While computers can help us manage data, human judgment and domain expertise is what turns it into understanding. Meeting the challenges of increasingly large and complex data requires methods that richly integrate the capabilities of both people and machines. In response to these challenges, my research combines methods from visualization, data management, human-computer interaction, and programming languages to enable effective methods for data analysis and communication.

In my talk, I will present new languages and models that power interactive systems for scalable data exploration. Vega-Lite is a high-level declarative language for rapidly creating interactive visualizations, while also providing a representation for tools that generate visualizations. Draco is a model of visualization design that extends Vega-Lite with design guidelines, formal reasoning over the design space, and visualization recommendation. Falcon and Pangloss enable scalable interaction and exploration of large data volumes by making principled trade-offs among people’s latency tolerance, precomputation, and the level of approximation. A recurring strategy across these projects is to leverage an understanding of people’s tasks and capabilities to inform system design and optimization.

My future research will contribute systems that automatically reason over domain-specific representations of interactive multi-view graphics, visualizations of large data, uncertainty representations, and data analysis. This reasoning can inform how to efficiently run data science pipelines and enhance our ability to analyze and communicate data.