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I'm a research and teaching assistant (PostDoc) at the University of Munich (Ludwig-Maximilians Universität München), more precisely at the Research and teaching unit for database and information systems. My PhD thesis had the topic: “Generalized and Efficient Outlier Detection for Spatial, Temporal, and High-Dimensional Data Mining”. A complete list of my publications can be found on my university homepage. Originally I studied mathematics and computer science, and I have degrees in mathematics (approx. bachelors degree), computer science (approx. masters degree) and management of digital technology (honors degree, at CDTM). My diploma thesis (which received the highest score, 1.0) bore the title “Statistical Approaches for Robustifying Correlation Clustering Algorithms” and belongs to the field of data mining / data clustering. My PhD thesis has the title Generalized and Efficient Outlier Detection for Spatial, Temporal, and High-Dimensional Data Mining and focuses on the detection of anomalous data. I also spent one term at the UC Berkeley as visiting scholer to research in Information Systems. My main hobby are the various swing dances (Lindy Hop, Balboa, Shag, Blues, Charleston, ...) and thus I'm dancing somewhere almost every night by now. As a consequence, I rarely ever have the time to update these web pages... What is “Vitavonni”? Lightbulb Vitavonni, when read backwards, is “innovativ” — this is what I care about: being innovative, doing something crazy, something new, something different – but also something useful, to improve something. It's not about just being different, but about being better. Creativity, innovation, knowledge, information — thats what is giving me a kick, what is driving me forward. I'm always on the run for new things, deeper understanding and new insights. I don't like following existing tracks, but there is no need to repeat the mistakes of others either, but should try to learn from them and do things better. Therefore I don't blindly trust existing knowledge, but I'll willingly accept it as the best guess and working hypothesis often to be followed until there is some reason to doubt.