CAREER: Real-Time Sampling, Estimation, and Inference in Networked Systems

University Of Pennsylvania, Philadelphia PA

Investigators

Abstract

The Internet of Things (IoT) and social networks have provided unprecedented platforms for the generation, dissemination and collection of real-time information. The information is often governed by processes that evolve over time and/or space (for example, on an underlying network). Estimation and inference for such processes, especially in time-critical applications, require efficient real-time and adaptive sampling strategies. This project will develop theoretical foundations and algorithmic designs for real-time sampling, estimation and inference in networked systems and considers applications such as estimation and control in IoT, testing and quarantine for diseases (e.g. COVID-19), and timely detection and control of the spread of misinformation in social networks. The project is interdisciplinary and features a synergistic plan to enrich the graduate and undergraduate curriculum at the University of Pennsylvania with the goal of narrowing the theory-practice gap. It will further provide undergraduates with research opportunities, targeting in particular underrepresented minorities. The outreach plan includes partnerships between academia and industry through the NSF-funded Innovation Corps program, public literacy and engagement concerning the spread of misinformation, as well as improved STEM education and mentorship of low-income and first-generation students through the NSF-funded Upward Bound Math Science program. The overarching goal of the project is to develop foundational frameworks for real-time (sequential) sampling strategies that exploit partial information for decision making in networks. It establishes fundamental tradeoffs between information extraction and reward maximization with the former traditionally rooted in information theory and the latter in networking, and develops solutions by merging ideas from information theory, stochastic processes, network sciences, graphical models, and machine learning. 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.