This page is a work in progress. Please check it regularly for more content on our thesis topics.
Theory and Methods
Robust, Data-Efficient, and Discrete-Continuous Learning
- Combining discrete probability and Deep Learning
- Explainable AI
- ML and combinatorial optimization
- ML as surrogate models
(Geometric) Deep Learning
- Graph Neural networks
- ML for multi-modal data
- ML for Spatio-temporal data, simulations, PDEs
- Equivariant NNS
Applications
- ML for Molecules
- ML for Physical Systems
- ML for (Bio-) Medical Applications