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.
Our research brings together machine learning, theoretical computer science, and discrete mathematics. We focus on the following:
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. Recently, I have gotten very interested in developing the theoretical foundation of data-driven algorithms with guarantees.
PhD Student
I am interested in graph machine learning, particularly in modeling long-range interactions and applying GNNs to optimization algorithms.
PhD Student
I'm interested in graph generative models, with a particular focus on diffusion models, and their practical applications, such as combinatorial optimization.
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.
PhD Student
I am interested in both theory and practical applications of graph machine learning, currently focusing on graph transformer and foundation models.
PhD Student
I am interested in the theory behind graph machine learning, particularly neural algorithmic reasoning, focusing on expressivity and learning guarantees.
Student Assistant
Student Assistant
Now at Google Research
PhD student from 2022 to 2026.
We also offer supervision over thesis works for RWTH students.
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:
Please include an up-to-date transcript of records.