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Big Data, Downscaling, and Interdisciplinary Approaches to Understanding Extreme Events

Banff International Research Station for Mathematical Innovation and Discovery

In a world with rapid climactic change and intensifying extremes, it becomes increasingly important to improve predictive modeling. The Sustainability and Data Sciences Laboratory (SDS Lab) at Northeastern University in Boston, MA has taken an interdisciplinary approach to understanding interconnected complex systems using a combination of mathematical, scientific, engineering, and computational tools. Through the use of machine learning, statistics, physics, and nonlinear dynamical methods—such as chaos and complex networks—we have developed enhanced quantitative understandings of extremes and change in a way that can be translated so as to inform policy and create more resilient social systems. The focus of our research centers around risk and adaptation, resilience of critical infrastructure and lifeline networks, and sustainability of ecosystems and resources. This presentation provides an overview of the research done at the SDS Lab, including methodologies, the use of interdisciplinary approaches, and important trends and outputs being observed in our results.

Mathematics; Statistics; Computer science; Data mining

video/mp4

Author affiliation: Northeastern University

2018-04-29

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/65690

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/