I’m Luis, a third year PhD student at RWTH Aachen University under the supervision of Christopher Morris.

I’m interested in studying the capabilities and limitations of general-purpose machine learning architectures in the context of graph learning. My current research focus is on deriving a principled understanding of graph transformers and their potential benefits over GNNs.

Email: luis.mueller [at] cs [dot] rwth-aachen [dot] de

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Selected Works

Towards Principled Graph Transformers
Luis Müller, Daniel Kusuma, Blai Bonet, Christopher Morris
Accepted at NeurIPS 2024.
Paper Code

Aligning Transformers with Weisfeiler-Leman
Luis Müller, Christopher Morris
Accepted at ICML 2024.
Paper Code

Attending to Graph Transformers
Luis Müller, Michael Galkin, Christopher Morris, Ladislav Rampasek
Accepted at TMLR.
Paper Code Talk

MiniMol: A Parameter-Efficient Foundation Model for Molecular Learning
Kerstin Klaser, Blazej Banaszewski, Samuel Maddrell-Mander, Callum McLean, Luis Müller, Ali Parviz, Shenyang Huang, Andrew William Fitzgibbon
ICML 2024 Workshop on Efficient and Accessible Foundation Models for Biological Discovery.
Paper Code

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michał Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Ioannis Koutis, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew Fitzgibbon, Błażej Banaszewski, Chad Martin, Dominic Masters
Accepted at ICLR 2024.
Paper Code