This week's Information Science Colloquium will be jointly held with Cornell's Department of Computer Science during CS's usual Tuesday colloquium timeslot. The featured speaker will be Arvind Satyanarayan, a computer science PhD candidate at Stanford University. Satyanarayan's research develops new declarative models of interactive visualization, which describe what a visualization should look like rather than how it should be computed, and leverages them in novel design systems.
Title: Declarative Interaction Design for Data Visualization
Abstract: Interactive visualization is an increasingly popular medium for analysis and communication as it allows readers to engage data in dialogue. Hypotheses can be rapidly generated and evaluated in situ, facilitating an accretive construction of knowledge and serendipitous discovery. Yet, existing models of visualization relegate interaction to a second-class citizen: imperative event handling callbacks that are difficult to specify, and even harder to reason about.
In this talk, I will introduce two new declarative languages that lower the threshold for authoring interactive visualizations, and enable higher-level reasoning about the design space of interactions. Reactive Vega is an expressive representation that is well-suited for custom, explanatory visualizations. It shifts the burden of execution from the user to the underlying streaming dataflow system. Vega-Lite builds on Vega to provide a higher-level grammar for rapidly specifying interactive graphics for exploratory analysis. Its concise format decomposes interaction design into semantic units that can be systematically enumerated.
Together, these languages serve as platforms for further research into novel methods of expressing visualization design, and systems for interactive data analysis. And, critically, they provide a growing and engaged community to study their use with -- the Wikipedia and Jupyter communities, for instance, have embraced Vega and Vega-Lite to author interactive visualizations within articles and data science notebooks, respectively.