I'm a disaster analyst and data visualization enthusiast who works with data to inform risk reduction and recovery decisions. In my research, the communities that I organize, or with the students I advise, I value cultivating open and understanding environments to bring different perspectives together.
I develop statistical models to quantify a disaster's impact to inform risk reduction and recovery decisions.
My research focuses on three main tenets, which are especially important as we enter this data-driven age in disaster management.
- Usable outputs. I have a specific and realistic decision and end-user in mind when developing a model.
- Flexible models with limits. Disasters are very place-based. Furthermore, statistical models developed in one place are not expected to perform as well if ported over to an entirely different place. I develop frameworks for models to be implemented in specific locations and try my best to be clear about the limits of these models.
- Thoughtful metrics. The choice of metric is important not just from a usability point of view (i.e. can it be used to make a decision) but also determines what type of plans are put in place. Metrics that predicate disaster aid shape how well (or not well) different groups recover. For examples, see my blog post or this code switch episode.
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I often think about how my work will leave an impact.
At a high-level, I hope my research leads to a more just society. On the day to day, I try to create a warm, inclusive environment with the
← organizations I lead, students I advise, and classes I teach.
Half the work of being a researcher is sharing it with those who use it.
I create content for academics, policymakers, or audiences generally interested in disasters through
← my publications, presentations, & visualizations