The engineering design discipline of hydrology and hydraulics has, for the past several decades, been largely based on probabilistic design techniques involving recurrence interval storm and flood events. The engineering ...

Gaussian processes (GPs) are widely used in geospatial analysis, machine learning and many application areas. We propose novel scalable methods to tackle two problems in Gaussian process modeling for large spatial datasets. In ...

The standard assumption in classification is that the training data are independent and identically distributed. Indeed, this assumption is so pervasive that it is often applied without mention. In this dissertation, we ...