Mitchell Black

Mitchell Black is a Postdoctoral Researcher at University of California San Diego. Black researches machine learning on graphs. He is particularly interested in the following questions: What are the capabilities and limitations of current graph neural network architectures, and how can these architectures be improved? How can ideas from classical graph theory, particularly spectral graph theory, be used to understand and improve graph neural networks? And how do these ideas on graphs translate to higher-dimensional topological spaces? Mitchell received a PhD in Computer Science from Oregon State University, for which he was awarded the Dissertation of the Year Award from the School of Electrical Engineering and Computer Science. He also received an MS in Computer Science from Oregon State University and a BS in Computer Science and Mathematics from Montana State University.

Area(s) of Expertise

• Graph Neural Networks
• Spectral Graph Theory
• Computational Topology

Course(s)

DSC 215R: Foundations of Probability and Statistics in Data Science