Our research

Our research lab is interested in learning algorithms and learning dynamics that enable artificial intelligence. We use probabilistic Machine Learning approaches to develop novel and to decisively improve existing algorithms for unsupervised and semi-supervised learning. We are especially interested in new principles of learning, new theoretical results and foundations, scalable and interpretable learning, learning based on strong models, and learning under difficult conditions (few data, big data, strong noise, structured noise, missing data, etc.). Our approaches address aspects of intelligence where current AI systems struggle. Our research topics include disentanglement, data understanding, generalization, advanced learning from limited data, efficient learning on big data etc. Generative models and variational optimization are our main theoretical frameworks, and we have a particular interest in models with discrete latent variables. Our developed systems often enable novel applications, or applications under conditions that are too challenging for conventional approaches. We apply our approaches to data from different domains including general pattern recognition data, visual data, acoustic data, heterogeneous medical data and medical imaging. Furthermore, we are interested in the relation of our learning systems to biological learning and biological intelligence.

More content on our projects will follow soon.