Our research works to improve disaster management and responses using artificial intelligence. Our work in this area focuses on building, testing, and refining new methods and models to gather and analyze community-scale big data to generate knowledge for decision-making. Our goal is to help residents, civilian volunteers, and emergency responders better predict and cope with crises.

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This area of research aims to bring a human approach to urban resilience. We are working to improve the standard model of infrastructure resilience by creating new “people-centric” models and knowledge. Our goal is to explore the societal impacts of service disruptions, and reduce disparities of vulnerable populations in disasters.

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This research area investigates how infrastructure interacts with population dynamics after a disaster has occurred. We use digital evidence, like social sensing and digital trace data (e.g., cell-phone GPS and social media data) to create new Artificial Intelligence (AI) methods and complex network models. Our goal is to understand how failure occurs in urban networks, its “spill-over” effects, and how people respond during extreme events such as wildfires and urban flooding.

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