Deep learning, big data, graphical models, efficient inference, Bayesian methods.
Machine learning I and machine learning II.
We create statistical learning and inference algorithms that operate on a very large (big data) scale. We use probabilistic generative graphical models and combine these with discriminative deep learning methods. We have developed distributed variational Bayesian and MCMC sampling algorithms that can handle up to a billion data-items.