I am a computer scientist who is interested in interdisciplinary work to gain an understanding of the world, and to make it a better place. My interests span across many natural sciences, from astrophysics to evolution. This allows me to communicate efficiently with people from diverse areas, to understand their approaches, and to work together productively. Thus, one of my core strengths is to connect people and ideas from different disciplines. My preferred mode of operation is to discuss ideas on the whiteboard with others, and then to implement prototypes to see if the ideas work in practice. In the end however, I always strive to make things that get the job done, that are easy to use, and that are valuable for other people.

One of my focus areas is data analysis: I like to visualize complex data in simple ways. While working on complex subjects and projects, I am able to maintain an overview, and am motivated to familiarize myself with new topics quickly. I want to apply my knowledge and skills to interesting problems, and help to discover innovative solutions. During my studies and PhD, I have worked on automatic speech recognition, on the prediction of the future energy demand in the EU, and most recently on my PhD thesis in the field of computational biology. The common denominator of my work is to develop novel methods and software. To this end, I employ approaches from graph theory, statistical data analysis, and machine learning.

Overall, I am passionate about both learning and teaching. On the one hand, I hence look for opportunities to learn new things and to gain experience in new fields. On the other hand, I enjoy giving talks, lectures and seminars to share what I have learned so far and to present the results of my work. See research for some of my most recent work.