Date of Award

Spring 5-15-2024

Document Type

Thesis (Ph.D.)

Department or Program

Engineering Sciences

First Advisor

Peter Chin

Second Advisor

Vikrant Vaze

Third Advisor

Colin Meyer


This thesis explores a variety of common graph theoretic problems from a machine learning perspective. The topics covered include fundamental network problems such as distance approximation, distance sensitivity, community detection, cross-network alignment, and graph embedding dimension reduction. These projects are unified by the theme of machine learning on graphs, graph embeddings, and representations of graphs.