جستجوی استاد راهنمای دانشگاهی

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

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

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. 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

Marco ALECCI is a Doctoral Researcher at the University of Luxembourg’s Interdisciplinary Centre for Security, Reliability and Trust (SnT), within the Trustworthy Software Engineering & mobile security research group (TruX). He is advised by Prof. Jacques Klein and works under the guidance of Prof. Tegawende Bissyande. AEU graduated with a Master’s degree from the University of Padua (Italy) in 2022. His research focuses on cybersecurity, particularly Android OS security and machine learning applications in threat detection. His academic background includes studies at the University of Padua, culminating in a Master’s degree. His work spans adversarial machine learning, Android malware analysis, and regulatory compliance (e.g., GDPR). He has contributed to projects like AndroZoo, a large-scale Android app dataset, and explored novel attack vectors such as virtualization-based exploits (e.g., Mascara). Marco’s publications reflect a trend toward practical cybersecurity challenges, including adversarial robustness, Android app categorization, and payment system vulnerabilities. His research bridges theoretical machine learning advancements with real-world security applications. He is affiliated with the TruX group, which specializes in trustworthy software engineering and mobile security, fostering collaborative research on emerging threats and mitigation strategies.

• Cybersecurity
• Android OS
• Machine Learning
• +۳ مورد دیگر

Gabriele Lenzini is an Associate Professor at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), where he leads the IRiSC research group. With a PhD in Computer Science from the University of Twente and dual MSc degrees from the University of Pisa, his research focuses on socio-technical security, electronic voting systems, and privacy engineering. He has extensive international experience through positions at CNR Italy, University of Twente, and Telematica Institute. His research explores security and privacy in human-centered systems through interdisciplinary approaches. Primary interests include: Design of trustworthy socio-technical systems Security protocols for electronic voting Location privacy assurance mechanisms Human factors in cybersecurity Publications demonstrate consistent focus on security and privacy innovations, particularly in social media analysis (misinformation detection, credibility systems) and cryptographic applications (voting protocols, authentication systems). Recent works show growing emphasis on AI/ML security implications and ethical HCI design patterns.

• Cybersecurity
• Privacy Engineering
• Socio-Technical Systems
• +۳ مورد دیگر

Sjouke Mauw is a full professor in "Security and Trust of Software Systems" at the University of Luxembourg , leading the Department of Computer Science and the SaToSS research group. His work bridges formal methods with real-world security and trust challenges. Current affiliation: University of Luxembourg (Faculty of Science, Technology and Communication) Previous roles: Associate Professor at Eindhoven University of Technology, Senior Researcher at CWI Research Focus Security protocols and their formal verification Attack trees and defense trees for structured threat analysis Privacy in voting systems and digital exchange Trust management frameworks and risk assessment RFID and e-voting security Cyber-physical socio-technical systems Recent Article Trends (2023-2025) include applications of deep learning to privacy risks, formal verification of distance-bounding protocols, and anomaly detection in resource-constrained environments like cubesats. These works span machine learning, cryptography, and aerospace engineering. Academic Contributions include over 50 refereed publications, supervision of 15+ PhD students, and leadership in European research projects (e.g., Meteor, Concur). He has developed formal methods for security protocols, attack-defense trees, and GNSS signal integrity frameworks.

• Information Security
• Formal Methods
• Privacy
• +۸ مورد دیگر