When

Monday, November 17, 2025 • 3:00–4:00 PM

Where

DMF 461, Bridgewater State University

Speaker

Kathryn Beck (Norbert Wiener Postdoctoral Fellow, Tufts University)

Format

In-person talk. Recording available below and via

Dropbox link
.

Organizers

Vignon Oussa (VOUSSA@bridgew.edu) •
Xiangfei “Fei” Chen (X10CHEN@bridgew.edu) •
Mahmoud El-Hashash (MELHASHASH@bridgew.edu)

Abstract

A major focus of Graph Signal Processing (GSP) is to develop signal processing methods for data on a graph domain
that take into account the underlying structure of the graph. We focus on developing techniques for Cayley graphs,
which are algebraically defined and highly symmetric in nature, making them a rich class of graphs for applications.
The main tool used in GSP, the graph Fourier transform, relies on an appropriate choice of eigenbasis for the
associated graph matrix. In order to better capture the symmetries in a Cayley graph, we present a spectral
decomposition of the adjacency matrix of a weighted Cayley graph based on the representation theory of the underlying group. We utilize this eigen-decomposition to construct frames that are suitable for signal processing on Cayley graphs. We also share an application to ranked data analysis.

Short Bio

Kathryn Beck is a Norbert Wiener Postdoctoral Fellow at Tufts University, working with Kasso Okoudjou. Her research
is in Graph Signal Processing, which is at the intersection of harmonic analysis, spectral graph theory, matrix
analysis, and functional analysis. She also works on applications of Graph Limit Theory to Graph Signal Processing.

Questions? Contact an organizer:
Vignon Oussa,
Xiangfei (Fei) Chen,
Mahmoud El-Hashash.