I am an associate professor in the Machine Learning group and the Center for Data Science at the Department of Informatics, University of Bergen. My research interests are focused on topological methods in statistics and machine learning. Topological data analysis refers to the analysis of data shape and persistent homology is one of the most important methods in the field. The method consists of several steps: First, the data is translated into a filtered simplicial complex. Starting from low filtration values, this filtered simplicial complex is then assembled, while keeping track of the components, holes and voids in the structure. In this way, we keep track of the features and the range of filtration values for which they exist, allowing for multi-scale analysis. Features that persist over large ranges of filtration values are called persistent features and thought to be important. For unsupervised machine learning, we then visualize the persistence diagrams. For supervised machine learning, persistence diagrams need to be transformed into a vector representation, before using standard supervised learning algorithms. My research spans from efficient encoding of data into simplicial complexes (topological representation and sparsification), validation measures of unsupervised persistent homology, data benchmarks, efficient vector representations of persistent homology and applications in biology and geophysics.

In my free time I enjoy dancing swing dances (balboa, lindy hop, boogie woogie), playing double bass, hiking, running, cooking and reading fantasy books.