| CARVIEW |
Christopher Morris
I am a full professor and DFG Emmy Noether fellow at RWTH Aachen University, where I lead the Learning on Graphs (LoG) group. 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 a postdoc at Polytechnique Montréal in the group of Andrea Lodi. Before my Montréal stint, I was a PhD student at TU Dortmund University advised by Petra Mutzel and Kristian Kersting. In Aachen, I supervise six great PhD students Luis Müller, Chendi Qian, Antoine Siraudin, Antonis Vasileiou, Timo Stoll, and Solveig Wittig.
Our research combines techniques from machine learning, TCS, and (discrete) mathematics and revolves around the following 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 make combinatorial algorithms faster in a data-driven manner?
For more details, please see my CV. You can also find me on X, Bluesky, and GitHub.
Fun Fact: My Erdős number is at most 3 (via Petra Mutzel → Bojan Mohar → P. Erdős).
Email: morris[ät]cs.rwth-aachen[dot]de
Selected Publications (All)
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Covered Forest: Fine-grained generalization analysis of graph neural networksInternational Conference on Machine Learning (ICML) 2025.
Antonis Vasileiou, Ben Finkelshtein, Floris Geerts, Ron Levie, Christopher Morris,
- Neural Information Processing Systems (NeurIPS) 2024.
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Weisfeiler-Leman at the margin: When more expressivity mattersInternational Conference on Machine Learning (ICML) 2024.
Billy J. Franks, Christopher Morris, Ameya Velingker, Floris Geerts,
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Exploring the Power of Graph Neural Networks in Solving Linear Optimization ProblemsInternational Conference on Artificial Intelligence and Statistics (AISTATS) 2024.
Chendi Qian, Didier Chételat, Christopher Morris,
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Fine-grained Expressivity of Graph Neural NetworksNeural Information Processing Systems (NeurIPS) 2023.
Jan Böker, Ron Levie, Ningyuan Huang, Soledad Villar, Christopher Morris,
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Combinatorial optimization and reasoning with graph neural networks
Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic,
Journal of Machine Learning Research, 2023.
- International Conference on Machine Learning (ICML) 2023.
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Ordered Subgraph Aggregation NetworksNeural Information Processing Systems (NeurIPS) 2022.
Chendi Qian, Gaurav Rattan, Floris Geerts, Christopher Morris, Mathias Niepert,
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Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
Christopher Morris, Gaurav Rattan, Petra Mutzel,
Neural Information Processing Systems (NeurIPS) 2020.
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Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe,
AAAI Conference on Artificial Intelligence (AAAI) 2019.
Teaching
| Semester | |
|---|---|
| WS 25/26 | Class (Bachelor+Master): Foundations and Applications of Machine Learning on Graphs |
| Seminar (Master): Machine Learning for Combinatorial Optimization | |
| Seminar (Master): Theory of Machine Learning on Graphs | |
| SS 25 | Class (Master): Algorithmic Foundations of Data Science |
| Seminar (Master): Theory of Machine Learning on Graphs | |
| WS 24/25 | Seminar (Master): Transformer on Graphs |
| Seminar (Bachelor): Maschinelles Lernen mit Graphen | |
| SS 24 | Class (Bachelor+Master): Foundations and Applications of Machine Learning with Graphs |
| WS 23/24 | Seminar (Master): Foundations of Supervised Machine Learning with Graphs |
| Seminar (Bachelor): Maschinelles Lernen mit Graphen | |
| SS 23 | Class (Master): Foundations and Applications of Machine Learning with Graphs |
| Seminar (Bachelor): Maschinelles Lernen mit Graphen | |
| WS 22/23 | Seminar (Master): Foundations of Supervised Machine Learning with Graphs |
| Seminar (Master): Machine Learning for Combinatorial Optimization |
