Christopher Morris
Full professor & DFG Emmy Noether fellow
I lead the Learning on Graphs (LoG) group at RWTH Aachen University. We develop machine learning methods for (graph-)structured data.
From 2022 to 2025, I was a tenure-track assistant professor at RWTH. Before joining RWTH, I was a postdoc at the Mila – Quebec AI Institute and McGill University in the group of Siamak Ravanbakhsh, and at Polytechnique Montréal in the group of Andrea Lodi. Before Montréal, I was a PhD student at TU Dortmund University, advised by Petra Mutzel and Kristian Kersting.
In Aachen, I supervise five great PhD students: Chendi Qian, Antoine Siraudin, Antonis Vasileiou, Timo Stoll, and Solveig Wittig.
Currently, we do not accept interns.
Research
Our research brings together machine learning, theoretical computer science, and discrete mathematics. We focus on three questions:
- How do we effectively capture (graph-)structured data in a data-driven manner?
- How can we ensure such methods generalize to unseen data?
- How can such methods improve discrete algorithms in a data-driven manner?
Selected publications (All)
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2026
Which Algorithms Can Graph Neural Networks Learn?
International Conference on Machine Learning (ICML)
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2025
Covered Forest: Fine-grained generalization analysis of graph neural networks
International Conference on Machine Learning (ICML)
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2024
Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems
International Conference on Artificial Intelligence and Statistics (AISTATS)
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2023
Fine-grained Expressivity of Graph Neural Networks
Neural Information Processing Systems (NeurIPS)
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2023
Combinatorial optimization and reasoning with graph neural networks
Journal of Machine Learning Research
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2023
WL meet VC
International Conference on Machine Learning (ICML)
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2020
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
Neural Information Processing Systems (NeurIPS)
Teaching
Winter 2026/27
- Class · MasterFoundations of Learning on Graphs
- Seminar · BachelorMaschinelles Lernen auf Graphen
- Seminar · MasterTheory of Machine Learning on Graphs
- Practical labMachine Learning for Combinatorial Optimization
Summer 2026
- Class · MasterDeep Learning for Structured Data
- Seminar · BachelorMaschinelles Lernen auf Graphen
- Seminar · MasterTheory of Machine Learning on Graphs
- Practical labMachine Learning for Combinatorial Optimization
Winter 2025/26
- Class · Bachelor & MasterFoundations and Applications of Machine Learning on Graphs
- Seminar · MasterMachine Learning for Combinatorial Optimization
- Seminar · MasterTheory of Machine Learning on Graphs
Summer 2025
- Class · MasterAlgorithmic Foundations of Data Science
- Seminar · MasterTheory of Machine Learning on Graphs
Winter 2024/25
- Seminar · MasterTransformer on Graphs
- Seminar · BachelorMaschinelles Lernen mit Graphen
Summer 2024
- Class · Bachelor & MasterFoundations and Applications of Machine Learning with Graphs
Winter 2023/24
- Seminar · MasterFoundations of Supervised Machine Learning with Graphs
- Seminar · BachelorMaschinelles Lernen mit Graphen
Summer 2023
- Class · MasterFoundations and Applications of Machine Learning with Graphs
- Seminar · BachelorMaschinelles Lernen mit Graphen
Winter 2022/23
- Seminar · MasterFoundations of Supervised Machine Learning with Graphs
- Seminar · MasterMachine Learning for Combinatorial Optimization