Projects

This study aims to address gaps in understanding regarding the effective implementation of AI into emergency dispatch infrastructure by identifying barriers to AI implementation in dispatch systems and addressing potential biases by controlling for the identified obstacles. By leveraging large language models, the study seeks to take the insights gained from regional dispatcher interviews and develop advanced bots capable of responding to non-emergency events with human-like text (e.g., creating prompts that mimic operator responses to gather detailed information from callers), while actively mitigating potential biases present in the model and evaluating its effectiveness via algorithmic fairness testing metrics.

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.

Who responds (and does not respond) to crowdsourced shaking intensity data, Did You Feel It?

In a collaboration between the USGS and the Natural Hazards Center at CU Boulder, we take a human-centered design approach to co-design the update of USGS earthquake impact products to be more actionable and accessible.

From remote sensing to crowdsourcing, an overwhelming amount of building damage data is produced after disasters. While this amount of data signifies impressive technological progress, it does not necessarily mean that all building damage data is usable for post-earthquake decision-making. This research applies geostatistical methods to integrate multiple sources of data to produce rapid estimates of building damage, primarily to support regional decision making.

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.

As a collaboration between the Stanford Urban Resilience Inititative, Heidelberg University, The World Bank, and Humanitarian OpenStreetMap Team, this project compares three approaches to crowdsourcing building damage data using satellite imagery to inform disaster response decisions.