This talk will focus on sparse sampling methods and fast optimization developed specifically for image processing and data analysis. Sparse sampling has the ability to provide accurate reconstructions of data and images when only partial information is available for measurement. Sparse sampling methods have demonstrated to be robust to measurement error. These methods have the potential to scale to large computational machines and analysis large volumes of data.
- Rick Archibald, Computational and Applied Mathematics Group