I am a postdoctoral researcher in machine intelligence and computational biology at Ghent University. My main activity is to develop machine learning algorithms to predict how animals, plants, microorganisms and molecules interact with another. Do you speak Dutch and have three minutes to spare. Watch this video on how my research can be used to predict what a sky bison eats.

Outside of science, I enjoy reading (fiction and nonfiction), cooking, running, bouldering and board games (I’m a level five in Dungeons and Dragons).


I have been a teaching assistant for five years, teaching bioscience engineering students statistics, machine learning and computational intelligence.

Now, I am teaching a course on mathematical optimization for bioinformatics students. We study convex optimization, graph-based methods, evolutionary computing, learning-based optimization and much more! Check out the Github repository of the course (under development).

Every year I try to tutor several students for their bachelor project or master thesis. During the year we did some cool stuff, such as building a beer classifier, optimizing a protein, making a recipe generator and much more.

Selected bibliography

  • M. Stock, T. Pahikkala, A. Airola, B. De Baets and W. Waegeman, A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression, Neural Computation, August 2018, The MIT Press. (link)

  • M. Stock, T. Poisot, W. Waegeman and B. De Baets, Linear filtering reveals false negatives in species interaction data, Scientific Reports 7 (2017), 45908. (link)

  • G. Van Peer, A. De Paepe, M. Stock, J. Anckaert, P-J. Volders, J. Vandesompele, B. De Baets and W. Waegeman, miSTAR: miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structure, Nucleic Acids Research 45 (2017), e51.

  • M. Stock, K. Dembczyński, B. De Baets and W. Waegeman, Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models, Data Mining and Knowledge Discovery 30 (2016), 1370-1394.

  • M. De Clercq, M. Stock, B. De Baets and W. Waegeman, Data-driven recipe completion using machine learning methods, Trends in Food Science & Technology 49 (2016), 1-13.

  • M. Stock, T. Fober, E. Hüllermeier, S. Glinca, G. Klebe, T. Pahikkala, A. Airola, B. De Baets and W. Waegeman, Identification of functionally-related enzymes by learning-to-rank methods and cavity-based similarity measures, IEEE/ACM Trans. on Computational Biology and Bioinformatics 11 (2014), 1157-1169.

  • M. Stock, S. Hoefman, F.-M. Kerckhof, N. Boon, P. De Vos, B. De Baets, K. Heylen and W. Waegeman, Exploration and prediction of interactions between methanotrophs and heterotrophs, Research in Microbiology 10 (2013), 1045-1054.

  • W. Waegeman, T. Pahikkala, A. Airola, T. Salakoski, M. Stock and B. De Baets, A kernel-based framework for learning graded relations from data, IEEE Trans. Fuzzy Systems 20 (2012), 1090-1101.

Contact me

Doing similar research? Want to collaborate or doing a research stay in Ghent? Can you recommend me a great board game? Do not hesitate to contact me!