Dr. Grégoire Danoy is a Research Scientist at the University of Luxembourg (UL) and Deputy-Head of the Parallel Computing and Optimisation Group (PCOG).
His current research interests include artificial intelligence techniques applied to unmanned autonomous systems (e.g., drones), satellite communications, vehicular networks, bioinformatics, high-performance and cloud computing.
Authorisation to direct research (ADR), 2019
University of Luxembourg, Luxembourg
PhD in Computer Science, 2008
Ecole des Mines of Saint-Etienne, France
Master in Computer Science, 2004
Ecole des Mines of Saint-Etienne, France
Industrial Engineer degree in Computer Science, 2003
Luxembourg University of Applied Sciences (IST)
* Research in Artificial Intelligence, Optimisation, Smart Cities, Vehicular Networks
* Research funding acquisition: FNR (Luxembourg), Eureka-Celtic (EU)
* Project management: Work package and task leader
* Daily advisory to doctoral and postdoctoral researchers
* Reviewer for international scientific conferences and journals
* Teaching optimisation at Bachelor and Master levels
* Initiator/Leader of the Dafo project, a distributed multi-agent framework for business problems optimisation
* Teaching UML and algorithmics at Bachelor and Master levels
Research Projects
Technology Transfer Projects
Past and current teaching activities at University of Luxembourg
Education Management:
Bachelor Level:
Industrial Engineering Level:
Master Level:
PhD level
Supervisor
Daily Advisor
Master & Bachelor level
Bachelor Level
Master level
Participation to PhD Boards
Identifying protein complexes in protein-protein interaction (ppi) networks is often handled as a community detection problem, with algorithms generally relying exclusively on the network topology for discovering a solution. The advancement of experimental techniques on ppi has motivated the generation of many Gene Ontology (go) databases. Incorporating the functionality extracted from go with the topological properties from the underlying ppi network yield a novel approach to identify protein complexes. Additionally, most of the existing algorithms use global measures that operate on the entire network to identify communities. The result of using global metrics are large communities that are often not correlated with the functionality of the proteins. Moreover, ppi network analysis shows that most of the biological functions possibly lie between local neighbours in ppi networks, which are not identifiable with global metrics. In this paper, we propose a local community detection algorithm, (lcda-go), that uniquely exploits information of functionality from go combined with the network topology. lcda-go identifies the community of each protein based on the topological and functional knowledge acquired solely from the local neighbour proteins within the ppi network. Experimental results using the Krogan dataset demonstrate that our algorithm outperforms in most cases state-of-the-art approaches in assessment based on Precision, Sensitivity, and particularly Composite Score. We also deployed lcda, the local-topology based precursor of lcda-go, to compare with a similar state-of-the-art approach that exclusively incorporates topological information of ppi networks for community detection. In addition to the high quality of the results, one main advantage of lcda-go is its low computational time complexity.
In this article, we propose SuSy-EnGaD, a surveillance system enhanced by games of drones. We propose three different approaches to optimise a swarm of UAVs for improving intruder detection, two of them featuring a multi-objective optimisation approach, while the third approach relates to the evolutionary game theory where three different strategies based on games are proposed. We test our system on four different case studies, analyse the results presented as Pareto fronts in terms of flying time and area coverage, and compare them with the single-objective optimisation results from games. Finally, an analysis of the UAVs trajectories is performed to help understand the results achieved.