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. 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.

In Aachen, I supervise two great PhD students Luis Müller and Chendi Qian.

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:

  1. How do we effectively capture the structure of graphs and networks in a data-driven manner?
  2. How can we scale up such methods for large-scale data?
  3. 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.

We are looking for a student assistant interested in implementing state-of-the-art machine learning architectures for graphs, click here.

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

  • WL meet VC (Preprint arxiv:2301.11039)
    Christopher Morris, Floris Geerts, Jan Tönshoff, Martin Grohe,
    International Conference on Machine Learning (ICML) 2023.

Journal Papers

Book Chapters

Workshop Papers

Edited Workshop and Competition Proceedings

  • 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,
    NeurIPS 2021 Competitions and Demonstrations Track, Proceedings of Machine Learning Research.

Working Papers



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