Christopher Morris
As of June 2022, I am a tenure-track assistant professor at RWTH Aachen University where I lead the LoG (Learning on Graphs) group. 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 two great PhD students Luis Müller and Chendi Qian.
My research combines techniques from machine learning, graph algorithms, and CS theory and revolves around the following questions:
- How do we effectively capture the structure of graphs and networks in a data-driven manner?
- How can we scale up such methods for large-scale 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 Twitter 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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Fine-grained Expressivity of Graph Neural Networks (PreprintNeural Information Processing Systems (NeurIPS) 2023.
arXiv:2306.03698)
Jan Böker, Ron Levie, Ningyuan Huang, Soledad Villar, Christopher Morris,
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Combinatorial optimization and reasoning with graph neural networks (Preprint
arXiv:2102.09544)
Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic,
Journal of Machine Learning Research, 2023.
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WL meet VC (PreprintInternational Conference on Machine Learning (ICML) 2023.
arxiv:2301.11039)
Christopher Morris, Floris Geerts, Jan Tönshoff, Martin Grohe,
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Ordered Subgraph Aggregation Networks (PreprintNeural Information Processing Systems (NeurIPS) 2022.
arxiv:2206.11168)
Chendi Qian, Gaurav Rattan, Floris Geerts, Christopher Morris, Mathias Niepert,
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SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks (Preprint
arXiv:2203.13913)
Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh,
International Conference on Machine Learning (ICML) 2022.
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MIP-GNN: A data-driven framework for guiding combinatorial solvers (Preprint
arXiv:2205.14210)
Elias B. Khalil, Christopher Morris, Andrea Lodi,
AAAI Conference on Artificial Intelligence (AAAI) 2022.
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Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings (Preprint
arXiv:1904:01543)
Christopher Morris, Gaurav Rattan, Petra Mutzel,
Neural Information Processing Systems (NeurIPS) 2020.
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Deep Graph Matching Consensus (Preprint
arXiv:2001:09621)
Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege,
International Conference on Learning Representations (ICLR) 2020.
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Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (Preprint
arXiv:1810.02244)
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 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 |