NASA Jet Propulsion Laboratory

Data Science for Scientific Discovery: Machine learning to evaluate the risk and resilience of societal critical infrastructure to space weather

This student team will work to explore the characteristics of space weather impacts on critical infrastructure, specifically the power grid and communications systems. This student team will work to utilize a machine learning model to predict outcomes in various cases and identify non-obvious connections. This student team will work to integrate data from the solar wind, geospace environment, and the critical infrastructure to study the systems effects. This student team will work to apply extreme value analysis (estimate of 1/100 year conditions), network analyses (understand the connectivity of the critical infrastructure), and machine learning (predictive capabilities). The outcome this student team will work to accomplish is to create a framework for studying risk and resiliency of a space weather natural hazard on a certain critical infrastructure. The student team will work to accomplish this outcome via the following programmatic milestones and processes: - Create analysis ready datasets - Development plan/approach -Extreme value analysis 1/100 year estimates -Network Analyses initial results -Machine learning initial results -A report out and conference/workshop presentations