now at University of Bonn: Computational Analytics at Bonn University
Algorithm engineering includes the design, the theoretical analysis, the implementation, and experimental evaluation of algorithms. This field intends to bridge the gap between the efficient algorithms developed in algorithmic theory and the algorithms used by practitioners.
Algorithm engineering in combination with optimization and modern methods from machine learning is the basis for successful research in computational analytics and algorithmic data analysis.
In this context, the research interests of our group focuses on algorithms, data structures, and combinatorial optimization, for polynomial and NP-hard optimization problems on graphs and networks. The methods we use are based on exact methods like efficient graph algorithms, machine learning approaches, decomposition methods, structural studies, branch-and-bound, branch-and-cut, integer linear programming, polyhedral combinatorics, and graph theory on one side and approximation techniques like randomisation, sampling, local-search-based methods and evolutionary algorithms on the other. In particular, big data challenges are tackled via randomised, sampling or decomposition approaches that at the same time guarantee some (expected) approximation.
Algorithm engineering requires thorough experimentation and evaluation of our new algorithms for real problems. We have experience with a variety of applications such as graph (network) visualisation problems, cheminformatics (drug design), bioinformatics, archeology, statistical physics, and logistic problems such as network design and optimization.
Our current lectures and courses can be found at Lectures.
If you are interested in writing a diploma/Master's/Bachelor's thesis, please contact Prof. Mutzel or members of her group directly. We do not list the possible topics on this website.