Christopher Morris
As of June 2022, I am a tenure-track assistant professor at RWTH Aachen. 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. My PhD research was generously supported by the German Research Foundation, through the Collaborative Research Center SFB 876 - Providing Information by Resource-Constrained Data Analysis, Project A6.
I develop machine learning methods for graphs, network, and relational data.
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
I am starting a new group at RWTH Aachen in Germany and have multiple openings for fully-funded PhD positions.
If you are a CS student at RWTH and interested in writing a bachelor or master thesis in my group, click here.
Publications (Scholar)
Conference Papers
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Weisfeiler and Leman Go RelationalLearning on Graphs (LoG) 2022.
Pablo Barcelo, Mikhail Galkin, Christopher Morris, Miguel Romero Orth,
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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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Reconstruction for Powerful Graph Representations (Preprint
arXiv:2110.00577)
Leonardo Cotta, Christopher Morris, Bruno Ribeiro,
Neural Information Processing Systems (NeurIPS) 2021.
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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,
International Joint Conference on Artificial Intelligence (IJCAI) 2021.
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The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs (Preprint
arXiv:2105.05911)
Christopher Morris, Matthias Fey, Nils M. Kriege,
International Joint Conference on Artificial Intelligence (IJCAI) 2021.
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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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Temporal Graph Kernels for Classifying Dissemination Processes (Preprint
arXiv:1911.05496)
Lutz Oettershagen, Nils Kriege, Christopher Morris, Petra Mutzel,
SIAM International Conference on Data Mining (SDM) 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.
- Hierarchical Graph Representation Learning with Differentiable Pooling (Preprint
arXiv:1806.08804)
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec,
Neural Information Processing Systems (NeurIPS) 2018, spotlight presentation, and KDD Deep Learning Day 2018.
- A Property Testing Framework for the Theoretical Expressivity of Graph Kernels
Nils M. Kriege, Christopher Morris, Anja Rey, Christian Sohler,
International Joint Conference on Artificial Intelligence (IJCAI) 2018.
- Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs (Preprint
arXiv:1703.02379)
Christopher Morris, Kristian Kersting, Petra Mutzel,
IEEE International Conference on Data Mining (IEEE ICDM) 2017, full paper.
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Recent Advances in Kernel-Based Graph Classification
Nils M. Kriege, Christopher Morris,
European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2017, nectar track.
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Faster Kernels for Graphs with Continuous Attributes via Hashing (Preprint
arXiv:1610.00064)
Christopher Morris, Nils M. Kriege, Kristian Kersting, Petra Mutzel,
IEEE International Conference on Data Mining (IEEE ICDM) 2016.
Journal Papers
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Classifying Dissemination Processes in Temporal Graphs
Lutz Oettershagen, Nils M. Kriege, Christopher Morris, Petra Mutzel,
Big Data, 2020.
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A Survey on Graph Kernels (Preprint
arXiv:1903.11835)
Nils M. Kriege, Fredrik D. Johansson, Christopher Morris,
Applied Network Science, Machine learning with graphs, 2020.
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A unifying view of explicit and implicit feature maps of graph kernels (Preprint
arXiv:1703.00676)
Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel,
Data Mining and Knowledge Discovery, 2019.
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Output-sensitive Complexity of Multiobjective Combinatorial Optimization (Preprint
arXiv:1610.07204)
Fritz Bökler, Matthias Ehrgott, Christopher Morris, Petra Mutzel,
Journal of Multicriteria Decision Analysis, 2016.
Book Chapters
Workshop Papers
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SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks (Preprint
arXiv:2203.13913)
Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh,
Geometrical and Topological Representation Learning (GT-RL, ICLR 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,
Graph Representation Learning and Beyond (GRL+, ICML 2020).
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TUDataset: A collection of benchmark datasets for learning with graphs (Preprint
arXiv:2007.08663)
Christopher Morris, Nils M. Kriege, Franka Bause Kristian Kersting, Petra Mutzel, Marion Neumann,
Graph Representation Learning and Beyond (GRL+, ICML 2020).
Working Papers
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Weisfeiler and Leman go Maching Learning: The Story so far (Preprint
arXiv:2112.09992)
Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten Borgwardt.
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The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights (Preprint
2203.02433)
Maxime Gasse, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros Gleixner, Aleksandr M. Kazachkov, Elias Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Christopher Morris, Dimitri J. Papageorgiou, Augustin Parjadis, Sebastian Pokutta, Antoine Prouvost, Lara Scavuzzo, Giulia Zarpellon, Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo, Xiang Zhou, Haohan Huang, Shengcheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan, Zixuan Cao, Yang Xu, Zhewei Huang, Shuchang Zhou, Chen Binbin, He Minggui, Hao Hao, Zhang Zhiyu, An Zhiwu, Mao Kun.
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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.
Thesis
Teaching
| Semester | |
|---|---|
| WS 22/23 | Seminar (Master): Foundations of Supervised Machine Learning with Graphs | Seminar (Master): Machine Learning for Combinatorial Optimization |