Abstract for April 25, 2006

Steven Damelin: Some open problems in Dimension Reduction, Inverse Scattering and Power Systems

Nowdays, we are constantly flooded with information of all sorts and forms and a common denominator of data analysis in many emerging fields of current interest are large amounts of observations that have high dimensionality. In this talk, we will outline work in progress that relates to the idea of local dimension reduction in imaging and distributed power networks. In particular, we will discuss joint work on Paley Weiner theorems in inverse scattering, learning on curved manifolds and terrain manifold estimation from localization graphs of sensor and neural networks.

This is joint and ongoing work with T. Devaney (Northeastern), R. Luke (Delaware), P. Grabner (Graz), M. Werman (Hebrew U), D. Wunsch (Missouri-Rolla) and Armit Argawal (Singapore).

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