Sparse grids techniques provide a means of approximating high dimensional data sets provided the data set exhibits an underlying smooth description. Possible topics include using sparse grid approximations to estimate probability density function, approximations of high dimensional functions, and uncertainty quantification of numerical models (how are model results dependent on parameter change). There are opportunities for mathematical analysis as well as practical implementation issues.