I'm a Senior Associate Professor in machine learning and Head of the Division of Statistics and Machine Learning. I am interested in the interplay between statistics and machine learning, in particular how statistical methodology can be used to quantify and reason about the uncertainties in the predictions and decisions made by machine learning systems.
My research interests span a wide range of topics in statistical machine learning, including approximate Bayesian inference, representation learning, graph-based machine learning, and spatio-temporal models. Most of my research is related to (generic) method development, and probabilistic modeling and uncertainty quantification are two common denominators. Together with my team, I also work on a range of different applications of machine learning, such as weather forecasting, materials science, biochemistry, and applications in the automotive industry.
For more information about my background and my research, please visit my external page.
