Skip to main content
Open Access Publications from the University of California

UC Irvine

UC Irvine Electronic Theses and Dissertations bannerUC Irvine

Machine Learning for the Sciences

Creative Commons 'BY-NC' version 4.0 license

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.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View