Adaptive Data Analysis and Sparsity

January 28 - February 1, 2013


Data analysis is important and highly successful throughout science and engineering, indeed in any field that deals with time-dependent signals. For nonlinear and nonstationary data (i.e., data generated by a nonlinear, time-dependent process), however, current data analysis methods have significant limitations, especially for very large datasets. Recent research has addresimage-postersed these limitations for data that has a sparse representation (i.e., for data that can be described by a only a few nonzero parameters) by exploiting methods such as compressed sensing, TV-based denoising, multiscale analysis, synchrosqueezed wavelet transform, nonlinear optimization, randomized algorithms and statistical methods. This workshop will bring together researchers from mathematics, signal processing, computer science and data application fields to promote and expand this research direction. Determination of trend and instantaneous frequency for nonlinear and non-stationary data are examples of the topics that the workshop will address.

This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.

Organizing Committee

Laura Balzano (University of Michigan, Electrical and Computer Engineering)
Ingrid Daubechies (Duke University, Applied Harmonic Analysis)
Tom Hou (California Institute of Technology, Applied and Computational Mathematics)
Norden Huang (National Central University)
Haomin Zhou (Georgia Institute of Technology, School of Mathematics)