Our Former Members

Thesis Students and Their Topics

Our group has had the privilege of guiding numerous thesis students through their research projects. Below is a list of these students, their thesis topics, and the year of their thesis:

(2024 – 2025)

• R. Claeys - Variational inference for DTI
• W. Welvaert - End-to-end de novo metabolomics

(2023 – 2024)

• 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)

• J.-H. Nowé - Countering illegal timbering by using multimodal machine learning
• N. Tourne - Two-branch neural networks for predicting protein-DNA interaction

(2021-2022)

• 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)

• 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)

• 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)

• 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)

• 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)

• 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)

• 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