Skip to content

Teaching

Teaching & supervision

We teach mathematics and machine learning in the bachelor and master programmes of Bioscience Engineering and Bioinformatics, and in lifelong learning programmes of the UGent Academy for Engineers.

Courses

Current courses

Previously

Master theses

Thesis students and their topics

We have had the privilege of guiding 45+ master students through their thesis research. Interested in doing your thesis with us? Get in touch.

Contact
2025 – 2026 3 students
  • W. Labaere Interpretability of spatial regression models for forest-risk commodity tracing
  • F. Denijs Conformal prediction for de novo metabolomics with structural similarity guarantees
  • J. Deketelaere Improving the reliability of geospatial models for timber harvest prediction by means of conformal prediction
2024 – 2025 2 students
  • R. Claeys Variational inference for DTI
  • W. Welvaert End-to-end de novo metabolomics
2023 – 2024 2 students
  • S. Jimenez Disentangling uncertainties in machine learning: a comparative approach
  • J. Cueva Villavicencio Novel neural networks for bacterial species prediction from MALDI-TOF data
2022 – 2023 2 students
  • J.-H. Nowé Countering illegal timbering by using multimodal machine learning
  • N. Tourné Two-branch neural networks for predicting protein-DNA interaction
2021 – 2022 2 students
  • Y. Van Laere Detection of 5mC modification in Nanopore sequencing data using deep learning
  • V. Moortgat On calibration of probabilistic multi-class classifiers with hierarchical factorization
2020 – 2021 4 students
  • E. Lorrez Quantifying the environmental controls on African tropical forest dynamics through using a Granger causality framework
  • L. Theunissen A comparison of flat and hierarchical classification for automatic annotation of single-cell transcriptomics data
  • T. Willaert Improving classification by rejection of high epistemic uncertainty points
  • K. Zhang Drug-target interaction prediction using multi-target prediction methods
2019 – 2020 5 students
  • L. Davey Artificial neural networks to uncover features in promoter sequences responsible for nonorthogonality in E. coli
  • B. De Saedeleer Combatting illegal timber trade using chemical fingerprints: the power of mathematics and mass spectrometry
  • L. Pollaris Protein secondary structure prediction using transformer networks
  • G. Tjon Automative drinking water monitoring using flow cytometry data
  • B. Verfaillie Pattern recognition in Raman spectroscopy data for a faster labelling of subjects in multiple domains
2018 – 2019 6 students
  • K. D'haeyer Towards a data-driven identification of the microbial "Rammanome"
  • B. De Clercq Forecasting tidal surge in the Lower Sea Scheldt using machine learning techniques
  • G. De Clercq Deep learning for classification of DNA functional sequences
  • N. Goeders Fight the illegal wood trade through chemical fingerprints: the power of mathematics and mass spectrometry
  • S. Top Vertical farming of lettuce: the influence of rhizosphere bacteria and substrates
  • M. V. Haeverbeke Detection of m6A modifications in native RNA using Oxford Nanopore Technology
2017 – 2018 4 students
  • A. De Graeve Detecting climate drivers for vegetation extremes
  • R. Ingels Understanding vegetation anomalies with machine learning methods
  • M. Misonne Prediction of RNA polymerase-DNA interactions in Escherichia coli
  • T. Vanlerberghe Hierarchical multi-label classification of food products
2016 – 2017 7 students
  • L. Bodyn Exploration of deep autoencoders for collaborative filtering on cooking recipes
  • S. Decubber Spatiotemporal optimization of Granger causality methods for climate change attribution
  • J. Heyse Development and application of single-cell analysis tools for the study of sympatric bacterial populations
  • T. Mortier Modeling of climate-vegetation dynamics using machine learning techniques in a non-linear Granger causality framework
  • D. Schaumont Een integratieve benadering gebaseerd op random forest voor de verbeterde predictie van exon-intron juncties
  • X. Yin Discovering relationships in climate-vegetation dynamics using dynamic feature selection techniques
  • C. Zhang Visualization and unsupervised learning of flow cytometry data for bacterial identification
2009 – 2016 8 students
  • F. Ramon A data-driven analysis of ingredients in cooking recipes
  • L. Tilleman In silico engineering van cytochroom P450 via machine learning technieken
  • J. De Reu Analyse van voorspellingsmodellen voor aardappelziekten en hun toepasbaarheid in Vlaanderen
  • W.K. Tsang Assessing pathogen invasion based on community evenness and metabolic similarity
  • M. De Clercq Prediction of ingredient combinations using machine learning techniques
  • A. De Paepe The prediction of interaction between mRNA and miRNA using machine learning techniques
  • M. Stock Learning pairwise relations in bioinformatics: three case studies
  • J. Vandepitte Voorspellen van Fusarium spp. aanwezigheid en DON concentraties in wintertarwe met machine learning technieken