networks with the aim of identifying their internal structure
Analyzes distributed optimization models and their convergence properties for energy management and optimization
This interdisciplinary thesis involves the design and analysis of coordination algorithms on networks, identification of dynamic networks, and estimation on networks with random geometries with implications for networks that support the operation of dynamic systems, e.g., formations of robotic vehicles, distributed estimation via sensor networks. The results have ramifications for fault detection and isolation of large-scale networked systems and optimization models and algorithms for next generation aircraft power systems. The author finds novel applications of the methodology in energy systems, such as residential and industrial smart energy management systems.