Research

 My broad research interests are in geometric and topological representation learning. Currently, I am interested in topological data analysis (TDA) and deep learning applications for graph structured data.

Papers and Preprints

NervePool: A Simplicial Pooling Layer

Sarah McGuire, Elizabeth Munch, Matthew Hirn

arXiv preprint, 2023

Do Neural Networks Trained with Topological Features Learn Different Internal Representations?

Sarah McGuire, Shane Jackson, Tegan Emerson, Henry Kvinge

Symmetry and Geometry in Neural Representations (NeurReps) Workshop at NeurIPS 2022

Taxonomy of Benchmarks in Graph Representation Learning

Renming Liu, Semih Cantürk, Frederik Wenkel, Sarah McGuire, Xinyi Wang, Anna Little, Leslie O’Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf, Ladislav Rampášek

Learning on Graphs Conference (LoG), 2022

Towards a Taxonomy of Graph Learning Datasets

Renming Liu, Semih Cantürk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, Leslie O’Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf, Ladislav Rampášek

Data-Centric AI workshop at NeurIPS, 2021