Christopher Morris
Professor
I am interested in both the theoretical aspects of graph learning—such as expressivity, generalization, and optimization—and its practical applications in combinatorial optimization and the sciences.
Learning on Graphs
At the Learning on Graphs group, led by Christopher Morris at the Chair of Machine Learning and Reasoning at RWTH Aachen University, we are developing theoretical and practical approaches to machine learning on graphs.
We are happy to work with interested and dedicated students on these problems. Refer to thesis guidelines for further information.
Research
Our research brings together machine learning, theoretical computer science, and discrete mathematics. We focus on the following:
- 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 improve discrete algorithms in a data-driven manner?
- Applying principled learning on graphs to real-world problems.
Group members
Chendi Qian
PhD Student
I am interested in graph machine learning, particularly in modeling long-range interactions and applying GNNs to optimization algorithms.
Antoine Siraudin
PhD Student
I'm interested in graph generative models, with a particular focus on diffusion models, and their practical applications, such as combinatorial optimization.
Antonis Vasileiou
PhD Student
I am interested in theoretical machine learning, focusing on generalization properties of graph neural networks, as well as graph similarity measures and their implications.
Timo Stoll
PhD Student
I am interested in both theory and practical applications of graph machine learning, currently focusing on graph transformer and foundation models.
Solveig Wittig
PhD Student
I am interested in the theory behind graph machine learning, particularly neural algorithmic reasoning, focusing on expressivity and learning guarantees.
Erik Müller
Student Assistant
Gia Chi Dang
Student Assistant
Alumni
Luis Müller
Now at Google Research
PhD student from 2022 to 2026.
Thesis
opportunities
We also offer supervision over thesis works for RWTH students.
Thesis guidelines
If you are interested, please reach out to us via morris[ät]cs.rwth-aachen[dot]de with the subject [Thesis] and answer the following questions:
- Is there a paper from our current research that interests you?
- Are you leaning more toward a theoretical or applied topic?
- What are your (CS-related) strengths and weaknesses?
- What is your ideal starting date?
Please include an up-to-date transcript of records.