Scholar
Luis Müller
Google Scholar ID: iPxfRnEAAAAJ
PhD Student, RWTH Aaachen University
Citations & Impact
Citations
290
H-index
6
i10-index
5
Publications
8
Co-authors
23
list available
Publications
Resume (English only)
Academic Achievements
- - Paper: 'Towards Principled Graph Transformers', NeurIPS 2024
- - Paper: 'Aligning Transformers with Weisfeiler-Leman', ICML 2024
- - Paper: 'Attending to Graph Transformers', TMLR
- - Paper: 'MiniMol: A Parameter-Efficient Foundation Model for Molecular Learning', ICML 2024 Workshop
- - Paper: 'Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets', ICLR 2024
Research Experience
- No specific work experience or positions mentioned.
Education
- Degree: PhD Student; Institution: RWTH Aachen University; Advisor: Christopher Morris; Year: Third Year; Field: Graph Learning.
Background
- Research Interests: Capabilities and limitations of general-purpose machine learning architectures in the context of graph learning, particularly focusing on graph transformers versus GNNs.
Miscellany
- Contact: luis.mueller [at] cs [dot] rwth-aachen [dot] de; Social Media: Google Scholar, X, GitHub, LinkedIn
