This research programme represents a strategic collaboration between Luxembourg Institute of Standardisation, Accreditation, Safety, and Quality of Products and Services (ILNAS) and SnT at the University of Luxembourg. This multidisciplinary initiative is aligned with Luxembourg's National Technical Standardisation Strategy 2024-2030 and emphasizes sustainability across various growth sectors for Luxembourg’s economy. The programme includes cutting-edge research activities and promotes active contributions to international standardisation efforts.
Real-world applications in Optimisation (Vehicle Routing Problem) and Machine Learning (Image recognition) are very challenging due to their large-scale structure. To cope with this issue, decomposition techniques have been historically introduced in mathematical programming. These techniques split a given problem into smaller and more affordable subproblems which are subsequently solved. Column Generation is a reference on this matter but suffers from certain pathologies that we first propose to tackled using Learning approaches. In the same vein, Machine Learning (ML) models keeps growing and become time-consuming and energy-hungry models that can only be tackled with High-Performance Computing platforms. Nonethless, ML experts and academics did not inspired from mathematical programming and the decompostion techniques developed in the same context but in a different field. In this project, we are going to transpose and adapt decomposition techniques to ML models which are intrisically otpimisation problems.
The SERENITY project aims to design and develop a brokering system dedicated to space data that brings optimised costs/revenues and QoS by advancing the state of the art in the field of data lakes, HPC, and brokering optimization.
The UltraBO project aims to design and implement novel parallel hybrid optimization algorithms for modern supercomputers. Many experiments reported in the literature demonstrate that high-quality results are obtained through hybridization. However, finding efficient and effective hybridization schemes is challenging and tedious. On the other hand, according to Top500 modern supercomputers are increasingly large (millions of cores), heterogeneous (various CPUs, GPU, FPGA, …) and less and less reliable (Mean Time Between Failures - MTBF<1h) making their programming increasingly complex. The design and implementation of parallel algorithms for these ultra-scale supercomputers is still in its infancy especially in combinatorial optimization.
The ADARS (Automating the Design of Autonomous Robot Swarms) project aims to propose a unique approach to automatically generate behaviours for distributed aerospace and space systems (DASS) thanks to a cross-fertilisation between multi-objective optimisation and machine learning techniques. ADARS will demonstrate through specifically designed software simulations and real field tests with multi-rotor drones, that state-of-the-art results can be obtained on two challenging DASS applications: swarm formation for a counter UAV system and swarm formation of small satellites for asteroid observation.
The proposed follow-up ILNAS-SnT research programme 2021-2024 is consciously sector-oriented and targets three highly relevant growth sectors for Luxembourg’s economy, in conformity with the national technical standardization strategy 2020-2030, that will deepen data and AI capabilities and enable a steady potential for innovation. The three sectors are a) ICT, b) Aerospace, and c) Construction. The economic power for these three domains is strongly determined by normative values notably related to trustworthiness, security, and privacy. Consequently, policies, regulations, and standards are required to be established to enhance trust in each particular domain-specific ecosystem
The HUNTED project (Heterogeneous multi-swarms of UNmanned auTonomous systEms for mission Deployment) aims at designing a novel generation of mobility models for heterogeneous multi-swarms of Unmanned Autonomous Systems (UAS) for surveillance and tracking of imminent threats.
Following the successful launch of the University Certificate “Smart ICT for business innovation” in September 2015 at the University of Luxembourg in partnership with the Institut luxembourgeois de la normalisation, de l'accréditation, de la sécurité et qualité des produits et services (ILNAS); the interdisciplinary center for security reliability and trust (SnT) and ILNAS entered a partnership to jointly develop Luxembourg as a European centre of excellence and innovation for secure, reliable, and trustworthy Smart ICT systems and services.
The ASIMUT project aims at decreasing the operator workflow during a surveillance mission lead by swarms of UAVs. To this end, innovative algorithms are developed increasing detection and data fusion capabilities, and increasing the swarms of UAVs autonomy. Role: Task leader
Internet Shopping Optimization Project (IShOP) is an INTER POLLUX project, co-funded by Luxembourg National Research Funds (FNR) and the Polish National Research Centre for Research and Development (NCBiR). This project proposes innovative and realistic models for different typical online shopping operations, supported by strong mathematical and operational research fundamentals, and well balanced with lightweight computational algorithms. These models are designed in order to allow the optimization of such transactions. Finding accurate solutions to the defined problems implies both lowering customer expenses and favouring market competitiveness. Role: Work package leader.