Because I think it is important that research is comprehensible and useful to everyone, I aim to make my contributions in science and programming publicly available as much as possible. With this blog, I intend to demonstrate what I am working on in an accessible way.
For a more detailed, non-technical introduction of my research, listen to this podcast.
About my research
My research is focused on describing transport and interactions of populations. Simply put, I look at large groups of particles, which can represent anything from pedestrians to molecules, and I try to express their behaviour in mathematical terms. If you are able to represent a situation in formulas or algorithms, it becomes a lot easier to analyse and predict. Most of my work is done in the context of crowd dynamics: the study of people in motion, interacting with each other and their environment. I did some work on evaluating evacuation strategies in buildings and large outdoor scenarios, and making new models that try to capture important elements in evacuation scenarios (like smoke and fire).
I deal with the numerical analysis of these systems, as well as tackling obstacles in the computationally demanding simulations. From the mathematical formulations I try to build simulations that capture the situations as accurate (and fast) as possible. This involves combining continuum systems with particle systems, which I researched in my MSc project, parallelising computations to increase scalability, and applying multiscale modelling techniques to compartmentalise transport and interaction.
Apart from my research in mathematics, I have a part-time research employment in computer science and do occasional data-engineering jobs. Furthermore, I have an ongoing visiting research fellowship at the University of Sussex (UK), where I work on topics in scientific computing with dr. Omar Lakkis and dr. Chandrasekhar Venkataraman.
Together with dr. Johan Garcia (Karlstad University), I investigate how to find patterns in internet traffic that distinguish regular browsing from activities like streaming or downloading. In order to anticipate high-intensity internet traffic but maintain user privacy, we are developing machine learning methods to identify and predict internet traffic properties from encrypted data streams. This allows internet service providers to anticipate changes in traffic and improve their quality of service. Our latest work, A Novel Flow-level Session Descriptor with Application to OS and Browser Identification has been presented at IEEEs Network and Operation Symposium (NOMS) in April 2020.
Additionally, from time to time I do freelancing work in data engineering. These jobs vary from performing qualitative or statistical analysis on large datasets to conducting data-driven analyses and developing models to describe the underlying principles.
I was born and raised in Nijmegen, The Netherlands. After high school, I moved to Eindhoven for my BSc and MSc degree in Applied Mathematics at the University of Technology Eindhoven. In August 2016, I moved to Karlstad, Sweden to start as a PhD student at Karlstad University in the field of applied mathematics.
Questions, discussions or ideas are very welcome! You can reach me at “firstname.lastname”@kau.se.