AI decision support, infrastructure co-simulation, and graph-based resilience modeling.
Proposal-linked PRAIRIE Center extension of CLARA that connects county-scale simulation workflows with climate-informed hazards, uncertainty-aware optimization, economic consequence mapping, and stakeholder-facing agentic AI.
Advanced agentic AI platform integrating automated infrastructure model generation, hazard data, heterofunctional graph construction, simulation tools, diagnostics, resilience metrics, and hardening strategy recommendation.
Human-centered AI decision-support platform connecting infrastructure modeling, CISCS, HFG-TK, geospatial data, simulation outputs, and stakeholder resilience questions.
A socio-technical co-simulation framework that couples infrastructure simulators, weather hazards, cascading failures, and community-level disaster impacts.
An automated toolkit that converts simulator and GIS data into annotated heterofunctional graphs for disruption propagation, accessibility degradation, and resilience analysis.
Agent-based infrastructure toolkit for modeling community resilience and coupled infrastructure behavior under disaster scenarios.
Pre-disaster risk-informed optimal restoration crew staging framework for infrastructure recovery and post-event restoration planning.
Socially weighted resilience assessment workflow for connecting infrastructure performance, community vulnerability, and recovery-relevant decision metrics.
Open-source toolkit for automatically generating Stormwater Management Models and accelerating stormwater simulation setup for resilience studies.
Enhanced model for the CLARC dataset supporting urban resilience analysis under tropical storm impact across infrastructure and community systems.
Functionality-graph methods for identifying critical assets and quantifying disruption impacts in interdependent infrastructure systems.
Socioeconomic vulnerability based resilience analysis metric for power systems and equitable infrastructure recovery analysis.
Publication-backed work on graph neural network state estimators for distribution systems, using network structure to infer system states from limited measurements.
Publication-backed work on meter placement approaches for matrix completion-based distribution system state estimators.
Publication-backed work on data-driven dictionaries that enhance compressive sensing-based state estimators.
Publication-backed work on tensor completion approaches for distribution system state estimation under sparse observations.
