I am always offering Bachelor and Master theses within the Angewandte Informatik program. Please contact me for details.
My research is located in the area of machine learning, a modern branch of artificial intelligence research. This is an interdisciplinary research topic in between computer science, statistics, and optimization, with connections to neuroscience and applications in robotics, engineering, medicine, economics, and many more disciplines. Within this wide area I am focusing on two aspects: supervised learning, mostly with support vector machines, and evolutionary algorithms for search and optimization.
Supervised learning is a learning paradigm with endless (mostly technical) applications. A learning machine (algorithm) builds a predictive model from data provided in the form of input/output pairs. Primary examples are classification and regression problems. Support vector machines (SVMs) have advanced to a standard method in the field. On the one hand I am interested in the SVM training problem, which basically amounts to large scale quadratic programming. On the other hand I am trying to simplify SVM usage for non-experts by developing robust methods for automatic model selection. My research activities include both theoretical and practical aspects ranging from SVM optimization to experimental comparison studies and software development.
Evolutionary Algorithms (EAs) are a class of nature-inspired algorithms that mimic the process of Darwinian evolution. This process is resolved into the components inheritance, variation, and selection. It has been widely recognized that EAs are useful for search and optimization. Formally they can be understood as randomized direct search heuristics.
Importantly, they are suitable for black-box optimization problems. I focus on evolution strategies, a class of optimization algorithms for continuous variables, and in multi-objective optimization. On the one hand I am interested in algorithm design and empirical studies. On the other hand the field offers fascinating theoretical challenges concerned with a deeper understanding of the mechanisms underlying direct randomized search. Last but not least I am interested in applications of EAs to relevant problems such as parameter tuning for learning machines or engineering problems.
This two-years research project has started in November 2013. It is conducted in cooperation with the chair Computergestützte Statistik at the Technical University of Dortmund. It is funded by the Mercator Research Center Ruhr (MERCUR). A short project description can be found here.
Currently I don't have any open positions.
I am an active developer of the Shark Machine Learning Library. Shark is an open-source, modular, and fast C++ library. A large share of my research code is either part of the library or based thereon. Check it out!
Shark is currently undergoing a major transition; actually it is more fair to speak of a complete rewrite. By now (2013) the work is mostly done, and we already have an alpha release of the brand new Shark 3. A few more design changes are underway that will make the library even faster and even more complete.
Coordinate descent with online adaptation of coordinate frequencies for fast training of linear models.
Maximization of dominated hypervolume for multi-objective benchmark problems.
Here you can find selected slides.