Hyeok Kim 

Visualization plays an important role in communicating data. To accommodate a broader audience, visualization authors need to simultaneously support varying user contexts in terms of device types, visual ability, and data literacy. This requires that visualization authors learn, explore, and apply design techniques beyond their familiarity, increasing time and effort. While visualization research provides applications for specific user contexts, software support for simultaneous visualization design for multiple contexts remains a major challenge. To reduce this gap, my research contributes both systems and end-user applications that support simultaneous authoring of visualizations for multiple user contexts. In this talk, I first present a mixed-initiative approach to responsive visualization: authoring data visualization that adapt across device types. The talk demonstrates how expert authors were able to produce satisfactory responsive charts quickly and carefully by mixing automated design recommendations and manual customization. Next, I introduce Erie, a declarative grammar for data sonification that describes data using sound for accessibility and an immersive experience. By interfacing with low-level audio programming techniques, Erie can help creators easily build sonifications for diverse purposes, such as statistical analysis and public science communication. Leveraging my research, I conclude with promising future directions including accessible statistics, constraint programming for multi-context visualization, and automation strategies for analyzing visualization research.