Gilles Vandewiele graduated magna cum laude as a computer science engineer in June 2016 at Ghent University. Later that year, in August, he joined the Internet and Data Science Lab (IDLab) research group in the Department of Information Technology (INTEC). In February 2021, Gilles obtained his PhD in Computer Science Engineering. Gilles conducts research in the domain of white-box machine learning for critical domains and (semantic) knowledge models. Other research interests are bio-inspired algorithms and sport-related data science in general.

If you cannot find Gilles sitting behind his desk, he is most likely doing a code golf in Python, competing in a data science competition or out playing football with friends or colleagues.

dr. ir. Gilles Vandewiele

gilles.vandewiele (at) ugent (dot) be

PhD Student
Internet and Data Science Lab (IDLab)
Department of Information Technology (INTEC)
University Ghent




Data Science Competitions


I am funded by a strategical basis-research grant, awarded by the Fonds Wetenschappelijk Onderzoek (FWO). My application number is 1S31417N.


  • Informatics (Python) (2017 - 2018 - 2019 - 2020): In this course, the first year engineers take their first coding steps by learning to program in Python. I assist different lab sessions: spanning from their first print function, object-oriented programming to implementing their own BCH codes. Moreover, an AI video game bot platform is set up every year, where the students have to write a bot that plays a game.
  • System Programming (C/C++) (2017): This course is given to second year computer science engineers and third year electronical engineers. They learn how to program in both C and C++. I assisted in 4 lab sessions, created one lab session myself and corrected it. In this lab session, the students had to create a morse encoder and decoder, by using dynamic programming. Moreover, I corrected the exercise part of the exam.
  • Big Data Science (2017): I created one lab session, in which students had to go through the entire machine learning pipeline: loading and processing data, exploratory data analysis, feature extraction, feature selection, model selection and hyper-parameter tuning. The given dataset contained occupancy informations of trains in Belgium. The task was to predict the occupancy (defined by three classes) for future trains.
  • Run4Music: four bright students (3 CS & 1 electrical engineer) created an application that both picks suited music according to your running pace and adjusts this music slightly such that the beats of the song are synchronized with your steps. A tweet about their result went trending on Twitter and one of the four students had the opportunity to present their work on one of the most popular radio stations in Belgium: QMusic.
  • Creating a generic platform for AI competitions: four enthusiastic bachelor students helped us to think out an architecture/flow to create a platform for AI competitions, which we host every year in the context of the course Informatics, such that we can relaunch it every year with a different game with minimal effort.
  • IEEE Access
  • PeerJ Computer Science
  • WSDM 2020

  • February 2021: obtained my PhD!!!
  • July 2020: Updated sections + pagination & toc + competitions section
  • September 2019: Added more articles & reviewing section
  • July 2019: Updated information + bootstrap 4 and some styling
  • January 2019: Updated information
  • December 2017: Created this website