You are visiting my personal academic webpage.
Currently, I work both at Leiden University and at the University of Amsterdam.
The over-arching theme of my research is computational network science.
In Leiden I work as an assistant professor at the Leiden Institute of Advanced Computer Science (LIACS), the computer science department of Leiden University. I teach a master course on Social Network Analysis in the fall semester and a bachelor course on Business Intelligence and Process Modelling in the spring semester.
In Amsterdam I work as a researcher in the CORPNET research group, which is hosted by the department of political science and the Amsterdam Institute for Social Science Research (AISSR) of the University of Amsterdam. Within the CORPNET project I work in an interdisciplinary team on the analysis of large-scale corporate and economic network data.
During the yearly Netherlands eScience conference in Amsterdam, I was awarded the 2017 Young eScientist award for my eScience-related research with amongst others the Dutch National Police, but primarily the research done within our CORPNET group at the University of Amsterdam. Following up on our recently published work on offshore finance, the awarded grant will allow us to further this direction of research, including several options for dissemination through an online interactive platform facilitating data exchange and visualization.
Our paper on a new data-driven network-based method to identify offshore financial centers is out in Scientific Reports. Using a dataset consisting of over 70 million ownership relations between firms across the world, we identify countries that are disproportionately involved as sinks and conduits of value in the global corporate ownership network. The research sheds light on topics such as tax evasion, providing the most fine-grained insight in offshore finance to date.
In 2011, I devised together with colleague Walter Kosters a new algorithm to efficiently compute the diameter (and radius, core and periphery) of small world networks. It has been available for reuse for a few years as part of the teexGraph package, and I am happy to see that this algorithm is now also part of the master branch of the latest version of NetworkX, an extensive and frequently used Python package for network analysis. Happy computing!
I was pleasantly surprised to receive the "Teacher of the Year 2016" award from the Faculty of Science of Leiden University. I gratefully thank the students who nominated me, as well as those who have chosen to finally elect me as the winner. If you have any suggestions as to how I can spend the EUR 2500 associated with this award on teaching-related activities, let me know!
With the UvA CORPENT group we are organzing a mini-symposium for academics, journalists and ngo's at SPUI25 in Amsterdam. The event relates to our recent work on corporate ownership networks in which we found that the Netherlands is the largest foreign investment conduit in the world, i.e., the Netherlands is used by multinationals to invest in other countries, and to transfer profits across their corporate structures. Registration is required.
This newly started research project aims to investigate data-driven modelling techniques from both computer science and economics in order to devise new methods, techniques and algorithms for determining official economic statistics. The project is a collaboration between Centraal Bureau voor de Statistiek (CBS), the University of Amsterdam and Leiden University.
I am involved in organizing the Leiden Networks Day, a one-day symposium about complex network analysis covering topics from network science to complex systems. It's free and has some great international speakers lined up! Currently, there are over 120 registrations, and space is running out. Register on time!
In the fall of 2016, I will for the 3rd year teach the course titled "Social Network Analysis for Computer Scientists" for master students in the Computer Science program at Leiden University. The course deals with topics such as algorithms for computing network topology, network knowledge discovery, and network data structures. Students work with real-world social network data and learn how to conduct scientific research in the area of network science.
I wrote a blog post about understanding the corporate elite through network science, focussing on the position of the United Kingdom and London in light of the recent Brexit referendum. It was featured by London School of Economics Business Review. The blog post is based on a paper that we wrote in the CORPNET group.
Last modified: October 16, 2017 @ 12:19:03.