جستجوی استاد راهنمای دانشگاهی

Dr. Jose Luis SANCHEZ LOPEZ is a Research Scientist at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) of the University of Luxembourg, leading the Aerial Robotics Lab (AeRoLab) within the Automation & Robotics Research Group (ARG). He joined SnT in 2017 as a Postdoc Research Associate, promoted to Research Scientist in 2021. His research focuses on autonomous robotics, particularly aerial systems, emphasizing situational awareness, SLAM, and trajectory planning. Education: Ph.D. in Robotics (2017), M.Sc. in Automation & Robotics (2012), and Engineering degree in Industrial Engineering (2010), all from the Technical University of Madrid. Visiting Research: Arizona State University (2012), LAAS-CNRS (2014–2016). Research Interests: Multi-agent robotic systems, sensor fusion, localization/mapping, computer vision, machine learning, and trajectory control. He has authored over 56 peer-reviewed publications, with an h-index of 18. Projects: Leads projects like DEUS (PI), NEDA, ÄerdFly (PI), and RoboSAUR. Contributions span European, Luxembourg, and Spain-funded initiatives, focusing on autonomous systems, 5G integration, and construction-site robotics. Teaching: Lectured in MICS, BiCS, BING (Uni.lu) and GITI (UPM). Actively involved in academic service as a reviewer, editor, and competition participant (e.g., IMAV, IARC). Outreach: National Coordinator for Luxembourg’s Robotics European Week, promotes robotics education in schools and public events.

• Robotics
• Autonomous Systems
• Computer Vision
• +۵ مورد دیگر

Mike Papadakis is an Associate Professor at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), where he leads the SerVal research group. His research focuses on software engineering, software security, and artificial intelligence. He holds a PhD in Software Testing and Verification from Athens University of Economics and Business, with an MSc and BSc from the same institution. His research explores mutation testing, machine learning applications in software development, and test optimization. Recent publications demonstrate a strong emphasis on AI robustness, flaky test analysis, and automated debugging techniques. Notable achievements include the IEEE TCSE Rising Star Award (2020) and 12 additional research awards. He has published over 100 peer-reviewed articles and delivered more than 30 invited talks globally.

• Software Engineering
• Software Security
• Artificial Intelligence

Maxime CORDY is a Research Scientist at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) , University of Luxembourg, within the Security, Design and Validation group (SerVal) . He holds a PhD from the University of Namur (Belgium, 2014) and specializes in software engineering, applied artificial intelligence, and cybersecurity. His work focuses on adversarial machine learning, deep learning robustness, and model checking for critical systems. Research interests include adversarial attacks on tabular data , energy system optimization , code understanding models , and software quality assurance . Recent projects address challenges in secure AI deployment, automated test generation, and fault detection in large language models. Publications emphasize empirical studies on adversarial defenses, data augmentation for code models, and energy consumption forecasting. He contributes to tools like Daedalux (variability-aware model checking) and benchmarks like Tabularbench for adversarial robustness evaluation. Current affiliations include leadership within the SerVal group and collaborations on interdisciplinary projects such as MALETSQUE (Machine Learning Techniques for Software Quality Evaluation). His work bridges theoretical computer science with practical applications in energy systems, medical imaging, and space program design.

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Benoît ALCARAZ is a Researcher affiliated with the Department of Computer Science at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM). His work focuses on advancing artificial intelligence and machine learning, particularly in ethical AI frameworks, argumentation systems, and natural language processing. He is based at the Maison du Nombre research facility in Esch-sur-Alzette. Research Interests include developing algorithms for ethical decision-making in AI, optimizing machine learning models through evolutionary techniques, and creating user-centric NLP tools for real-time applications. His work bridges theoretical computer science with practical ethical considerations, as seen in his exploration of bias mitigation in visual learning and argumentation-based reinforcement learning agents. Notable contributions include frameworks like Ajar for ethical reinforcement learning and IRRMA for image-based meeting assistants. While no explicit awards or grants are listed, his publications indicate active involvement in interdisciplinary AI research with a focus on societal impact.

• Artificial Intelligence
• Machine Learning
• Argumentation Theory
• +۳ مورد دیگر

Jorge GONCALVES is a Full Professor at the Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, and a Principal Research Associate at the University of Cambridge's Department of Plant Sciences. He holds expertise in computational biology, systems biomedicine, and machine learning applications in healthcare. His roles include leading research on gene regulatory networks, epidemic modeling, and deep brain stimulation technologies. Education: Licenciatura from University of Porto (1993), M.S. and Ph.D. from MIT (1995/2000). Postdoctoral work at MIT and Caltech, followed by academic positions at the University of Cambridge (Lecturer, Reader, Principal Research Associate). Visiting Professorships at the University of Luxembourg and Caltech. Research Interests : Dynamical systems modeling, network inference, biomedical applications of AI, and systems biology. His work spans drug repositioning, early warning signals for disease transitions, and multiomics analysis in immunology. Publications : Focused on interdisciplinary approaches combining machine learning with biological systems. Key themes include computational methods for gene network analysis, epidemic monitoring systems, and AI-driven medical diagnostics. Awards : Not explicitly listed in provided texts. Advising and grants: Extensive collaboration with institutions globally, including leadership in EU-funded projects and industry partnerships. Active in mentoring researchers across computational biology and engineering disciplines. Labs/Teams: Leads LCSB teams focused on systems biomedicine and AI-driven health solutions. Collaborates with the University of Cambridge on plant sciences and robotics initiatives.

• Computational Biology
• Systems Biomedicine
• Machine Learning
• +۵ مورد دیگر

Luis A. Leiva is an Assistant Professor in the Department of Computer Science at the University of Luxembourg’s Faculty of Science, Technology and Medicine. He leads the Computational Interaction research group and serves as director of the Interdisciplinary Lab for Intelligent and Adaptive Systems (ILIAS). His academic background includes a PhD in Computer Science (with honors), an MAS in Pattern Recognition and Artificial Intelligence, an MSc in Electrical Engineering, and dual BSc degrees in Industrial Design and Industrial Engineering, all from the Universitat Politècnica de València (UPV). His research lies at the intersection of Human-Computer Interaction (HCI) and Machine Learning , with a focus on computational interaction . He investigates methods to enable, explain, and support user interaction through computational models and data-driven techniques. His key areas include implicit interaction —leveraging behavioral signals such as mouse movements and gaze—and synthetic data generation , particularly using kinematic models to simulate human-like gestures. His work has contributed to usability evaluation, user engagement prediction, adaptive interfaces, and stroke gesture modeling. His recent publications span top-tier journals and conferences such as ACM Transactions on Computer-Human Interaction (TOCHI) , IEEE Transactions , CHI , IUI , and SIGIR . The research trends reflect a strong emphasis on multimodal interaction, affective computing, brain-computer interfaces, and deep learning applications in user interface modeling and personalization. Best paper award at IUI'25 Marie Skłodowska-Curie Actions (MSCA) Seal of Excellence (2019) Extraordinary PhD thesis award (2014) Outstanding Reviewer Recognition at CHI, ICMI, MobileHCI, and UIST Quality Teaching Awards finalist at Aalto University (2020) Luis A. Leiva has advised numerous MSc and BSc students at the University of Luxembourg and Aalto University and has secured significant research funding as PI or co-PI for projects such as SYMBIOTIK (EIC), SCRIPTOR (FNR), BANANA (CHIST-ERA), and SAMUSE (Audacity). He is an Associate Editor for the International Journal of Human-Computer Studies and Machine Learning with Applications , and actively serves on the program committees of major HCI and AI conferences. He is also a co-founder and former CTO of Sciling, an SME specializing in machine learning solutions, and has held postdoctoral positions at Aalto University and the PRHLT Research Center (UPV). He leads the Computational Interaction research group and the ILIAS lab, both dedicated to advancing intelligent, adaptive, and user-centered computing systems through interdisciplinary research combining machine learning, cognitive science, and interactive technologies.

• Human-Computer Interaction
• Machine Learning
• Computational Interaction
• +۱۲ مورد دیگر

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

Djamila Aouada is an Assistant Professor and Senior Research Scientist at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), where she heads the Computer Vision, Imaging, and Machine Intelligence (CVI2) research group. She earned her State Engineering degree from École Nationale Polytechnique, Algeria, and PhD from North Carolina State University. Her research spans computer vision, signal processing, pattern recognition, and data modeling. Dr. Aouada leads several national and European projects including FNR FAVE, 3D-Act, and H2020 STARR, focusing on AI-driven solutions for industrial and security applications. She has received four IEEE Best Paper Awards for her contributions. Dr. Aouada has supervised 5 completed PhD theses and currently mentors 5 PhD candidates, while maintaining collaborations with Los Alamos National Laboratory and Mitsubishi Electric Research Labs. She serves as Senior IEEE Member and previously chaired IEEE Benelux Women in Engineering.

• Computer Vision
• Signal Processing
• Image Processing
• +۳ مورد دیگر

Fabrizio PASTORE is a Professor and Chief Scientist 2 in Software Engineering at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) , University of Luxembourg. He leads the Software Verification and Validation (V&V) Lab under Prof. Lionel Briand. His research focuses on advancing software testing methodologies, particularly in cyber-physical systems (CPS), deep neural networks (DNN) safety analysis, and program analysis. He holds a PhD from the University of Milano-Bicocca (2010) and has held roles including Assistant Professor at the same institution and Post-Doctoral Researcher at the University of Lugano (Switzerland). Key research interests include: Mutation testing and fuzzing techniques Automated tool development (e.g., MOTIF, SAFE, DaMAT) Safety analysis of DNNs using clustering and GAN-enhanced simulations Testing of edge frameworks and SDN configurations Model-driven testing and system evolution analysis His work bridges academic research with industrial applications, emphasizing practical tools for software reliability and security. He has contributed to over 100 publications since 2004, with a focus on testing methodologies for complex systems. Notable achievements include: Development of the AVA , RADAR , and MASS tools for regression and safety analysis Pioneering studies on metamorphic testing and explainable AI for safety-critical systems Leadership in the V&V Lab, fostering interdisciplinary collaboration in software engineering His research addresses challenges in CPS, autonomous systems, and AI safety, with a strong emphasis on tool-supported validation and verification methodologies.

• Software Engineering
• Software Testing
• Program Analysis
• +۵ 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

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

Anis KACEM is a Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, part of the Signal Processing and Satellite Communications (SIGCOM) research group. His work focuses on Computer Vision and Pattern Recognition, particularly in Human Behavior Understanding from visual data. He received his PhD in Computer Science from the University of Lille (France) in 2018. Research interests include advanced topics such as Earth Observation via multi-modal autoencoders, domain adaptation for image classification, vulnerability-aware deepfake detection, and CAD system reverse engineering. His contributions span neural network pruning, 3D shape analysis, and generative models for medical imaging. Publications highlight innovations in spatio-temporal learning for deepfake detection, hybrid attention mechanisms for pedestrian detection, and tool-augmented CAD task solvers. His work bridges theoretical advances with practical applications in autonomous systems and space technology. Notably, he has contributed to challenges like the SHARP 2023 Challenge on CAD history recovery and developed frameworks like Picasso for CAD sketch inference using self-supervised learning.

• Computer Vision
• Pattern Recognition
• Human Behavior Understanding
• +۵ 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