Driving Exploratory Visualization through Perception & Cognition
Visualizations allow analysts to rapidly explore and make sense of their data. However, visualizations traditionally rely on perceptual models from psychology to guide designs that communicate relationships in data. These models ignore the complexities arising in real-world analytics applications, such as imperfect displays and crowded data distributions, leading to scalability limitations, flawed understandings, and erroneous conclusions. My research models how people interpret visualized data to understand limitations in current visualization systems. We use these results to develop novel visualization systems that support accurate analysis of complex data that better scale to the needs of modern analytics challenges by incorporating interactive statistical analytics and novel display technologies to increase the accessibility, scalability, and pervasiveness of data-driven reasoning. In this talk, I will discuss our efforts towards improving exploratory data analysis tools across a variety of domains.
Danielle Albers Szafir is an Assistant Professor and member of the founding faculty of the Department of Information Science, an Affiliate Professor of Computer Science, and a Fellow in the ATLAS Institute and Institute of Cognitive Science at the University of Colorado Boulder. Her research, which sits at the intersection of information visualization, data science, and cognitive science, has been integrated into leading tools such as D3 and Tableau and has received best paper awards at IEEE VIS and IS&T Color and Imaging. She was named to the Forbes 30 Under 30 Class of 2018 for Science. Dr. Szafir received a B.S. in Computer Science at the University of Washington as a NASA Space Grant Scholar and a Ph.D. in Computer Sciences at the University of Wisconsin-Madison.