Systemic Risk & Cascading Failures
We study how local disruptions spread through economies, infrastructure, supply chains, and social systems, and identify the nodes that amplify systemic risk.
Risk Science & Decision Intelligence
We combine risk science, data infrastructure, and computational modeling to understand how disruptions propagate through cities, industries, infrastructure, and economies, and to translate research into reusable datasets, models, and decision tools.
Research Themes
The first version organizes the lab around long-running research themes. Each theme can later connect publications, projects, datasets, tools, and people.
We study how local disruptions spread through economies, infrastructure, supply chains, and social systems, and identify the nodes that amplify systemic risk.
We assess the direct and indirect effects of disasters and compound shocks on industries, cities, and regional economies, and simulate recovery pathways.
We study exposure, vulnerability, adaptation capacity, and long-term resilience investments under flood, heat, storm surge, and other climate risks.
We map dependencies among transport, energy, water, communications, and supply-chain systems to analyze disruptions, bottlenecks, and substitution pathways.
We explore machine learning, knowledge graphs, and language models for risk identification, evidence extraction, scenario generation, and decision support.
We build maintainable, citable, and traceable data products to support risk research, model development, and policy assessment.
Selected Outputs
Placeholder records define how publications, reports, datasets, and tools should be presented and cross-linked.
Working paper record, details to be updated
Technical report placeholder
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Latest
The first prototype of the lab website starts with research themes, people, publications, datasets, tools, projects, and partner networks.
The lab begins defining the input-output structure, scenario configuration, and documentation model for the ARIO workbench.
The lab drafts metadata fields for dataset pages, including coverage, time span, update frequency, owner, and access mode.
The collaboration network will include universities, research institutes, government and policy partners, industry partners, and data partners.