Link to my Google Scholar profile.
My research mainly focuses on detecting communities in complex networks. Communities are groups of nodes that are more densely connected internally than externally. Such communities could, for example, correspond to groups of friends on social media or fields of study in citation networks. There are many heuristic algorithms for community detection, but there is limited understanding about which algorithm works well on what network.
For many machine learning tasks, there exist many measures to quantify the similarity between two outcomes. These measures are used to validate the performance of a machine learning algorithm by measuring the similarity between the outcome of the algorithm and the desired ('true') outcome. However, there are many of these validation measures, making it difficult to decide which one to use. This choice is especially important, since many of these validation measures have inherent biases towards certain outcomes, which may lead to an unfair or suboptimal algorithm being chosen in practice.
For the machine learning tasks of clustering and classification, we mathematically analyzed the most popular validation measures, which led to recommendations about which measure to use in what setting. This resulted in two papers that were published at ICML2021 and NeurIPS2021, regarding the clustering and classification task respectively.
For the completion of the Bachelor Applied Mathematics, I analysed a stochastic degradation model and created an algorithm to obtain the optimal maintenance policy. This work has been rewarded with a 9.5/10. The thesis can be found here.
My PhD project focuses on community detection in complex networks. This project is supervised by Nelly Litvak and Remco van der Hofstad.
For the Network Pages, a project by the Networks Center, I develop interactive visualizations that help explain network theory in a way that can be understood by non-mathematicians. An example of such a visualization is given below, taken from this blogpost about bottleneck detection algorithms.
As a student assistent, I assisted lecturers with various teaching tasks such as supervising student projects and grading assignments.
ViNotion is a specialist in the field of automated video content analysis that uses image recognition to analyze traffic. For ViNotion I used a game engine to generate synthetic datasets with ground truth. This data can be used to validate the performance of the products and to experiment with training on synthetic data.
For AthenaStudies, I gave exam trainings to help a group of first year Industrial Engineering students prepare for their mathematics resit.
Together with a friend, we have been occasionally brewing beer in the last few years. The beer is unfortunately not commercially available.
I also enjoy programming and software development. An example of one of my hobby projects is NetSweeper, a network-based variant of Minesweeper. The game can be played here.