A variety of technologies developed in diverse areas such as medical imaging, industrial
inspection, and oil and gas present a common challenge-which is that the analysis of models and/or
data present scientists with large collections, or ensembles, of complex data objects. Often times,
data analysists or application scientists benefit from a holistic view of the data. These views
could be concise summarizes that allow people to develop intuitions about overall patterns or to
confirm very general expectations, e.g. regarding data quality. This talk describes the use of data
depth and rank statistics for organizing and visualizing collections of complex data
Ross Whitaker earned a B.S. degree in Electrical Engineering and Computer Science from Princeton
University in 1986, and a Ph.D. in Computer Science from the University of North Carolina in 1994.
Since 2000 he has been at the University of Utah, where he is the Director of the School of
Computing and a Professor in the Scientific Computing and Imaging Institute. He is a recipient of
the NSF Career Award and an IEEE and AIMBE Fellow. He teaches discrete math, scientific
visualization, probability and statistics, and image processing. He has leads a graduate-level
research group in image analysis, geometry processing, and scientific computing, with a variety of
projects supported by both federal agencies and industrial contracts. His published works have
received over 13,000 citations.
University of Stuttgart, VISUS, Powerwall Room, cellar
The talk will be transmitted to Konstanz.