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$2.5M NSF grant for Critical Infrastructure Resilience

July 28, 2016

ECE professors Mario Sznaier, Octavia Camps, Ali Abur, & Edmund Yeh, MIE assistant professor Jacqueline Griffin, CEE professor Jerome Hajjar, COS professor Lisa Feldman Barrett, CCIS professor Stacy Marsella, and Kostas director Peter Boynton were awarded a $2.5M NSF grant for the "Identification and Control of Uncertain, Highly Interdependent Processes Involving Humans with Applications to Resilient Emergency Health Response".

Abstract Source: NSF

The growing pace of urbanization, particularly along coasts, in conjunction with non-robust infrastructures with poorly understood interdependencies, will make major weather events, intensified by warming oceans and rising sea level, more likely to turn into disasters in the near future. Superstorm Sandy provided a particularly poignant example of this situation. This Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) Type 2 grant will pioneer a new approach to enable communities to withstand and bounce back quickly from hazards. This will be accomplished through a new paradigm of proactive resiliency through "prediction, intervention and adaptation," as opposed to the current reactive cycle of discovery, recovery and redesign. A unique feature of the this research is the integration of engineering (electrical, industrial, civil), computer science and social science strategies for achieving resilience over the short and long term. Education is proactively integrated into this project, starting with summer STEM courses for urban middle school students and continuing at the college level with a multi-disciplinary program that uses the central metaphor of resilient communities to link a full range of distinct subjects. This approach will help broaden participation of underrepresented groups in research and substantially transform engineering and social sciences education.

This research seeks to develop a comprehensive framework for designing and operating resilient communities by modeling them as partially engineered networked cyber-physical-human systems. The ultimate goal is to develop a synthesis framework for such systems, capable of guaranteeing minimum levels of performance and rapid recovery of functionality in the face of disruptions. This goal will be accomplished by recasting the problem in the context of data-driven identification and control of processes represented by dynamical graphical models, where both the nodes and interconnecting edges are dynamical systems that encapsulate the cyber-physical and human aspects of the problem. The approach allows for treating all aspects of the problem in a unified way, leveraging recent advances in optimization, networked control and agent based modeling to obtain scalable, computationally tractable solutions. Salient features of this framework include the integration of agent based modeling of human responses into a chance constrained optimization framework that identifies strategies to optimize resilience, the use of data driven models to extract actionable information from extremely large data sets, and purposeful manipulation of system-wide components, enabled by novel fuse-based design techniques. The resulting framework will be tested using a scenario involving four systems critical to maintaining minimum levels of emergency medicine: power, communications, critical goods supply chain and built environment.