Statistics are commonly used to quantify and visualize uncertainty in data sets. In this talk, I will explore the use of statistics in understanding complex problems and describe the look of typical datasets created in this way. From there, I will discuss challenges of displaying these complex data sets, and statistical measures specific to expressing uncertainty within visualization. The remainder of the talk will focus on visualization methods, including a recounting of historical methods from the field of graphical data analysis including the boxplot, as well as an overview of methods from scientific and information visualization. Examples of current state-of-the art methods will be presented and a discussion of pending challenges in need of further exploration.
Kristi Potter is a Research Associate at the University of ¾«¶«Ó°ÊÓ, working in the College of Arts and Science's IT department as a visualization specialist to support research computing. She is colloquially know on campus as the 'Visualization Evangelist', as her duties include providing visualization consultation, service, and research for all departments within the College. In addition to her research support duties, she is actively researching visualization problems including topics related to uncertainty visualization, performance visualization, evaluation, and application specific techniques. To this end, she is working closely with Hank Childs and the CDUX research group in ¾«¶«Ó°ÊÓ's CIS department. Before joining the University of ¾«¶«Ó°ÊÓ, she worked as a Research Scientist at the SCI Institute at the University of Utah and collaborated closely with researchers at Sandia National Labs. She received her Ph.D. in Computer Science from the University of Utah and her B.S. in Computer Science and Fine Arts from the University of ¾«¶«Ó°ÊÓ.