Data gathering, processing and management have become omnipresent in our society. Most of data gathered have very high dimensions and complex geometric structure. It calls for novel mathematical techniques to deal with explosive growth of dimensionality.
Computational linear algebra has successfully applied to solve massive data processing problems, computational harmonic analysis has developed dictionaries for adaptive sparse representation, and high-dimensional approximation theory could crack the curse of dimensionality.
The one-day workshop will concentrate on data fitting and approximation. During the workshop,
- Professor Charles A. Micchelli, distinguished professor at New York University at Albany and an invited speaker of International Congresses of Mathematician, Warsaw 1983, will talk about high-dimensional data fitting using discrete least squares.
- Professor Boris Shekhtman from University of South Florida will provide a taste of ideal interpolation concerning projection.
- Professor Plamen Simeonov from University of Houston Downtown will bring a polynomial blossom for the Askey-Wilson operator.
- Professor Yi Wang from Auburn University at Montgomery will discuss sparse representation of signals with non-linear Fourier atoms.
- Professors Teng Zhang will consider well-tempered landscape for non-convex robust subspace recovery.
- Professor Alexander Tovstolis will present some sharp results on approximating smooth functions by entire functions of exponential type.
The workshop is organized by Mourad Ismail, Charles Micchelli and Qiyu Sun and it is partially supported by the Department of Mathematics and the National Science Foundation.
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