Christopher Morris

I am a tenure-track assistant professor and DFG Emmy Noether fellow at RWTH Aachen University, where I lead the Learning on Graphs (LoG) 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 six great PhD students Luis Müller, Chendi Qian, Antoine Siraudin, Antonis Vasileiou, Timo Stoll, and Solveig Wittig.

We develop machine learning methods for (graph-)structured data.

Our research combines techniques from machine learning, CS theory, and discrete mathematics and revolves around the following questions:

  1. How do we effectively capture (graph-)structured data in a data-driven manner?
  2. How can we ensure such methods generalize to unseen 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 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

Currently, we do not accept interns.

Selected Publications (All)

Teaching

Semester
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