Scholar
Thomas Gebhart
Google Scholar ID: HMGjFuwAAAAJ
University of Minnesota
Citations & Impact
Citations
263
H-index
8
i10-index
7
Publications
20
Co-authors
4
list available
Publications
Resume (English only)
Academic Achievements
- The paper 'Graph Convolutional Networks from the Perspective of Sheaves and the Neural Tangent Kernel' was accepted as part of the TAGML Workshop at ICML 2022 and published in PMLR.
Research Experience
- Serves as a Research Scientist at the University of Minnesota. Scheduled to give talks or present posters at several international conferences, including topics such as cellular sheaf theory in AI, scientific disruption and citation centrality, and spectral modeling of scientific novelty and disruption.
Education
- Received a Ph.D. in Computer Science from the University of Minnesota in 2023. Also holds B.S. degrees in Mathematics and Economics from the University of Minnesota.
Background
- A computer scientist, applied mathematician, and research scientist at the University of Minnesota. His research focuses on applying ideas from machine learning, network science, and algebraic topology to a range of multi-disciplinary problems. He is particularly interested in systems where knowledge is parameterized or transformed by networks.
