Computer Science ETDs

Publication Date

Fall 11-14-2016


Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in feature discovery, with relevant applications to neuroimaging. Through our works within, this dissertation presents strong evidence that deep learning is a viable and important tool for neuroimaging studies.




Deep learning, neuroimaging, machine learning, variational inference, graphical models

Document Type


Degree Name

Computer Science

Level of Degree


Department Name

Department of Computer Science

First Committee Member (Chair)

Trilce Estrada

Second Committee Member

Vince Calhoun

Third Committee Member

Sergey Plis

Fourth Committee Member

Kyunghyun Cho

Fifth Committee Member

Ruslan Salakhutdinov