Predictive Risk Investigation SysteM (PRISM) for Multi-layer Dynamic Interconnection Analysis
The natural-human world is characterized by highly interconnected systems, in which a single discipline is not equipped to identify broader signs of systemic risk and mitigation targets. For example, what risks in agriculture, ecology, energy, finance and hydrology are heightened by climate variability and change? How might risks in, for example, space weather, be connected with energy, water and finance? Recent advances in computing and data science, and the data revolution in each of these domains have now provided a means to address these questions. The investigators jointly establish the PRISM Cooperative Institute for pioneering the integration of large-scale, multi-resolution, dynamic data across different domains to improve the prediction of risks (potentials for extreme outcomes and system failures). The investigators' vision is to develop a trans-domain framework that harnesses big data in the context of domain expertise to discover new critical risk indicators, holistically identify their interconnections, predict future risks and spillover potential, and to measure systemic risk broadly.
This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity. Our team's expertise cover ecology, climate, computer science, space weather, agriculture, hydrology, finance, energy, and statistics. Our team consists of Judy Che-Castaldo from Lincoln Park Zoo, Remi Cousin from Columbia, Rajesh Gupta from UCSD, Ryan McGranaghan from ASTRA, Wei Ren from University of Kentucky, Chaopeng Shen from Penn State, Mila Sherman from Univ of Amhert, Deborah Sunter from Tufts, Lan Wang from University of Minnesota and David S. Matteson from Cornell University.