Complex Networks and Inference

Complex networks and information seeks to understand mathematically how fundamental approaches to information exchange influence overall network and system performance and behavior. From this understanding we wish to develop strategies to assess and influence the predictability and performance of heterogeneous types of networks and information systems that must provide reliable transfer of data in dynamic and high interference environments. The goal is to develop approaches to describe information content, protocol, policy, structure, and dynamic behavior by mathematically characterizing network and information systems so that we may understand fundamental limits in system behavior, inference in the presence of measured data, and design of secure and fault-tolerant information systems. This talk will highlight our recent work in the areas of geometry processing with uncertainty, active learning methods for human-in-the-loop tasking, robust online subspace tracking under changing observation dimensionality, and localization under adversary misdirection.
February 19 @ 12:30
12:30 pm (1h)

Discovery Building, Orchard View Room

Lauren Huie, Lee Seversky