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The goal of this workshop is to identify techniques, in the broadest theoretical and practical sense,
that are already useful or likely to be useful when designing or analyzing signal processing algorithms
that are robust under data, or "snapshot", constraints.
Since this problem is particularly acute in systems/applications where the underlying parameter space is often high dimensional and/or rapidly varying, the analysis and design of techniques to combat this so-called "curse of high-dimensionality" using novel computational or sparsity exploiting methods fit within the proposed scope of this workshop.
There will be an emphasis on identifying open research problems and highlighting promising new directions
inspired by the rapidly emerging discipline of stochastic eigen-analysis (random matrix theory).
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