NSF Convergence Accelerator: Symposium on Predicting Extremes by Data-Driven Analytics

University Of Maryland, College Park, College Park MD

Investigators

Abstract

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. This symposium on Predicting Extremes by Data-Driven Analytics will help identify topic areas for new tracks in the NSF Convergence Accelerator. Extreme events and associated hazards in natural, commercial, and security systems underlie the most devastating catastrophes in society. The societal risks arising from extreme events are very high both in terms of likelihood and impact on society. The increasing impact of disasters and the consequent risks have led leading organizations and institutions around the world to develop new approaches to mitigate the impacts and develop strategies for resilience. Improved predictive capability for extreme events is a critical scientific need in order to achieve effective disaster risk assessment, which is a product of three factors: the probability of the underlying events, vulnerability of the system and consequences therein. The understanding and modeling of extreme events contributes towards developing the probabilities of occurrence. This symposium will bring together participants from academia, government and industry to develop data-driven analytics as a pathway for predicting extreme events in natural and anthropogenic systems, including applications in terrestrial and space weather, finance and economics, and cybersecurity. The symposium is planned as a 3-day event to be run as a virtual meeting. It will emphasize participation by researchers and students from underrepresented communities that may be especially vulnerable to the effects of extreme events. An essential step toward achieving better predictability is uncertainty quantification, which is an inherently interdisciplinary endeavor, requiring convergence across multiple disciplines and participation by multiple stakeholders across academia, industry and government. A Convergence Accelerator track on this theme would benefit multiple sectors. Developing a framework for predicting extremes requires the harnessing of massive data from a variety of sources. The symposium will discuss the idea of developing a common platform for stakeholders in government, industry and nonprofits as part of this potential new track in the Convergence Accelerator. The themes of the symposium are of general interest due to their potential for high impact on society. A symposium proceedings will be produced, which will provide broad exposure to the potential outcomes of convergence research in this topic area. 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.