CAREER: Bridging the Data-Model Gap -- Leveraging Surveillance for Propagation Mining over Networks

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The long-term goal of the proposed research is to understand, manage efficiently, and utilize dynamical mechanisms like propagation on large networks, occurring across natural, social, and technological systems. Understanding such processes enables us to manipulate them for our benefit. Propagation and networks have numerous applications in areas as diverse as public health and epidemiology, systems biology, cyber security, viral marketing and social media---hence progress in this domain promises scientific, commercial and social benefits. The proposed research aims to develop extensible, data-driven frameworks for propagation-related problems getting more implementable and generalizable tools. The PI's investigations will lead to novel mining and learning problems and scalable techniques which can be applied to massive datasets, helping make more informed choices for future. Educational activities are also closely integrated with this research agenda, including integrating research with education through courses, tutorials, and other university programs. Most current work in propagation mining assume the existence of well-calibrated models. Performing model calibration is typically very expensive, and not robust. Indeed, in many situations it is not clear which parameterized model should be calibrated. However there is an increasing availability of surveillance data like online media and medical health records. The PI's approach is unique in the sense that the aim is to directly use surveillance data and formulate optimization problems based on the data and network together. The proposed problems include inventing data-driven immunization policies for diseases like influenza, automatically finding missing infections/activations in cascade datasets, and automatically learning graph summaries based on distributed feature representations of propagation data as well as the network. The PI proposes to develop a flexible and expressive framework for all these problems. In addition, the developed algorithms will be applied to various domains, leveraging multiple collaborations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.