This talk will focus on mathematics developed to help with the mathematical challenges face by the Department of Energy (DOE) at the experimental facilities at Oak Ridge National Laboratory (ORNL). This talk will specifically focus on sparse sampling methods for large scale experimental data. 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.