Find Academic Supervisors

• Software Engineering
• Software Security
• Artificial Intelligence

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
• Artificial Intelligence
• Machine Learning
• +7 more

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.

• Machine Learning operations (MLops)
• Data Science
• Software Engineering

Amal AKLI is a Researcher affiliated with the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), specifically within the Department of Computer Science. She is part of the Security Design and Validation research group (SERVAL), led by Prof. Yves Le Traon. Amal holds a Master’s degree in computer science from the National School of Computer Science in Algeria (2022). Her research focuses on Machine Learning operations (MLops), data science, and software engineering, with a particular emphasis on applying AutoML and few-shot learning techniques to improve software development and testing processes. Her recent publications explore parameter-efficient fine-tuning of pre-trained code models and predicting flaky tests using few-shot learning. While no scientific awards are explicitly mentioned, her work contributes to advancing automated testing and machine learning integration in software engineering. Amal's research aligns with the broader goals of the SERVAL team in enhancing software design and validation methodologies.

• Computational Neuroscience
• Machine Learning
• Perceptual Decision Making
• +3 more

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.

• Neural Networks
• Computer Vision
• Predictive Coding
• +3 more

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.

• Theoretical Condensed Matter Physics
• Quantum Mechanics
• Machine Learning in Materials Science
• +3 more

Alexandre Tkatchenko is a Full Professor of Theoretical Condensed Matter Physics at the University of Luxembourg, leading the Department of Physics and Materials Science and the Theoretical Chemical Physics group. His work integrates quantum mechanics, statistical mechanics, and machine learning to model complex materials, with over 150 peer-reviewed articles (h-index 58). He has held roles including Alexander von Humboldt Fellow at the Max Planck Institute (2008–2016) and has secured five ERC grants, including Advanced and Proof of Concept awards. Education: Bachelor in Computer Science and PhD in Physical Chemistry from Universidad Autónoma Metropolitana, Mexico City. Research focuses on quantum mechanical methods for molecular interactions, machine learning force fields, and computational tools like FHI-aims and libMBD. Key contributions include non-covalent interaction modeling, van der Waals forces, and AI-driven drug design. Awards include the 2020 Dirac Medal and APS Fellowship (2019). Grants include ERC Starting (2011), Consolidator (2017), and Advanced (2022) grants. His labs develop software for electronic structure calculations and explore materials for energy and drug discovery. Recent work addresses lithium diffusion in solid electrolytes and AI explainability in ADMET modeling.