Intellectual Merit: The objective of this research is to develop a general and flexible framework for automatic inference in networked systems that can admit wide applications and provide desired tradeoff between accuracy and efficiency, while allowing simple and distributed implementation. Given that exact interference is generally intractable whereas the widely-adopted mean field approach is limited in inference quality, the approach of this research is to explore the structured variational methods to bridge the gap and allow flexible tradeoff among various potentially conflicting goals, which can be tuned according to the needs and available resources of applications. Various models, dependency structures, and inference algorithms will be considered in this variational inference framework, together with efficient distributed implementation. An associated research line is to design automatic clustering schemes that ideally can match the needs of inference tasks while being easy to implement in a distributed manner. The interaction between the clustering and inference layer will be explored to achieve better performance and adaptivity.
Broader Impacts: This project contributes fundamental concepts, designs, analytical tools and software towards building a general and systematic framework for automatic reasoning in large-scale networked systems, such as sensor networks and cyber-physical systems. The expected outcome can be applied in diverse areas including security, transportation, medicine, energy, manufacturing and many others. Through suitable modification, the proposed distributed clustering algorithms also admit wide applications in various areas of network science and engineering. The multi-disciplinary nature of the proposed research also lends itself to cross-disciplinary education and well-rounded training of a future IT workforce. Special efforts are taken to encourage the participation of students from underrepresented groups. Various channels are pursued to disseminate research findings, developed software, and educational materials to industry and the broader public.
