Our Research Interests

In our research, we cover a multitude of interdisciplinary topics in the context of data analytics, machine learning and optimization. In addition to methodological research in these areas, we work on their application in the transportation domain.

One of our current research topics is the automated selection and configuration of algorithms. In the context of machine learning, this for example leads to Automated Machine Learning (AutoML), even enabling beginners to apply complex methods successfully.

Furthermore, we deal with Exploratory Landscape Analysis (ELA), which allows the description of the individual instances of optimization problems, even if they are black-box. On the one hand, this brings an improved problem understanding, on the other hand, it enables algorithm selection and configuration for these types of problems.

As a final topic, we would like to highlight multi-objective optimization. Here, we work in particular on an improved understanding of the multimodality of continuous problems, for which we develop new visualization methods and optimization algorithms.