KERMIT -- Research Unit Knowledge-based Systems

Waegeman Willem

willem.waegeman@ugent.be
@KERMIT, office 110.069
(+32) 9 264.92.00

Waegeman Willem
https://orcid.org/0000-0002-5950-3003
Biblio UGent
Google Scholar

Research Interests: Machine learning

Date	Title
18/08/2014	2015 IEEE CIS Outstanding TFS Paper Award
27/08/2012	Best Paper Award for Willem Waegeman
17/08/2012	Second Winner Award for Willem Waegeman

Name	Research Topic
Iliadis Dimitrios	Novel algorithms for extreme classification
Dewolf Nicolas	Machine learning methods for time series data
Theunissen Lauren	Machine learning methods for single-cell transcriptomics
De Waele Gaetan	Deep learning in genomics
Hustinx Jelle	Out-of-distribution detection in deep learning
Jürgens Mira Kristin	Novel methods for quantifying aleatoric and epistemic uncertainty in machine learning

Name	Research Topic
Choi Seongik	Comparing the effect of different feature representations for drugs and targets in Drug Discovery
Van den Bergh Scout	Generative models for protein design

Name	Title	Date
T. Mortier	Efficient algorithms for set-valued prediction in classification	23/06/2023
M. Gasparyan	Model reduction and parameter estimation for kinetic models of biochemical reaction networks	19/01/2023
F. Mey	Integration of synthetic biology, systems biology and machine learning for the production of chitooligosaccharides in Escherichia coli	18/01/2023
J. Clauwaert	Deep learning techniques for genome processing and annotation tasks in prokaryotes	24/09/2020
P. Rubbens	Machine learning approaches for microbial flow cytometry at the single-cell and community level	24/09/2019
C. Papagiannopoulou	A data-guided insight into global climate-vegetation dynamics	05/10/2018
M. Stock	Exact and efficient algorithms for pairwise learning	21/04/2017

Name	Title	Date
J.-H. Nowé	Countering illegal timbering by using multimodal machine learning	2022 - 2023
N. Tourne	Two-branch neural networks for predicting protein-DNA interaction	2022 - 2023
Y. Van Laere	Detection of 5mC modification in Nanopore sequencing data using deep learning	2021 - 2022
T. Willaert	Improving classification by rejection of high epistemic uncertainty points	2020 - 2021
L. Theunissen	A comparison of flat and hierarchical classification for automatic annotation of single-cell transcriptomics data	2020 - 2021
K. Zhang	Drug-target interaction prediction using multi-target prediction methods	2020 - 2021
E. Lorrez	Quantifying the environmental controls on African tropical forest dynamics through using a Granger causality framework	2020 - 2021
G. Tjon	Automative drinking water monitoring using flow cytometry data	2019 - 2020
L. Davey	Using artificial neural networks to uncover features in promoter sequences responsible for nonorthogonality in E. Coli	2019 - 2020
B. Verfaillie	Pattern recognition in raman spectroscopy data for a faster labelling of subjects in multiple domains	2019 - 2020
B. De Saedeleer	Combatting illegal timber trade using chemical fingerprints: the power of mathematics and mass spectrometry	2019 - 2020
L. Pollaris	Protein secondary structure prediction using transformer networks	2019 - 2020
B. De Clercq	Forecasting tidal surge in the Lower Sea Scheldt using machine learning techniques	2018 - 2019
M. Van Haeverbeke	Detection of m6a modifications in native RNA using Oxford Nanopore Technology	2018 - 2019
S. Top	Vertical farming of lettuce: the influence of rhizosphere bacteria and substrates	2018 - 2019
N. Goeders	Fight the illegal wood trade through chemical fingerprints : The power of mathematics and mass spectrometry	2018 - 2019
G. De Clercq	Deep learning for classification of DNA functional sequences	2018 - 2019
K. D'haeyer	Towards a data-driven identification of the microbial "Rammanome"	2018 - 2019
T. Vanlerberghe	Hierarchical multi-label classification of food products	2017 - 2018
A. De Graeve	Detecting climate drivers for vegetation extremes	2017 - 2018
R. Ingels	Understanding vegetation anomalies with machine learning methods	2017 - 2018
M. Misonne	Prediction of RNA polymerase-DNA interactions in Escherichia Coli	2017 - 2018
L. Bodyn	Exploration of deep autoencoders for collaborative filtering on cooking recipes	2016 - 2017
J. Heyse	Development and application of single-cell analysis tools for the study of sympatric bacterial populations	2016 - 2017
T. Mortier	Modeling of climate-vegetation dynamics using machine learning techniques in a non-linear Granger causality framework	2016 - 2017
S. Decubber	Spatiotemporal optimization of Granger causality methods for climate change attribution	2016 - 2017
C. Zhang	Visualization and unsupervised learning of flow cytometry data for bacterial identication	2016 - 2017
D. Schaumont	Een integratieve benadering gebaseerd op random forest voor de verbeterde predictie van exon-intron juncties	2016 - 2017
X. Yin	Discovering relationships in climate-vegetation dynamics using dynamic feature selection techniques	2016 - 2017
F. Ramon	A data-driven analysis of ingredients in cooking recipes	2015 - 2016
L. Tilleman	In silico engineering van cytochroom P450 via machine learning technieken	2015 - 2016
J. De Reu	Analyse van voorspellingsmodellen voor aardappelziekten en hun toepasbaarheid in Vlaanderen	2014 - 2015
W.K. Tsang	Assessing pathogen invasion based on community evenness and metabolic similarity	2014 - 2015
M. De Clercq	Prediction of ingredient combinations using machine learning techniques	2013 - 2014
A. De Paepe	The prediction of interaction between mRNA and miRNA using machine learning techniques	2012 - 2013
M. Stock	Learning pairwise relations in bioinformatics: three case studies	2011 - 2012
J. Vandepitte	Voorspellen van Fusarium spp. aanwezigheid en DON concentraties in wintertarwe met machine learning technieken	2009 - 2010

Waegeman Willem

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