Research into novel feature selection techniques and learning algorithms applied to a variety of industrial domains.
Research into behavioral aspects of decision making via (evolutionary) game theory and simulations to better understand human behavior.
Research into scalable big data solutions for the analysis of complex (e.g. high throughput, high volume, high velocity) data from ICT and medicine
Research and development into medical and biological questions using machine learning, statistical and modelling approaches
Experimentally and theoretically analysing human - human and human - AI interactions in order to develop meaningful models.
Reducing the number of features in order to identify those that are most relevant for classification, regression etc.
learning patterns from labelled and unlabelled data using classification, regression and clustering techniques.
How to adapt to prior decisions and to anticipate future choices of agents in strategic situations.
Developing computational methods and software tools for the analysis and interpretation of biological data.
The theory of strategic decision-making in cooperative and non-cooperative situations.
Analysis and multi-step ahead forecasting of multivariate temporal data.
Development of publicly available software packages and sharing code analysis through online repositories.
Agent learning by social imitation or survival of the fittest.
P`ere, Nathaniel Mon
Statistical biophysics of hematopoiesis and growing cell populations PhD Thesis
2020, (Funder: Universite Libre de Bruxelles).
@phdthesis{info:hdl:2013/314684,
title = {Statistical biophysics of hematopoiesis and growing cell populations},
author = {Nathaniel Mon P`ere},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/314684},
year = {2020},
date = {2020-01-01},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Papadimitriou, Sofia
2020, (Funder: Universite Libre de Bruxelles).
@phdthesis{info:hdl:2013/312576,
title = {Towards multivariant pathogenicity predictions: Using machine-learning to directly predict and explore disease-causing oligogenic variant combinations},
author = {Sofia Papadimitriou},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/312576},
year = {2020},
date = {2020-01-01},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Pollaris, Arnaud; Bontempi, Gianluca
Latent Causation: An algorithm for pairs of correlated latent variables in Linear Non-Gaussian Structural Equation Modeling Miscellaneous
2020, (Conference: BNAIC/BENELEARN (19 & 20 November 2020: Leiden (online))).
@misc{info:hdl:2013/314680,
title = {Latent Causation: An algorithm for pairs of correlated latent variables in Linear Non-Gaussian Structural Equation Modeling},
author = {Arnaud Pollaris and Gianluca Bontempi},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/314680/3/BNAICBENELEARN_2020_Final_paper.pdf},
year = {2020},
date = {2020-01-01},
abstract = {This paper addresses the problem of inferring causation in a pair of linearly correlated continuous latent variables. We first discuss the limitations of the Direction Dependance Analysis (DDA) approach and then introduce the Latent Causation (LC). Five variants (in terms of dependency statistic) of the LC algorithm are assessed with ROC curves, then we consider the case of a latent confounder (uniform or chi-square distributed). While the distribution and the correlations of the latent confounder influence the accuracy, experimental results show the robustness of the method using bootstrapped p-values. Implications and limits of the experimental results are then discussed together with future directions.},
note = {Conference: BNAIC/BENELEARN (19 & 20 November 2020: Leiden (online))},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Colaprico, Antonio; Olsen, Catharina; Bailey, Matthew; Odom, Gabriel G J; Terkelsen, Thilde; Silva, Tiago Chedraoui; Olsen, André Vidas; Cantini, Laura; Zinovyev, Andrey; Barillot, Emmanuel; Noushmehr, Houtan; Bertoli, Gloria; Castiglioni, Isabella; Cava, Claudia; Bontempi, Gianluca; Chen, Xi Steven; Papaleo, Elena
Interpreting pathways to discover cancer driver genes with Moonlight Journal Article
In: Nature communications, vol. 11, no. 1, 2020, (DOI: 10.1038/s41467-019-13803-0).
@article{info:hdl:2013/301750,
title = {Interpreting pathways to discover cancer driver genes with Moonlight},
author = {Antonio Colaprico and Catharina Olsen and Matthew Bailey and Gabriel G J Odom and Thilde Terkelsen and Tiago Chedraoui Silva and André Vidas Olsen and Laura Cantini and Andrey Zinovyev and Emmanuel Barillot and Houtan Noushmehr and Gloria Bertoli and Isabella Castiglioni and Claudia Cava and Gianluca Bontempi and Xi Steven Chen and Elena Papaleo},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/301750/1/doi_285394.pdf},
year = {2020},
date = {2020-01-01},
journal = {Nature communications},
volume = {11},
number = {1},
abstract = {Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.},
note = {DOI: 10.1038/s41467-019-13803-0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Simar, Cédric; Cebolla, Ana Maria; Chartier, Ga"elle; Petieau, Mathieu; Bontempi, Gianluca; Berthoz, Alain; Cheron, Guy
Hyperscanning EEG and Classification Based on Riemannian Geometry for Festive and Violent Mental State Discrimination Journal Article
In: Frontiers in Neuroscience, vol. 14, 2020, (DOI: 10.3389/fnins.2020.588357).
@article{info:hdl:2013/317394,
title = {Hyperscanning EEG and Classification Based on Riemannian Geometry for Festive and Violent Mental State Discrimination},
author = {Cédric Simar and Ana Maria Cebolla and Ga{"e}lle Chartier and Mathieu Petieau and Gianluca Bontempi and Alain Berthoz and Guy Cheron},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/317394/3/pdf},
year = {2020},
date = {2020-01-01},
journal = {Frontiers in Neuroscience},
volume = {14},
abstract = {Interactions between two brains constitute the essence of social communication. Daily movements are commonly executed during social interactions and are determined by different mental states that may express different positive or negative behavioral intent. In this context, the effective recognition of festive or violent intent before the action execution remains crucial for survival. Here, we hypothesize that the EEG signals contain the distinctive features characterizing movement intent already expressed before movement execution and that such distinctive information can be identified by state-of-the-art classification algorithms based on Riemannian geometry. We demonstrated for the first time that a classifier based on covariance matrices and Riemannian geometry can effectively discriminate between neutral, festive, and violent mental states only on the basis of non-invasive EEG signals in both the actor and observer participants. These results pave the way for new electrophysiological discrimination of mental states based on non-invasive EEG recordings and cutting-edge machine learning techniques.},
note = {DOI: 10.3389/fnins.2020.588357},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
March 30th 2023, 12:15, room S.R42.4.502 (Solbosch) Machine learning is a powerful tool to support business decision-making. For instance, predictive models can be learned from data to anticipate the future and to make informed decisions, with the eventual objective of optimizing the efficiency and...
March the 23th, 2023 at 3:00PM, on Teams: link. In the latest years, scholars started focusing on how to develop statistical tool for the analysis of population of complex data, such as high-dimensional vector data and functions, but also more complex data objects such as...
While the promise of more knowledge makes the use of expert groups appealing, reality suggests that making good use of a larger number of experts is not so simple. Beliefs held by individuals tend to spread, which in turn makes decision-making by collectives less reliable....
Elias Fernandez and co-authors were awarded the best poster award at the AAAI 2021 conference workshop on AI for Behavior Change. The poster was titled Delegation to autonomous agents promotes cooperation on collective-risk dilemmas. You can find the abstract below. This research is a collaboration...
Axel Abels and Tom Lenaerts were, together with their collaborators, awarded with the Best Paper award at the International Computational Collective Intelligence (ICCCI) for the paper entitled “How expert confidence can improve collective decision-making in contextual multi-armed bandit problems. Below the abstract of the paper....
NESTA UK reporting on the behavioural experiments we performed in collaboration with the VUB AI lab and Francisco C. Santos team in INESC-ID in Lisbon. Want to know more? Follow this link to get information and download the full report We tested whether using AI...
Title: “Dealing with noisy phenotypes to build more robust predictors of drug response in cancer”By: Dr. Benjamin Haibe-KainsWhen: Tuesday 10th December, at 15.00Where: Université libre de Bruxelles, Campus de la Plaine (http://www.ulb.ac.be/campus/plaine/plan.html), NO Building, 8th Floor, NO8.08, Rotule room (http://www.ulb.ac.be/campus/plaine/plan-NO.html), ...
This year’s Benelux AI (BNAIC)and Machine Learning (Benelearn) conferences will be organised in Brussels. The conferences are hosted by the ULB Machine Learning Group and the AI lab of the VUB. More information can bound on the website http://ai-synergies.be/
The Flemish news site VRT nieuws and the French news paper L’Echo are reporting on the excellent work of the oligogenic team. Congratulations! L’Echo: À Bruxelles, les maladies rares sont pistées grâce à l’intelligence artificiellehttps://www.lecho.be/r/t/1/id/10133875 VRT Nieuws : VUB en ULB ontwikkelen AI-methode om genetische...
https://actus.ulb.be/fr/actus/recherche/l-intelligence-artificielle-pour-identifier-les-maladies-rares
The call for papers for the Benelux conferences on Artificial Intelligence are launched. More information can be found on http://bnaic19.brussels and http://benelearn19.brussels
We are looking for a PhD student for the FWO funded project DELICIOS. The research will be performed with the groups of Prof. Pieter Simoens (UG – IDlab), Prof. Jo Pierson (VUB – SMIT) and Prof. Tom Lenaerts (VUB/ULB – AI lab/MLG). This works builds...
Congratulations to Axel Abels for having his paper entitled “Dynamic Weights in Multi-Objective Deep Reinforcement Learning”, accepted for presentation and for publication in the conference proceedings at ICML 2019!!
Today a coalition of academic, public and private sector specialists launched the Belgian strategy for AI, originally called AI4Belgium. All information concerning the recommendations from this panel can be found on the coalition website. Summary of recommendations of the AI 4 Belgium Coalition: We have...
When and where: March 29th at 3PM, room: ForumC, Campus La Plaine Abstract Nowadays, there are applications in which the data are modelled best not as persistent tables, but rather as transient data streams. In this keynote, we discuss the limitations of current machine learning...
Applications are invited for full-time post-doctoral (3 years) research position at the Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Belgium. https://euraxess.ec.europa.eu/jobs/211909
A MLG team (G. Bontempi, Y. Le Borgne, J. De Stefani) was awarded (Honorable Mention Research Paper) in Tokyo at the 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA ’17) for the paper entitled “A Dynamic Factor Machine Learning Method for...
MLG takes part to the new ULB Specialized Master in Data Science and Big Data https://www.ulb.ac.be/programme/en/MS-BGDA/index.html. Details on content and program can be found on http://www.ulb.ac.be/di/map/gbonte/ftp/BDMaster.pdf
Applications are invited for full-time post-doctoral (3 years) research position at the Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Belgium. https://euraxess.ec.europa.eu/jobs/211909