What we do:
Our research group studies how communities can withstand and recover from natural hazards and climate change. We focus on essential service systems that support daily life—electric power, transportation, communications, food, and education—and investigate how they fail during disasters and how they can be made more resilient. A central theme of our work is advancing the theoretical foundations of risk and resilience. We build theory on what it means to be resilient across ecological, engineering, and social domains. This conceptual work provides a rigorous basis for designing and evaluating interventions that are not only efficient but also equitable.
For example, ecologists often describe a forest that burns and then regenerates as grassland as resilient, since it has adapted and persisted through change. But how should we apply that idea to infrastructure and community systems? If a power grid fails and is rebuilt smaller but more reliable, or if a community adapts by relying more on informal aid networks, do we consider that resilience?
How we do it:
We build on these foundations by developing and applying methods from statistical machine learning, optimization, simulation, and geospatial analytics to predict hazard impacts, simulate disruptions, and optimize recovery. These tools are explicitly designed to account for differences in vulnerability and access, ensuring that resilience strategies strengthen communities while addressing long-standing inequities.
Current Research Areas
We advance the theoretical foundations of risk and resilience to make them measurable, comparable, and decision-relevant. While resilience is often invoked across disciplines, in practice it has lacked precise definitions and operational metrics.
Risk and Resilience Theory
For example, we demonstrate how overemphasis on rapid recovery can create resilience traps in power restoration, and how ecological models—where systems may shift into alternative stable states—offer insights for infrastructure and community resilience . More broadly, we contribute to the study of systemic risk, defined as the cascading and emergent consequences that arise when interconnected infrastructure and social systems fail . By ensuring that predictive modeling, optimization, and simulation are firmly anchored in these theoretical concepts, we provide decision-makers with tools to design interventions that are not only efficient but also adaptable and equitable.
Recent (published) examples:
- Quantifying maladaptive resilience traps (Nature Communications)
- Two working papers on the same idea (feel free to reach out for a pre-print!)
Predictive Modeling of Hazard Impacts
We work in predictive modeling to anticipate how natural hazards disrupt communities and essential services. Our models integrate weather, infrastructure, environmental, and demographic data to forecast power outages, characterize vulnerability, and identify where assistance will be needed before and during disasters. These tools are designed to reveal the pathways through which disruptions propagate—and to surface the inequities embedded in those pathways—providing planners, utilities, and emergency managers with early, actionable insight into emerging risks.
Recent (published) examples
- How do electric vehicles increase access risk during long-duration blackouts? (npj Sustainable Mobility and Transport)
- One more in press (11/11/2025)
Simulation of Access to Essential Services
We work in simulation to understand how households and communities experience disruptions to essential services such as mobility, healthcare, food access, and education. Our models quantify how daily life changes when power fails, roads flood, or infrastructure degrades, and how new technologies like electric vehicles reshape resilience by coupling mobility with electricity supply. By representing access at the human scale, these simulations highlight the conditions that make disruptions more burdensome for some populations than others and inform strategies to promote equitable resilience.
- Links to dashboards and publications to come!
Optimization for Disaster Response and Recovery
We work in optimization to design decision-support tools for allocating limited resources during disasters. Our models help utilities schedule power restoration crews, guide where to position mobile communication assets, and evaluate policies that trade off speed, cost, and fairness in recovery. By embedding multiple objectives and value structures directly into these optimization frameworks, we support decisions that recognize both efficiency and equity, and provide insight into how alternative priorities shape who benefits first during crises.
- Links to come