Data Visualization

Visualizations allow people to apply their own knowledge to understand and explore data. This talk will explore how we can construct visualizations that enable exploratory big data analysis. Specifically, I will outline how cognition and analytics intersect to enable new insights into data. This talk will outline studies of how people interpret visual representations of data and show how designers can use this understanding to drive interactive systems that support more effective analysis at larger scales. By blending cognition and statistical analytic methods, developers can use existing theories from visual perception to support analysts in generating richer insights at greater scales and across a broader spectrum of domains.

Danielle Albers Szafir is an Assistant Professor and member of the founding faculty of the Department of Information Science at the University of Colorado Boulder. Her research focuses on increasing the scalability and comprehensibility of information visualization by quantifying perception and cognition for visualization design. Her work develops interactive systems and techniques for exploring large and complex datasets in domains ranging from biology to the humanities. Prior to joining CU, she received her PhD in Computer Sciences at the University of Wisconsin-Madison and a BS in Computer Science from the University of Washington.