Projects

As natural hazards are becoming more frequent, insurance companies are withdrawing from high-risk areas in the United States, leaving homeowners without adequate financial protection. While parametric insurance presents a potential solution by offering rapid payouts based on predefined hazard triggers, its adoption by communities and households remains limited due to multiple factors, including potential misalignment between what payouts are provided and community expectations. This study aims to explore the feasibility of reciprocal parametric insurance at the community level, working with households, business owners, and city planners in Sarasota, FL.

In the immediate aftermath of an earthquake, rapid information about regional impact (e.g., building damage, fatalities, financial loss,etc.) is crticial for both short-term impact assessement and long-term recovery planning. However, traditional methods such as buliding damage surveys do not satsify the urgent data needs that arise in immediate post-earthquake scenarios. This collaborative project seeks to develop a framework that integrates high-resolution commerical satellite data, machine learning, and loss estimation to rapidly generate regional earthquake impact metrics. Findings from the project will promote actionable post-earthquake impact assessment while also supporting long-term recovery planning in the wake of devastating seismic events. The project is supported by the NASA CESRA program.

This project supports research focused on understanding population mobility subject to repeated flooding in regions that are historically unprepared to cope with such events. Repeated, low-attention flood disasters do not receive widespread media coverage compared to larger, catastrophic ones. Low-attention flood events are currently understudied, but their cumulative impacts are likely to compound underlying causes of risk, inequality, and poverty. Furthermore, there is not a good understanding of how they contribute to people's decisions to evacuate, return, or permanently move. By filling the knowledge gap, this study aims to better inform local and regional policymakers responsible for designing policies for mitigation strategies and aid distribution before, during, and after these events.

The Himalayan Climate Data Field Lab is a month-long, flexible unconference that will gather scholars, practitioners, activists, community leaders, and storytellers to examine the ways that climate change data and information infrastructures shape adaptation and mitigation in the Himalayan region. Join the Field Lab to co-design, test and produce new ideas, analytic tools, maps, sensing technologies, data protocols, artistic pieces and communication products that address climate change and its impacts, with the aim of creating a more equitable and pluralistic data landscape in the Himalayan region.

A community-curated, open-access, evolving platform that aggregates global landslide inventories and related geospatial data, providing detailed metadata for each dataset. The platform serves as a central hub for high-quality, globally sourced landslide and geospatial data, supporting the development and benchmarking of reliable, scalable, and generalizable AI models.

While landslides are pervasive across the U.S., their specific social, economic, and ecological impacts remain underappreciated due to their localized and episodic nature, co-occurrence with other hazards, and variability across broad regions. In this study, we leverage generative AI to develop an agentic framework capable of extracting and synthesizing diverse web sources to capture the full range of direct and indirect landslide impacts.

Informatics for Equitable Recovery is a transdisciplinary research collaboration that brings together data scientists, engineers, social scientists, and civic organizations to improve post-disaster information systems and decision support tools.