جستجوی استاد راهنمای دانشگاهی

Marcella ASTRID is a Researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), specifically within the CVI2 department. Her research focuses on advanced machine learning techniques applied to cybersecurity and computer vision challenges, particularly in the detection of synthetic media (deepfakes) and anomaly detection systems. She specializes in developing robust models that address vulnerabilities in adversarial scenarios and improve generalizability across diverse datasets. Key research areas include deepfake detection through spatio-temporal analysis, anomaly detection via novel training paradigms, and efficient neural network compression for resource-constrained environments. Her work bridges theoretical advancements in machine learning with practical applications in surveillance systems, autonomous robotics, and cybersecurity infrastructure. Recent contributions emphasize leveraging autoencoder weaknesses, pseudo anomaly generation, and localized attention mechanisms to enhance detection accuracy and model robustness. Marcella’s research also explores cross-modal learning (audio-visual synchronization) and semi-supervised methodologies to tackle data scarcity issues in critical domains like battery thermal imaging and multi-camera traffic classification. She actively publishes in top-tier venues and collaborates on interdisciplinary projects addressing real-world security and reliability challenges.

• Computer Vision
• Deep Learning
• Cybersecurity
• +۳ مورد دیگر

Juan Carlos Merlano Duncan is a Research Scientist at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), specializing in the SigCom department. He contributes to the SATSENT project with Prof. Ottersten and Dr. Symeon Chatzinotas, focusing on satellite sensor networks for spectrum monitoring. His roles include advancing technologies in wireless communications, remote sensing, and embedded systems. **Education:** Bachelor's in Engineering from Universidad del Norte (2004) M.Sc. and Ph.D. in Telecommunications from Universitat Politècnica de Catalunya (2009 and 2012) **Research Interests:** Wireless communications, remote sensing, distributed systems, Software Defined Radios (SDR), embedded systems, and AI-driven satellite technologies. His work emphasizes FPGA design, radar systems (e.g., SABRINA project), and cognitive radio networks. **Publications:** Recent work spans AI in satellite communications, digital beamforming, and energy-efficient MIMO systems. Notable themes include STAR-RIS networks, onboard image classification, and THz reconfigurable surfaces. **Grants & Advising:** No explicit grants or advisees listed, though his projects imply collaborative research funding. He has led FPGA implementations for radar and cognitive radio systems. **Labs & Teams:** Core member of SnT’s SigCom group, contributing to satellite telecommunication testbeds using COTS devices and onboard processing architectures.

• Wireless Communications
• Remote Sensing
• Distributed Systems
• +۶ مورد دیگر

Dr. Tedros Salih ABDU is a Postdoctoral researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), specifically within the SigCom department. His work focuses on advancing satellite communication systems, particularly leveraging artificial intelligence and machine learning for optimizing resource management, ground segment planning, and network efficiency in non-geostationary and geostationary satellite constellations. His research interests span satellite communication, AI-driven network solutions, resource allocation strategies, and high-throughput systems. He explores challenges in demand-aware adaptive systems, interference mitigation, and energy-efficient designs for next-generation satellite networks. Recent publications highlight trends in AI applications for SATCOM, ground station optimization for NGSO constellations, and precoding techniques for bandwidth and power efficiency. His work contributes to both theoretical advancements and practical solutions for scalable satellite infrastructure. Dr. ABDU's affiliations include the SnT Centre at the University of Luxembourg, where he collaborates on interdisciplinary projects. He is based at the Belval Campus in Luxembourg City.

• Satellite Communication
• Artificial Intelligence
• Telecommunications
• +۳ مورد دیگر

Jacques Klein is Full Professor of Software Engineering and Mobile Security at the University of Luxembourg. His research bridges software engineering principles with security challenges in modern computing environments. Research focuses on software security, mobile systems analysis, and increasingly on large language model applications for program analysis. Recent work examines Android ecosystem security, infrastructure-as-code vulnerabilities, and AI-enhanced program repair techniques. Klein's 2025 publications demonstrate strong emphasis on LLM capabilities for automated compliance checking, malware detection, and program understanding. The research combines empirical software engineering with AI methodologies to address security and maintenance challenges.

• Software Engineering
• Mobile Security
• Program Analysis
• +۲ 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.

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

Abd El Rahman Shabayek is a Research Scientist at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, leading research in AI-driven Computer Vision for space and energy sectors. His interdisciplinary background includes contributions across prestigious universities in France, the UK, Spain, and Egypt. He holds an Erasmus Mundus MSc and a PhD in Computer Vision and Robotics from France. Current Affiliations: CVI² Research Group (SnT), University of Luxembourg. Former Roles: Assistant Professor at Suez Canal University, adjunct positions at Sinai University and the Information Technology Institute. Research focuses on optimizing deep neural networks for spatial/terrestrial applications, with over 50 publications and an h-index of 16. Notable contributions include work on 3D deformation transfer, anomaly detection in battery thermal imaging, and hybrid attention models for pedestrian detection. His work bridges academia and industry through collaborations in healthcare, 3D scanning, and space tech. He has received two best paper awards and actively reviews for IEEE/Springer journals and top conferences like IROS, AAAI, ICCV, and CVPR. Teaching includes the Computer Vision module in the International Space Master’s program and AI summer camps for adolescents. Awards: Two best paper awards (3D deformation and skeletal feedback). Grants: Successfully led proposals for Luxembourg National Research Fund (FNR) projects under PPP and CORE programs. Key projects include the Zero-G Lab for space operations emulation and development of the STARR system for stroke rehabilitation. His labs focus on AI-driven solutions for energy systems, robotics, and biomedical applications.

• Artificial Intelligence
• Computer Vision
• Deep Learning
• +۵ مورد دیگر

Caesar Wu is a Researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the PCOG department. His work focuses on trustworthy AI, distributed learning systems, machine learning optimization, and strategic decision-making frameworks. He holds a Postdoctoral researcher position and contributes to advancing methodologies in data-centric AI, transformer models, and cybersecurity. Research interests include: - Trustworthy AI : Developing robust frameworks for explainability, fairness, and algorithmic transparency. - Distributed Systems : Optimizing communication and fault tolerance in distributed machine learning. - Time Series Forecasting : Hyperparameter tuning and transformer-based predictive models. - Financial Applications : Strategic decision-making in credit risk assessments using ML. Recent publications (2023-2025) emphasize: Trustworthiness metrics for stochastic gradient descent in distributed environments Unified hyperparameter optimization pipelines for transformer models Survey analysis of data-centric AI in time series forecasting Ethical considerations in AI-driven strategic decisions No scientific awards were explicitly mentioned in the provided texts. Current role involves no formal advisees, but contributes to interdisciplinary research teams within SnT. His work integrates machine learning with cloud computing principles, reflecting expertise in both theoretical and applied domains.

• Artificial Intelligence
• Machine Learning
• Distributed Systems
• +۳ مورد دیگر

Salijona DYRMISHI is a Postdoctoral Researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), specifically within the SerVal research group. Her work focuses on adversarial machine learning, security of AI systems, and generative models. She explores realistic adversarial examples, defense mechanisms against attacks, and synthetic data generation for tabular datasets. Her research interests include enhancing ML security through adversarial hardening, evaluating human perception of adversarial text attacks, and developing unified frameworks for adversarial attack-defense strategies. She has also contributed to mobile positioning via particle filters and automated machine learning using meta-learning approaches. Salijona's recent work emphasizes the intersection of adversarial robustness and practical applications, such as privacy-preserving synthetic data and realistic threat modeling. She has published extensively on topics like constrained feature spaces, generative adversarial networks (GANs) for tabular data, and the empirical validation of adversarial defenses.

• Adversarial Machine Learning
• Machine Learning Security
• Deep Generative Models
• +۳ مورد دیگر

Heiko SCHÜTT is an Associate Professor of Computational Science and Modelling at the University of Luxembourg's Faculty of Humanities, Education and Social Sciences. He is affiliated with the Department of Behavioural and Cognitive Sciences, where his research focuses on integrating computational models with cognitive and neural processes. His work spans machine learning, perceptual decision making, and visual perception, leveraging tools like deep neural networks and Bayesian analysis. Key research interests include analyzing neural representations through methods like representational similarity analysis, developing probabilistic models for feature segmentation, and investigating reward processing mechanisms in the brain. His publications highlight contributions to understanding how computational models can explain human cognition, particularly in areas like face recognition and color constancy. Recent work emphasizes the application of Bayesian experimental design to controversial stimuli and the optimization of loss functions in machine learning contexts. SCHÜTT collaborates across disciplines, bridging artificial intelligence with neuroscientific insights while maintaining a focus on experimental validation of computational hypotheses. He holds a strong academic profile with over 35 publications since 2016, addressing topics ranging from early visual processing to high-level cognitive functions. Despite his prolific output, no formal awards or grants are listed in the provided materials.

• Computational Neuroscience
• Machine Learning
• Perceptual Decision Making
• +۳ مورد دیگر

Dr. Michael Spratling is a Postdoctoral Researcher in the Department of Behavioural and Cognitive Sciences at the University of Luxembourg. His work bridges computational neuroscience and artificial intelligence, with specialization in neural networks, computer vision, and biologically-inspired learning systems. Spratling develops robust AI models through adversarial training techniques and neural architecture innovations. His core research investigates predictive coding mechanisms in machine learning, adversarial defenses for computer vision systems, and biologically-plausible neural network architectures. Recent projects focus on improving model resilience against data perturbations using adaptive smoothing techniques. Spratling maintains active GitHub repositories featuring benchmark tools for evaluating deep learning robustness. His work has applications in medical imaging and autonomous systems requiring reliable visual recognition. He received the Vebleo Scientist Award in 2020 for contributions to molecular biology-inspired AI research. He collaborates internationally on projects involving few-shot anomaly detection and multimodal neural networks. Current grants support research in length extrapolation for large language models and margin-based loss functions.

• Neural Networks
• Computer Vision
• Predictive Coding
• +۳ مورد دیگر

Dr. Mohammadamin ALANDIHALLAJ is a Postdoctoral Researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), within the SPASYS department. His work focuses on advanced space systems engineering, control systems, and aerospace technologies. Key research areas include satellite mission design (e.g., the POQUITO PocketQube mission), autonomous orbital correction using machine learning, and fractionated spacecraft architectures for enhanced connectivity. His research integrates cutting-edge techniques like Model Predictive Control (MPC), reinforcement learning (e.g., PPO algorithms), and LSTM networks to address challenges in spacecraft navigation, fault tolerance, and on-orbit operations. Notable contributions include developing control strategies for satellite docking, fuel-efficient orbit correction, and mitigating disturbances in on-orbit refueling. Dr. ALANDIHALLAJ also leads the Zero-G Lab, a multi-purpose facility for simulating space operations, enabling experimental validation of technologies such as floating platform stabilization and fuel sloshing mitigation. His work bridges theoretical advancements with practical applications, including wildfire monitoring via fractionated spacecraft and deep-space exploration systems for Europa missions. He has authored over 30 peer-reviewed publications, with recent emphasis on AI-driven space systems, fractionated architectures, and zero-gravity environment emulation. Current research trends highlight the convergence of machine learning, systems engineering, and experimental validation for next-generation space technologies. Awards and recognitions are not explicitly mentioned in the provided texts. His research is supported by collaborations within SnT and interdisciplinary projects at the University of Luxembourg.

• Space Systems Engineering
• Control Systems
• Aerospace Engineering
• +۷ مورد دیگر

Dr. Mohammed Oualid ATTAOUI is a Postdoctoral Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, affiliated with the SVV department. His work focuses on advancing artificial intelligence and machine learning methodologies, particularly in the context of deep neural network (DNN) safety analysis, clustering algorithms for data streams, and medical image processing applications. He holds a strong research interest in generative adversarial networks (GANs), black-box model analysis, and transfer learning techniques. His current position involves developing robust frameworks to ensure safety and reliability in AI systems, with notable contributions to the SAFE methodology for DNN retraining and validation. Research interests span across multiple domains including: Deep Learning and Safety-Critical AI Systems Data Stream Mining and Real-Time Clustering Medical Imaging Analysis (Histopathology, Computer Vision) Feature Selection and Optimization in Bioinformatics Subspace and Topological Clustering Approaches His recent research trends emphasize the intersection of adversarial machine learning and safety engineering, with publications demonstrating advancements in GAN-enhanced simulation-driven testing and clustering-based safety analysis for DNNs. His work bridges theoretical algorithm development with practical applications in healthcare imaging and autonomous systems. Dr. ATTAOUI's research outputs reflect a strong focus on methodological innovation with over 15 peer-reviewed articles since 2018. He actively contributes to open-source tools for data stream clustering and maintains an active presence in AI safety communities. His current projects explore adaptive clustering techniques for evolving data streams and robustness certification of medical image processing systems.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ 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.

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

Anne GRÜNEWALD is a Full Professor and Chief Scientist at the University of Luxembourg's Luxembourg Centre for Systems Biomedicine (LCSB), leading the Molecular and Functional Neurobiology department. Her research focuses on understanding Parkinson's disease mechanisms, particularly mitochondrial dysfunction, neuroinflammation, and environmental-genetic interactions. She directs the Grünewald Lab, pioneering work in iPSC-derived models and organoids to study disease progression. Her work integrates systems biology approaches with clinical data, emphasizing translational research. Key projects include the Luxembourg Parkinson’s study, examining disease heterogeneity and biomarkers, and investigating modifiers of LRRK2-associated penetrance through exposomics and metagenomics. She also explores machine learning applications for predicting motor complications and disease subtypes. Research interests span mitochondrial dynamics, neurodegeneration-inflammation feedback loops, and cross-talk between astrocytes/neurons. Her lab develops novel tools for high-throughput drug screening in Parkinson's models and investigates epigenetic influences on disease onset. Collaborations include international cohorts for deep phenotyping and genomic analysis. Notable contributions include identifying mitochondrial DNA heteroplasmy as a disease biomarker, demonstrating environmental modifiers' role in Parkinson's, and advancing 3D organoid models for patient-specific research. Ongoing projects focus on mitochondrial polygenic risk scores, lifestyle interactions, and neuroinflammatory pathways. Lab activities include generating patient-derived cell lines, analyzing single-cell transcriptomics, and developing biomarker assays. Future work aims to bridge basic research with clinical trials targeting mitochondrial dysfunction and neuroprotection.

• Parkinson's Disease
• Mitochondrial Dysfunction
• Neuroinflammation
• +۵ مورد دیگر

Yves Le Traon is Professor and Director of the Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg. His research focuses on software engineering, software testing, model-driven engineering, security, and adaptive systems. Recognized among the top 20 most impactful software engineering researchers, he received the Facebook Testing and Verification Research Award (2019) and IEEE Fellowship (2022). Dr. Le Traon leads research on AI-based systems and secure software development, with publications spanning software engineering, security, and artificial intelligence venues. He founded DataThings, a spin-off developing AI software for real-time decision-making.

• Software Engineering
• Software Testing
• Model Driven Engineering
• +۳ مورد دیگر