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The goal of my research is to help more people make sense of complex information, and in particular to reason about uncertainty as they use data. Information visualizations leverage perception to summarize data in a cognitively efficient format, making them popular in science, government, and the media. However, many visualizations and other data summaries fail to communicate effectively. One problem is that authors often omit uncertainty information in favor of optimizing the design of visualizations for pattern finding. As a result, conclusions from data are often believed to be more credible than they are. Other challenges arise when visualization use in the world “outgrows” the assumptions under which visualization tools and design knowledge were developed. For example, assumptions that visualizations are typically used in isolation for analysis of large datasets on desktop computers lead to a lack of sufficient tooling for helping authors negotiate design trade-offs that arise when visualizations are comprised of multiple related views, will be viewed on a range of devices, or are intended to communicate a set of specific points.

My research develops novel interactive tools and techniques that aim to extend and amplify users' abilities to reason under uncertainty when working with data. I achieve this by identifying abstractions that better align with people’s natural internal representations of complex phenomena, while remaining grounded in theories of statistical reasoning. My work has contributed techniques, theory, and systems related to uncertainty visualization, Bayesian inference, automated construction of visualizations for communication, and measurement analogies, among others.