جستجوی استاد راهنمای دانشگاهی

• Artificial Intelligence
• Optimization Algorithms
• Machine Learning
• +۵ مورد دیگر

Grégoire DANOY is a Researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability, and Trust (SnT) and Head of the Parallel Computing and Optimization Group (PCOG). He specializes in artificial intelligence, with a focus on optimization algorithms, machine learning, and swarm intelligence. His work addresses challenges in cloud computing, high-performance computing, smart mobility, and unmanned autonomous systems like drone swarms. He has authored over 150 publications, including articles in IEEE Transactions and conferences like NeurIPS and GECCO. He currently leads major projects such as UltraBO (€1.019M), ADHOC (€1.291M), and SERENITY (€1.228M), collaborating with institutions in France and Poland. Education: PhD in Computer Science (2008) from École Nationale Supérieure des Mines de Saint-Étienne, Master’s in Computer Science (2004), and Industrial Engineering Degree (2003) from Luxembourg University of Applied Sciences. Research Interests: Developing novel AI techniques for solving large-scale optimization problems, with applications in distributed systems, autonomous robotics, and federated learning. He emphasizes scalable solutions for combinatorial challenges using parallel computing and swarm intelligence. Grants & Projects: Principal Investigator for EU-funded initiatives like ADARS (2021–2024) and FNR PoC/SIMMS (2019–2021). His work bridges academia and industry, with technology transfer projects in autonomous robot swarms. Awards: Recognitions include the Best Student Paper Nomination (2022), IEEE CybConf Best Paper Award (2017), and ACM GECCO nominations (2016, 2009). He serves on the editorial board of Engineering Applications of Artificial Intelligence (EAAI). Labs & Teams: Leads the Parallel Computing and Optimization Group (PCOG), focusing on interdisciplinary research in AI and distributed systems. He also contributes to outreach programs like FNR's Researchers at School.

• Parallel Computing
• Optimization Techniques
• High-Performance Computing (HPC)
• +۵ مورد دیگر

Pascal BOUVRY is a full Professor of parallel computing and optimization techniques at the University of Luxembourg's Department of Computer Science , within the Faculty of Science, Technology and Medicine (FSTM) . He currently serves as the Dean of the FSTM and leads the Parallel Computing and Optimisation group . Additionally, he oversees the University's High Performance Computing (HPC) infrastructure. His research focuses on parallel computing, optimization algorithms, distributed systems, and their applications in bioinformatics and distributed environments. Prof. BOUVRY holds a Ph.D. in Computer Science from the University of Grenoble (France) and has extensive industry experience, including roles as CEO/CTO of tech firms and leadership in telecom and financial services sectors. He has pioneered initiatives like the Master in High-Performance Computing and Technopreneurship programs at the University of Luxembourg. His research interests span GPU programming paradigms , federated learning , distributed optimization , and trustworthy AI . Recent work includes advancements in HPC education frameworks, federated learning frameworks (e.g., FedPref), and quantum circuit optimization. He contributes to editorial boards of journals like IEEE Transactions on Sustainable Computing and IEEE Cloud Computing Magazine. Prof. BOUVRY has held international leadership roles, including as Honorary Chair of conferences and active participation in technical committees. His educational efforts emphasize bridging academic research with industrial applications, particularly in HPC and AI.

• Software Engineering
• Dependable Systems
• Formal Methods
• +۴ مورد دیگر

Nicolas GUELFI is a Full Professor at the University of Luxembourg's Faculty of Science, Technology and Medicine (Department of Computer Science). He has been a faculty member since 1999, leading the Lassy laboratory for over a decade and co-founding the academic Bachelor in Computer Science program. His research focuses on software engineering, dependability, resiliency, formal methods, and AI-driven systems. He has coordinated over 60 staff members on international projects and contributed to standards like ERCIM's RISE and SERENE working groups. Education: PhD in Formal Methods (Paris XI, 1999) Teaching: Software Engineering across universities (Paris XI, EPFL, EPITA) since 1994 Roles: ERCIM Executive Committee member, Expert for Judicial Conformance Trials Research interests emphasize requirements engineering (MESSIR framework), resilient systems, and AI integration. He has pioneered tools like SEMKIS-DSL and MESAS for system specification. His work bridges academia and industry through DevOps education programs and standards analysis. Publications span formal verification, Petri nets, and model-driven engineering. He has advised on high-stakes litigation involving €0.1M–€100M projects, leveraging his expertise in system conformance.

Stéphane BORDASمشاهده پروفایل

استاد

• Computational Mechanics
• Biomechanics
• Finite Element Methods
• +۶ مورد دیگر

Stéphane BORDAS is a Full Professor in Computational Mechanics at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), leading the Computational Mechanics (Legato) research group. His work focuses on free boundary problems, method development for complex geometries, and applications in fracture mechanics, biomechanics, and computational engineering. He previously held roles at Cardiff University and the Swiss Federal Institute of Technology in Lausanne (EPFL). His research integrates computational methods like XFEM, isogeometric analysis, and meshfree techniques to address challenges in engineering and medicine. Education: Ph.D. in Theoretical and Applied Mechanics, Northwestern University (2003) M.Sc. in Civil Engineering, École Spéciale des Travaux Publics and Northwestern University (1999) Research Interests: Computational Mechanics, Biomechanics, Finite Element Methods, Fracture Mechanics, High-Performance Computing, Isogeometric Analysis. Grants & Projects: ERC Starting Grant (RealTCut) for surgical simulation and material cutting FP7 ITN INSIST for meshless methods Labs/Teams: Computational Mechanics (Legato) Group at the University of Luxembourg. His work bridges academia and industry, with applications in aerospace, biomedical engineering, and materials science. He is active in open-source software development, including codes for XFEM, isogeometric analysis, and meshfree methods.

• Autonomous Driving
• V2X Communication
• Robotics
• +۳ مورد دیگر

Dr. Gamal ELGHAZALY is a Research scientist at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), affiliated with the Ubiquitous and Intelligent Systems department. His work focuses on autonomous driving technologies, V2X communication systems, robotics, and control systems. Key research areas include 5G-enabled teleoperated driving, high-definition map construction, and 3D perception for autonomous vehicles. He has developed platforms like RoboCar and contributed to frameworks like FastCycle for modular automated systems. Research interests span robotics kinematics (planar parallel manipulators), adaptive control methodologies (sliding mode control, fuzzy logic), and hybrid systems (cable-driven robots). His publications emphasize real-time motion planning, sensor fusion, and safety-critical systems. Recent works (2023-2025) highlight advancements in 4D perception, cloud-assisted 3D reconstruction, and V2X-enabled collaborative systems. Publications from 2017-2018 reflect early contributions on parallel manipulator modeling and control, while recent efforts focus on integrating cutting-edge technologies like 5G and edge computing into autonomous systems. His work bridges theoretical robotics with practical implementations, addressing challenges in both dynamic environments and industrial applications.

• Machine Learning
• Deep Learning
• Multi-Agent Systems
• +۳ مورد دیگر

Hedieh HADDAD is a Doctoral researcher at the University of Luxembourg, affiliated with the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the Parallel Computing & Optimization Group (PCOG), headed by Prof. Pascal Bouvry. Her research focuses on Machine Learning (particularly Deep Learning), Multi-Agent Systems, Optimization Algorithms, and Intelligent Transportation Systems. Education : She holds a Master’s degree from Shahid-Beheshti University of Tehran (Iran) , completed in 2019. Previously, she served as a research assistant at the Artificial Intelligence Research Lab. Research Interests : Hedieh’s work emphasizes advanced Machine Learning applications, Multi-Agent Systems for complex problem-solving, and Optimization Algorithms for Intelligent Transportation Systems. Her interdisciplinary approach bridges theoretical advancements with real-world deployment challenges in transportation and computational efficiency. Affiliations : She is part of the Parallel Computing & Optimization Group (PCOG) at SnT, focusing on high-performance computing and optimization strategies.

• Edge Computing
• Satellite Communications
• Network Virtualization
• +۳ مورد دیگر

Haffay ABREHA is a Doctoral researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), within the SigCom department. His research focuses on advanced networking technologies, including edge computing, satellite communications, and federated learning. He holds a strong interest in network virtualization, resource management, and optimizing mission-critical applications in distributed systems. Contact: haftay.abreha@uni.lu Research Interests: ABREHA’s work spans satellite edge networks, federated learning frameworks, fog computing monitoring, and resource-aware caching strategies. He explores how machine learning can enhance adaptive systems and improve network reliability in dynamic environments. Recent studies emphasize fairness-aware VNF scheduling, SDN/NFV integration, and optimizing content delivery in multi-layer satellite architectures. Labs/Teams: Active member of the SigCom research group at SnT, contributing to interdisciplinary projects on secure and reliable communication systems.

Carol MARTINEZ LUNAمشاهده پروفایل

پژوهشگر

• Space Robotics
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Dr. Carol Martinez Luna is a Research Scientist and Deputy Head of the Space Robotics group (SpaceR) at the SnT Interdisciplinary Centre, University of Luxembourg. She holds a PhD in Robotics and Automation from Universidad Politécnica de Madrid (2013) and previously served as an Assistant Professor at Pontificia Universidad Javeriana, Colombia. Her research spans Space Robotics , Computer Vision , and Machine Learning , with current focus areas including: Multi-purpose manipulation for planetary/orbital robots Perception systems for autonomous space operations Domain adaptation to bridge simulation-to-reality gaps On-ground testing of orbital technologies Recent publications (2022-2025) predominantly address space debris removal , autonomous grasping , and robotic perception , with heavy emphasis on machine learning techniques and high-fidelity simulation validation. Notable trends include generative models for manipulation and adaptive control under uncertainty. She actively advises 7 PhD students and 9 Master's candidates on topics ranging from space manipulation to vision-based safety systems. Her lab develops experimental platforms for orbital scenario emulation and collaborates on ESA-funded debris removal initiatives.

Daniele Perloمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Deep Learning
• Medical Imaging
• +۳ مورد دیگر

Daniele Perlo is a Postdoctoral Fellow at Luxembourg Institute of Health specializing in Brain Imaging & Neuro Epidemiology. His research bridges artificial intelligence and medical imaging to advance neuro-oncology diagnostics, with emphasis on brain tumor hypoxia detection and MRI analysis. His primary research interests include Artificial Intelligence , Deep Learning , Medical Imaging , and Neuroimaging , with specific focus on hypoxia detection in brain tumors using multiparametric MRI. He develops AI models for medical image synthesis, artifact correction, and tumor characterization, translating computational methods into clinical applications for improved cancer diagnostics. Analysis of Perlo's 15 recent publications (2021-2024) reveals a concentrated research trajectory in AI-driven medical imaging. Key themes include brain tumor hypoxia prediction through MRI-PET synthesis, MRI artifact correction via k-space deep learning, and creation of specialized medical datasets (Unitobrain, Unitochest, Unitopatho). His work demonstrates increasing emphasis on practical clinical implementations, particularly in neuro-oncology and multi-organ cancer imaging, leveraging large-scale deep learning frameworks. Perlo participated as a key contributor in the "Pseudo PET" research project (2022-2024), collaborating with principal investigators Keunen and Niclou to develop deep learning methods for synthesizing pseudo-PET images from MRI data to assess brain tumor hypoxia without radiation exposure. This HPC-intensive project utilized multiple GPUs for medical image processing in neuro-oncology applications. He operates within the Brain Imaging & Neuro Epidemiology research group at Luxembourg Institute of Health, focusing on AI applications for neuroimaging analysis and epidemiological studies of brain disorders through advanced computational techniques.

• Bioinformatics
• Systems Biology
• High-Performance Computing
• +۵ مورد دیگر

Reinhard SCHNEIDER is a Full Professor and Chief Scientist in Bioinformatics at the University of Luxembourg's Luxembourg Centre for Systems Biomedicine (LCSB), leading the Bioinformatics Core facility. He has extensive experience in bioinformatics, computational biology, and data integration, having previously held roles at EMBL (1994-2010) and co-founded LION bioscience AG. His research focuses on systems biology, disease mechanism modeling, and high-performance computing. He has over 90 publications and actively contributes to open science initiatives like FAIR data principles. Education: PhD in Biology from University of Heidelberg, Germany. Research Interests: Systems Biology Computational disease modeling Data Science and Machine Learning High-Performance Computing Knowledge Management FAIR data standards Publications Trends: Recent work emphasizes disease maps (e.g., ISD Map, AsthmaMap), computational tools (MINERVA, COBREXA), and translational applications in Parkinson’s disease, epilepsy, and immune-related disorders. Highlights include repurposing drugs, predictive modeling of postoperative delirium, and multi-omics analysis of immunotherapy responses. Grants/Advising: Leads the Luxembourg Elixir node and collaborates with EU-funded projects like BioCog (predicting cognitive decline post-surgery). Involved in startups and industry partnerships in Germany and Luxembourg. Labs/Teams: Heads the LCSB Bioinformatics Core, develops tools like Arena3Dweb for 3D network visualization, and manages platforms like BioKC for model curation.

• Big Data
• Probabilistic Databases
• Information Extraction
• +۷ مورد دیگر

Prof. Martin Theobald is a Full Professor in Computer Science with a focus on Big Data at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), Department of Computer Science. He leads the Big Data research group and serves as the Study Program Director for the Bachelor in Computer Science (BiCS) program since 2022. Previously, he held professorial roles at the University of Ulm (Co-Director of the Institute for Databases and Information Systems) and the University of Antwerp (Advanced Database Research and Modeling group). His academic journey includes a joint doctoral degree from Saarland University and the Max Planck Institute for Informatics in 2006, followed by a postdoctoral research stint at Stanford University's Infolab from 2006 to 2008. Education: PhD in Computer Science (2006) from Saarland University and Max Planck Institute for Informatics, Saarbrücken Postdoctoral Researcher at Stanford University's Infolab (2006–2008) Research Interests: Martin's work centers on Big Data systems, probabilistic databases, distributed stream processing, and machine learning. He has extensively explored techniques such as asynchronous iterative routing (AIR), real-time neural network training from data streams (TensAIR), and large-scale information extraction leveraging statistical methods. His contributions span scalable architectures, optimization frameworks for deep learning, and knowledge graph construction with high precision and recall. Publications: His recent work reflects a focus on real-time systems, convergence analysis of distributed algorithms, and robust embedding techniques. Key themes include the development of efficient dataflow engines, probabilistic database models, and frameworks for handling uncertain and temporal data. Scientific Awards: None explicitly mentioned in the provided texts. Advising & Grants: Martin has advised numerous students (though names are not listed) and contributed to over 100 international conferences/workshops as a PC member, reviewer, and organizer. He has held editorial roles, including Area Editor for Elsevier’s 'Information Systems' (2013–2018) and PVLDB (2020–2021). His research has driven advancements in Jeopardy! clue-solving SPARQL engines, automotive navigation systems under uncertainty, and the design of open-domain information extraction architectures. Labs/Teams: Current leadership of the Big Data research group at the University of Luxembourg. Previously led the 'Ranking and Uncertain Data Management' group at the Max Planck Institute for Informatics (2008–2012). Key collaborations include the development of the AIR dataflow engine and the YAGO-QA knowledge base system.

• Computational Mechanics
• Numerical Methods (Meshfree/XFEM/Isogeometric)
• Mixed Variational Methods
• +۳ مورد دیگر

Jack HALE is a Research Scientist at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), Department of Engineering. He joined Prof. Stéphane Bordas' team in 2013, focusing on computational mechanics and numerical methods. His work integrates advanced techniques like meshfree methods, XFEM, and isogeometric analysis to address challenges in solid mechanics and high-performance computing. Education : PhD in Aeronautics, Imperial College London (2009-2013), supervised by Dr. Pedro M. Baiz Villafranca. MEng in Engineering, University of Bristol (2004-2008). Research exchange at Rice University (2006-2007) on cross-flow filtration processes. Research Interests : Implicit boundary methods for medical image-based simulations. Development of scalable meshfree/XFEM/isogeometric analysis frameworks. Mixed variational methods to resolve locking phenomena in solid mechanics. High-performance computing for distributed parallel systems. Publications & Software : His 50+ publications emphasize open-access research via ORBilu, with a focus on FEniCSx-based tools (e.g., DOLFINx, FEniCS-shells). Recent work addresses Bayesian model selection, melt instability identification, and SAR data assimilation in aquifer modeling. Collaborations : Open to academic/industrial partnerships in computational mechanics, material science, and biomedical engineering.