This is another in the Department of Computer Science Pizza Seminar Series.
“Exploring the Potential of Data Depth for Uncertainty Characterization and Visualization of Ensembles”
When computational models or predictive simulations are used, researchers, analysts and decision makers are not only interested in understanding the data but also interested in understanding the uncertainty present in the data as well. In such situations, using ensembles is a common approach to account for the uncertainty, and explore the possible outcomes of a model. Visualization as an integral component of data-analysis task can significantly facilitate the communication of the characteristics of an ensemble including uncertainty information.
In this talk, I will introduce novel ensemble visualization paradigms based on the generalization of conventional univariate boxplots and the concept of data depth. Generalizations of boxplot provide an intuitive yet rigorous approach to studying variability and descriptive features of an ensemble. The nonparametric nature of this type of analysis makes it an advantageous approach to study uncertainty in various applications ranging from image analysis to fluid simulation to weather and climate modeling.
Refreshments will be served beforehand at 4:30 p.m. in the reception area of the 3rd floor of the Ungar building, 1365 Memorial Drive, Coral Gables, FL 33146 ( map/directions ).