UC Irvine

Progress in the sciences depends critically on the analysis of ever-growing bodies of data. Many of these analysis patterns are inferential in nature; their goal is to infer the value of one or more parameters which bear some real-world meaning. Others are in essence discriminative; their goal is to build a black-box model with the strongest possible predictive power. For both of these analysis styles, machine learning offers a host of powerful tools to tackle historically unapproachable problems.

In this dissertation, I present three examples of machine learning tools applied to the sciences. The first offers a novel model of textual diversity applied to the science of science itself. The second, explores a series of discriminative models which probe the evolution of the cosmos. The third offers a novel convolutional neural architecture for discriminating effective from ineffective drug candidates.

Taken together, these studies offer a glimpse of the breadth and potency of the contributions that machine learning can offer to the sciences.