Curriculum vitea

I am currently a postdoctoral researcher at the KERMIT (knowledge-based systems) and Biobix (bioinformatics) groups at Ghent University. Machine intelligence and living systems fascinate me. In my research, I develop intelligent techniques to understand, predict, and control biological systems, large and small. My main toolbox involves a mix of machine learning, optimization, bioinformatics, and graph theory. I use these methods to predict how plants, animals, microorganisms, and molecules interact with each other. Much of my work involves working together with others, translating biological problems as mathematical or computational ones. Every year, I try to engage students in projects and theses, doing cool things such as making a beer classifier or designing new proteins. During my years as a teaching assistant, I taught various courses on data analytics and computational intelligence, including statistics, probability theory, and machine learning. Now, I am the responsible teacher for the class Selected Topics in Mathematical Optimization, teaching master students of bioinformatics how to solve concrete problems. Throughout all my activities, I value creativity and collaborating with people from different fields to develop highly innovative ideas.

Education and work experience

  • 2020-present: doctoral assistand Ghent University

    • topic: computational intelligence for synthetic biology

  • 2020-2021: 10% bioinformatics and machine learning consultant for BioLizard

  • 2017-2020: FWO postdoc

    • topic: machine learning methods for understanding and predicting ecological networks

  • 2012-2017: PhD in BioScience Engineering, Ghent University

    • topic: machine learning methods for pairwise learning

  • 2010-2012: Master BioScience Engineering: Cell-and Gene Biotechnology

    • major: computational biology

  • 2007-2010: Bachelor BioScience Engineering, Ghent University

  • 2001-2007: Science-Mathematics, Sint-Pietercollege en Sint-Jozefshandelsschool, Blankenberge

Teaching experience

As a lecturer:

As a former teaching assistant:

  • Statistical Data Analysis

  • Applied Statistics

  • Predictive Modelling

  • Computational Modelling

  • Selected Topics in Mathematical Optimization

  • Probabilistic Models

  • Quality Control and Risk Assessment


See Google Scholar.