In the past decade, the analysis of massive, high-dimensional, noisy, time-varying data sets has be-come a critical issue for a large number of scientists and engineers. Observations across several disciplines suggest the existence of geometrical and topological structures in many data sets, and much current research is devoted to modeling and exploiting these structures to aid in prediction and information extraction.

At Duke, faculty from several departments have been working together to develop new data analysis techniques with a broad range of applications, from genomics to art history, in mind; see people and projects for a sampling.

With backing from a NSF Research Training Grant, there has recently begun a large coordinated effort to integrate these techniques into the undergraduate and graduate curriculum. This involves new undergraduate courses in the core theoretical areas, graduate topics courses, an extensive summer research program for undergraduates, as well as year-long seminars aimed at both graduate and undergraduate students.

For further information, please contact Paul Bendich (lastname@math.duke.edu).