My main research interest lies at the intersection of theoretical computer science and algorithm engineering with a focus on computational geometry but also graph algorithms. More precisely, while I have a strong interest in algorithm design and fine-grained complexity theory, I also believe that turning theoretical insights and results into practical algorithms is a worthwhile contribution to the research community. Algorithm design in combination with conditional lower bounds from fine-grained complexity theory provides us with the tools necessary to find conditionally-optimal polynomial-time algorithms. These techniques help to pinpoint the crucial difficulty that is inherent to solving a computational problem. For both upper and lower bounds deeply understanding the structure of the problem at hand is key. Equipped with this theoretical knowledge, engineering an algorithm for solving a problem becomes significantly easier, as it becomes clearer which structure can be exploited to achieve better practical running times. I believe that a combination of both theoretical results as well as algorithm engineering is key to obtaining state-of-the-art practical algorithms for many fundamental polynomial-time problems.