Scholar
Fabian Jogl
Google Scholar ID: UM-2zakAAAAJ
PhD Student, TU Wien
Citations & Impact
Citations
68
H-index
5
i10-index
3
Publications
16
Co-authors
0
Contact
Publications
Resume
Academic Achievements
- 1. Paper 'Maximally Expressive Graph Neural Networks for Outerplanar Graphs' accepted at TMLR (November 2024)
- 2. Top reviewer at NeurIPS 2024
- 3. Two workshop papers at NeurIPS (October 2024)
- 4. Paper 'The Expressive Power of Path-Based Graph Neural Networks' accepted at ICML 2024 (May 2024)
- 5. Gave a talk at BeST 2024 (March 2024)
- 6. Top reviewer at NeurIPS 2023
- 7. Two extended abstracts accepted at the LoG 2023 conference
- 8. Paper 'Maximally Expressive GNNs for Outerplanar Graphs' accepted as an oral at the GLFrontiers@NeurIPS workshop
- 9. Paper 'Expressivity-Preserving GNN Simulation' accepted at NeurIPS 2023
- 10. Best Poster Award at G-Research’s ICML poster party (2023)
- 11. Paper 'Expectation-Complete Graph Representations with Homomorphisms' accepted at ICML 2023
Research Experience
- 1. Summer@EPFL Research Fellowship: DATA Lab at EPFL (Summer 2022), research on high dimensional data cubes, advised by Christoph Koch and Peter Lindner
- 2. Student Employee: CV Lab at TU Wien (2021 - 2022), research on applying computer vision techniques to historical films
Education
- 1. PhD in Computer Science at CAIML and TU Wien (2022-2026), advised by Thomas Gärtner
- 2. MSc in Logic and Computation at TU Wien (2019-2022)
- 3. BSc in Physics at University of Vienna (2016-2019)
Background
- PhD student in machine learning, interested in graph neural networks. Research interests lie at the intersection of machine learning and classical algorithmics, aiming to combine applied machine learning with a strong theoretical and mathematical basis.
Miscellany
- No personal interests mentioned