NRT-DESE: Preparing Resilient and Operationally Adaptive Communities through an Interdisciplinary, Venture-based Education (PROACTIVE)

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This National Science Foundation Research Traineeship (NRT) award to Clemson University will respond to the urgent need for professionals capable of crossing disciplinary boundaries to assess technological and societal risks, to communicate those risks to decision makers, and to devise strategies that improve community resilience to natural or man-made disasters. The last five decades have seen a sharp increase in the frequency and impact of natural hazards and a significantly higher risk of man-made disasters, particularly since 9/11. Predicting and mitigating such extreme events is difficult due to interactions among infrastructure systems with complex, poorly understood feedback loops. Society needs professionals who can conceptualize complex systems where physical, cyber, and human infrastructure systems converge and who can transform this conceptual understanding into reliable computational models that are validated by data. Moreover, these professionals must be equipped with skills to effectively communicate with their peers in other disciplines and with decision and policy makers to ensure cohesion between science and policy. This NRT award will address these challenges through curriculum development, transformation in graduate education, and research with societal impact. The project anticipates training fifty-two (52) MS and PhD students, including twenty-six (26) funded trainees, from a variety of science and engineering disciplines related to model- and data-enabled infrastructure resiliency. This project envisions a new paradigm of graduate education conducive to training STEM professionals who are transdisciplinary system thinkers capable of crossing disciplinary boundaries and working in a dynamic network of continuously learning individuals and evolving knowledge. It represents a transformation in graduate education through the creation of collaborative research communities with strong peer-learning aspects, resulting in a local scientific community that enables students to learn the ?business? of science (networking, collaboration, communication, etc.). The NRT award will promote an agile, adaptive curriculum structure responsive to the changing needs of students through the development of a modular, personalized training program. It will enhance students? ability to apply academic research to complex, real-world problems with an awareness of societal impacts via a uniquely integrated research, training, and outreach program that studies infrastructure vulnerabilities that disproportionately affect low-income regions. Developed within a logic framework and with a thorough, research-driven evaluation plan, this training program will be reproducible on a larger scale. Student and faculty teams will conduct research in three core model engineering and data science areas: (1) integrating models to models, (2) incorporating data into models, and (3) communicating model predictions to decision makers. Their work in each of these areas will allow them to highlight key model/data science issues, understand how these issues translate to societal impacts caused by vulnerabilities in infrastructure systems, and develop solutions to mitigate damage caused by potential infrastructure vulnerabilities. The research on infrastructure resiliency will result in new approaches for modeling and analyzing coupled systems, enabling scientists and decision makers to come together to better understand interdependent infrastructure systems and their uncertainties.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.