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Machine Learning Group [Machine Learning Group] (MLG)
Faculté des Sciences / faculty of Sciences - Informatique (unité ULB741)

Le Laboratoire en apprentissage automatique a été fondé en 2004 par Gianluca Bontempi. En Octobre 2008 Tom Lenaerts a rejoint le groupe. Les activités de recherche concernent l'apprentissage automatique, la fouille intelligente des données et leur applications à la bioinformatique et la biologie computationnelle. [The ULB Machine Learning Group (MLG) was founded within the Computer Science Department of the Faculty of Sciences in 2004 by Gianluca Bontempi. The activity of the group covers the areas of machine learning, computational modelling and statistics and their applications in data mining, simulation and time series prediction. In October 2008, Tom Lenaerts joined MLG as a new academic, extending the group's expertise towards computational biology, evolutionary dynamics and complex systems research. Currently we focus on:Data mining, modeling and predictionBioinformatics and computational biologyDynamics of cooperation and competition]



coordonnées / contact details


Machine Learning Group [Machine Learning Group]
tel +32-2-650.55.91
http://mlg.ulb.ac.be
Campus de la Plaine, NO8
CP212, boulevard du Triomphe, 1050 Bruxelles

Pour en savoir plus, consultez le site web de l'unité.



responsables / head


Gianluca BONTEMPI Tom LENAERTS


composition / members


Giovanni BURONI Fabrizio CARCILLO Matthieu DEFRANCE Jacopo DE STEFANI Elias FERNANDEZ Bertrand LEBICHOT Yann-Aël LE BORGNE Nathaniel MON PERE Charlotte NACHTEGAEL Gabriele ORLANDO Sofia PAPADIMITRIOU Arnaud POLLARIS Sylvie VANDE VELDE Nassim VERSBRAEGEN


projets / projects


DEFEATFRAUD: Assessment and validation of deep feature engineering and learning solutions for fraud detection [DEFEATFRAUD: Assessment and validation of deep feature engineering and learning solutions for fraud detection]
The project aims at improving the existing fraud detection process of Worldline by adding a number of deep learning and adaptive functionalities to the existing data driven strategies. This will be made possible by increasing the degree of autonomy and adaptivity of the detection process thanks to a number of methodological improvements: 1) design and assessment of an online learning classifier based on deep learning, whose great potential has not yet been explored in the domain of fraud detection 2) automation of the feature creation step by adopting recent representation learning techniques (deep learning) 3) integration of supervised and unsupervised techniques for precision improvement 4) introduction of an exploration step (based on active and semi-supervised learning) in the labeling process to improve the reactivity to fraud change and nonstationarity. We expect several benefits ranging from enhanced fraud detection accuracy, better interpretability of fraudulent patterns, and fraud prevention. [The project aims at improving the existing fraud detection process of Worldline by adding a number of deep learning and adaptive functionalities to the existing data driven strategies. This will be made possible by increasing the degree of autonomy and adaptivity of the detection process thanks to a number of methodological improvements: 1) design and assessment of an online learning classifier based on deep learning, whose great potential has not yet been explored in the domain of fraud detection 2) automation of the feature creation step by adopting recent representation learning techniques (deep learning) 3) integration of supervised and unsupervised techniques for precision improvement 4) introduction of an exploration step (based on active and semi-supervised learning) in the labeling process to improve the reactivity to fraud change and nonstationarity. We expect several benefits ranging from enhanced fraud detection accuracy, better interpretability of fraudulent patterns, and fraud prevention.]

Brussels MOBI-AID : Brussels MOBIlity Advanced Indicators Dashboard [Brussels MOBI-AID : Brussels MOBIlity Advanced Indicators Dashboard]
Brussels MOBI-AID (Brussels MOBIlity-Advanced Indicators Dashboard) aims at designing and building this performance monitoring system, by means of a dashboard of advanced mobility indicators that will allow (1) to better understand mobility dynamics in Brussels Region, (2) to support local authorities in designing suitable and sustainable policies, (3) to assist Brussels, capital of Europe, to be recognized as a model of a Smart Region. The transition from the current model to a Smart Region model raises a number of non-trivial challenges, which range from reliable storage of, and easy access to, massive amounts of mobility data, to extracting sustainable mobility indicators in an automated and validated manner and to actioning the extracted indicators in a dashboard. We expect that Brussels MOBI-AID by making mobility smarter and more sustainable in Brussels Region will provide many valorisation pathways as it will provide an open data platform with smart data on mobility. [Brussels MOBI-AID (Brussels MOBIlity-Advanced Indicators Dashboard) aims at designing and building this performance monitoring system, by means of a dashboard of advanced mobility indicators that will allow (1) to better understand mobility dynamics in Brussels Region, (2) to support local authorities in designing suitable and sustainable policies, (3) to assist Brussels, capital of Europe, to be recognized as a model of a Smart Region. The transition from the current model to a Smart Region model raises a number of non-trivial challenges, which range from reliable storage of, and easy access to, massive amounts of mobility data, to extracting sustainable mobility indicators in an automated and validated manner and to actioning the extracted indicators in a dashboard. We expect that Brussels MOBI-AID by making mobility smarter and more sustainable in Brussels Region will provide many valorisation pathways as it will provide an open data platform with smart data on mobility. ]

CAUSEL: Vers une sélection génomique à l'aide de variants causaux chez les bovins Blanc-Bleu Belges (BBB) [CAUSEL: Vers une sélection génomique à l'aide de variants causaux chez les bovins Blanc-Bleu Belges (BBB) ]
The main goal of the CAUSEL project is to develop a procedure that improves the classic bovine selection. The project targets the two limiting factors of the classic selection: i) the SNP markers are not causal variants and ii) the statistical models only consider additive effects and neither dominance or epistasis. [The main goal of the CAUSEL project is to develop a procedure that improves the classic bovine selection. The project targets the two limiting factors of the classic selection: i) the SNP markers are not causal variants and ii) the statistical models only consider additive effects and neither dominance or epistasis. ]

Deciphering oligo- to polygenic genetic architecture in brain developmental disorders [Deciphering oligo- to polygenic genetic architecture in brain developmental disorders ]
Deciphering oligo- to polygenic genetic architecture in brain developmental disorders [Deciphering oligo- to polygenic genetic architecture in brain developmental disorders ]

BRIGHTanalysis: Brussels Intelligent ICT for Genomic High Throughput Analysis [BRIGHTanalysis: Brussels Intelligent ICT for Genomic High Throughput Analysis ]
BRIGHTanalysis: Brussels Intelligent ICT for Genomic High Throughput Analysis [BRIGHTanalysis: Brussels Intelligent ICT for Genomic High Throughput Analysis ]

Identifying the mechanisms involved in transducing binding information through SH3-SH2 supradomains and their role in regulating the activity of members of the family of Src kinases [Identifying the mechanisms involved in transducing binding information through SH3-SH2 supradomains and their role in regulating the activity of members of the family of Src kinases ]
Identifying the mechanisms involved in transducing binding information through SH3-SH2 supradomains and their role in regulating the activity of members of the family of Src kinases [Identifying the mechanisms involved in transducing binding information through SH3-SH2 supradomains and their role in regulating the activity of members of the family of Src kinases ]

The role of information disclosure in group formation, network stability and strategic decision-making [The role of information disclosure in group formation, network stability and strategic decision-making ]
The role of information disclosure in group formation, network stability and strategic decision-making [The role of information disclosure in group formation, network stability and strategic decision-making ]

Identifying the mechanisms involved in transducing binding information through SH3-SH2 supradomains and their role in regulating the activity of members of the family of Src kinases [Identifying the mechanisms involved in transducing binding information through SH3-SH2 supradomains and their role in regulating the activity of members of the family of Src kinases ]
Identifying the mechanisms involved in transducing binding information through SH3-SH2 supradomains and their role in regulating the activity of members of the family of Src kinases [Identifying the mechanisms involved in transducing binding information through SH3-SH2 supradomains and their role in regulating the activity of members of the family of Src kinases ]



theses


Martin Bizet. Bioinformatic inference of a prognostic epigenetic signature of immunity in breast cancers, 2017

Liran Lerman. A Machine Learning Approach for Automatic and Generic Side-Channel Attacks, 2015

Andrea Dal Pozzolo. Adaptive Machine Learning for Credit Card Fraud Detection, 2015

Miguel Lopes. Inference of gene networks from time series expression data and application to type 1 Diabetes, 2015

Lucia R. Fernandez. Unravelling the information transfer through the second SH2 domain from the human phosphatase PTPN11 by NMR, 2015

Souhaib Ben Taieb. Machine learning strategies for multi-step-ahead time series forecasting, 2014

Catharina Olsen. Causal Inference and Prior Integration in Bioinformatics using Information Theory, 2013

R. Huculeci. Unravelling Fyn SH2's snap-lock mechanism: identification and analysis through NMR, 2013

Abhilash Miranda. Spectral Factor Model for Times Series Learning, 2011

Benjamin Haibe-Kains. Identification and Assessment of Gene Signatures in Human Breast Cancer., 2009

Olivier Caelen. Sélection Séquentielle en Environnement Aléatoire Appliquée à l'Apprentissage Supervisé, 2009

Yann-ael Le Borgne. Learning in Wireless Sensor Networks for Energy-Efficient Environmental Monitoring, 2009

Kevin Kontos. Gaussian Graphical Model Selection for Gene Regulatory Networké Reverse Engineering and Function Prediction, 2009

Patrick E. Meyer. Information Theoretic Variable Selection and Network Inference from Microarray Data., 2008



prix / awards


Prix De Meurs-François Prize pour la thèse de doctorat, 2009. - Yann-Aël LE BORGNE

Award Solvay pour le mémoire - Souhaib BEN TAIEB

Award Solvay pour la thèse de doctorat - Benjamin HAIBE KAINS

Solvay Award for his PhD Thesis in Computer Science, 2014 - Catharina OLSEN



savoir-faire/équipements / know-how, equipment


Laboratoire avec capteurs sans-fil



mots clés pour non-spécialistes / keywords for non-specialists


analyse des données apprentissage automatique bioinformatique biologie numérique la théorie des jeux


disciplines et mots clés / disciplines and keywords


bioinformatics dynamics fraud detection genomics genomic selection mobility


codes technologiques DGTRE


Bio-informatique, informatique médicale, biométrie Intelligence artificielle Sciences de l'ordinateur, analyse numérique, systèmes, contrôle