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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.

• Computational Mechanics
• Biomechanics
• Finite Element Methods
• +6 more

Sara Beatriz Gomes Fernandes is a Postdoctoral Researcher at the Luxembourg Centre for Systems Biomedicine (LCSB) within the University of Luxembourg , specializing in Translational Neuroscience . Her work focuses on advancing systems-level understanding of neurodegenerative diseases through computational and data-driven approaches. She is based at the Ketterthill research facility in Belvaux, Luxembourg, and her contact email is sara.gomes@uni.lu . Her research integrates biomedicine, systems biology, and computational modeling to address complex neurological challenges. Further details about her publications are available on the University of Luxembourg’s institutional repository, ORBilu .

• Neuroscience
• Biomedicine
• Systems Biology
• +3 more

Alexander SKUPIN is an Associate Professor and Chief Scientist in the Luxembourg Centre for Systems Biomedicine (LCSB) at the University of Luxembourg, specializing in Modelling of Biomedical Data. His research focuses on computational systems biology, neurodegenerative diseases, and integrative multi-omics analysis. Key research interests include modeling cellular energy metabolism, microglial dynamics in neurodegeneration, and calcium signaling dysfunction in Parkinson’s and Alzheimer’s diseases. His work combines experimental data with advanced computational methods to dissect disease mechanisms and predict therapeutic targets. Recent publications highlight innovations in: Sex-specific transcriptomic changes in tauopathy models Computational models of astrocytic morphology and mitochondrial efficiency Multi-omics integration for Parkinson’s disease intervention points Epidemiological modeling using Kalman-filtered SIRS networks No scientific awards explicitly mentioned. Advising and grants: No specific advisees or grant details listed, but his work implies leadership in collaborative projects involving organoid models and patient-derived xenografts. Labs/Teams: Part of LCSB’s Integrative Cell Signalling group, collaborating on systems biomedicine initiatives involving computational modeling and experimental validation.

• Systems Biomedicine
• Neurodegenerative Diseases
• Computational Modelling
• +6 more

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
• +5 more

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
• +12 more

Jean Botev is a Research Scientist and co-head of the Collaborative and Socio-Technical Systems (COaST) group at the University of Luxembourg's Faculty of Science, Technology and Medicine. He directs the VR/AR Lab, focusing on immersive technologies and human-centered design. His work bridges physical and digital realities through mediated reality approaches, emphasizing collaborative virtual environments and interaction techniques. Dr. Botev holds a PhD in Computer Science (2011, University of Luxembourg) and master's/bachelor's degrees from the University of Trier (Germany). He teaches courses in Virtual/Augmented Reality, Immersive Media, and programming disciplines across multiple university programs. His research spans 85+ peer-reviewed publications, with notable contributions to IEEE VR, ACM IVA, and TVCG. He leads €5M+ in grants, including the EU H2020 FET Open ChronoPilot project (€3M) exploring time perception modulation. Awards include the 2024 EuroXR Best Demonstration Award and multiple best paper recognitions. Botev actively engages in public outreach, organizing events like ACM SIGGRAPH Asia Demo Scene and promoting ethical design principles. His work integrates socio-technical systems, human factors, and interdisciplinary collaboration to advance immersive technology applications in education, cultural heritage, and healthcare.

• Mediated Reality
• Human-Computer Interaction
• Collaborative Socio-Technical Systems
• +3 more

Pedro Cardoso Leite is an Associate Professor in Cognitive Science and Assessment at the University of Luxembourg's Faculty of Humanities, Education and Social Sciences, within the Department of Behavioural and Cognitive Sciences. His work focuses on cognitive control, educational assessment methodologies, and the application of machine learning in education. Research interests include cognitive science applications in healthcare (e.g., Friedreich ataxia clinical trials), game-based learning for collaboration skills, and the ethical challenges of remote proctoring systems. He has also contributed to frameworks like CogPonder and CogEnv, advancing computational models of cognitive processes. Publications highlight interdisciplinary approaches, such as combining neuroscience with education technology and analyzing media impacts on child development. His work bridges theory and practice, addressing real-world challenges like educational equity and AI-driven assessment systems.

• Cognitive Science
• Educational Assessment
• Neuroscience
• +5 more

Diego Andrés BLANCO MORA is a Postdoctoral Researcher at the University of Luxembourg's Luxembourg Centre for Systems Biomedicine (LCSB), specializing in the AI Modelling and Prediction department. His work focuses on interdisciplinary research at the intersection of neuroscience, neurorehabilitation, and technology. Key areas include motor control, brain-computer interfaces (BCI), and applications of virtual reality (VR) in healthcare, particularly for stroke rehabilitation. He also explores urban neuroscience, investigating how brain activity relates to urban environments and public health. Diego's research combines advanced neuroimaging techniques like fMRI and EEG with innovative technologies such as tACS, tDCS, and VR-BCI systems. His recent studies highlight causal roles of beta-band oscillations in motor control, long-term VR-BCI training outcomes in stroke patients, and the predictive power of brain imaging for urban behavior. He has also contributed to developing urban stimulus datasets derived from social media and ethical considerations in pain detection for non-communicative patients. His publications reflect a strong emphasis on translational research, aiming to bridge fundamental neuroscience discoveries with clinical and urban applications. Diego collaborates across disciplines, engaging in projects that address neurorehabilitation challenges and the neuroscientific underpinnings of urban design.

• Neuroscience
• Neurorehabilitation
• Brain-Computer Interfaces (BCI)
• +7 more

Johanna HEIDER is a Postdoctoral Researcher at the Luxembourg Centre for Systems Biomedicine (LCSB) within the University of Luxembourg. Her work focuses on Translational Neuroscience , based in the Biotech II building in Belvaux, Luxembourg. She can be reached at johanna.heider@uni.lu. Specializes in systems biomedicine and computational models for neurodegenerative diseases Active in interdisciplinary research bridging biology and data science

• Neuroscience
• Biomedicine
• Systems Biology
• +3 more

Andreia COSTA is a Researcher at the University of Luxembourg, affiliated with the Faculty of Humanities, Education and Social Sciences and the Department of Behavioural and Cognitive Sciences. She holds a Master’s in Experimental Psychology from the University of Minho (Portugal) and a PhD from the University of Luxembourg, focusing on emotional reactivity in children with ASD under Prof. Georges Steffgen’s supervision. Her research emphasizes understanding emotional difficulties in ASD, including alexithymia and family dynamics, alongside interventions and multilingualism impacts on academic success. Current projects include the Luxembourg National Research Fund-backed 'Academic Success in Autism: The Impact of Emotions and Multilingualism' (ASA) and 'The Situation of Autism in Luxembourg' (SAUL). She collaborates with local institutions to improve care for ASD individuals and their families. Notable contributions include exploring robotics in therapy for children with ASD and analyzing parental psychological wellbeing across neurodiverse conditions. Andreia has received a significant grant (€397,000) for her work on multilingualism and ASD. Her publications span emotional regulation in ASD, robotic interventions, and parental stress factors. She actively contributes to autism-related policy and clinical guidelines in Luxembourg.

• Autism Spectrum Disorder
• Emotional Regulation
• Developmental Psychology
• +3 more

Josh Berryman is a Research Scientist (level 2) at the University of Luxembourg’s Faculty of Science, Technology and Medicine, leading the Biomolecular Modelling team within the Theoretical Chemical Physics group. He holds project management authority (ADR) and is a member of the Luxembourg Institute for Advanced Studies and the Complex Living Systems Initiative. His interdisciplinary research focuses on molecular biophysics, neuroscience, and neurodegenerative diseases, leveraging advanced computational methods including quantum mechanics and machine learning. Education: BSc in Computational Physics (University of Edinburgh, 2000), MSc in Artificial Intelligence (Edinburgh, 2001), and MSc/PhD in Physics and Life Sciences (University of Leeds, 2009). Professional roles include tenured positions at the University of Luxembourg since 2010 and teaching roles in computational physics and data science. He co-founded the Physics and Materials Science MSc program, developed its computational infrastructure, and currently chairs the BSc Physics sanctions committee. Research emphasizes biomolecular dynamics, particularly amyloid aggregation mechanisms and their links to neurodegenerative diseases. Collaborations include the AMBER software consortium (co-author of AMBER 14–24) and Luxembourg’s Google partnership in AI-driven molecular modeling (e.g., GEMS system). He coordinates the EU COVAMINF workgroup investigating SARS-CoV2’s potential role in neurodegeneration. Additional roles include non-executive roles in the Luxembourg Physics Society and advocacy for educational reforms through the National Parents Union. Teaching portfolio includes courses on classical electrodynamics, computational physics, and machine learning. He has supervised three PhD students independently and secured €569,500 in external research funding. His work bridges fundamental science and applied outcomes, with contributions to both academic software tools (AMBER) and cutting-edge AI applications in biomedicine.

• Molecular Biophysics
• Neuroscience
• Computational Physics
• +5 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.

• Computational Neuroscience
• Machine Learning
• Perceptual Decision Making
• +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.

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

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
• +3 more

Enrico Glaab is an Assistant Professor and Senior Research Scientist at the University of Luxembourg’s Luxembourg Centre for Systems Biomedicine (LCSB), leading the Biomedical Data Science Group. His roles include Deputy Course Director for the Master in Molecular and Computational Biomedicine and Principal Investigator. He holds academic qualifications from the University of Nottingham (PhD in Computer Science/Bioinformatics) and Saarland University (MSc and BSc in Computational Molecular Biology). Research focuses on applying machine learning and bioinformatics to neurodegenerative diseases like Alzheimer’s and Parkinson’s, emphasizing sex-dependent molecular mechanisms and integrative omics approaches. He has pioneered work on digital health technologies for personalized medicine, including gait sensor integration and biomarker discovery. Key achievements include winning international challenges (e.g., MEDIC, LongHack) and developing predictive models for disease progression and therapy responses. Teaching responsibilities span undergraduate, master’s, and PhD levels, covering systems biology, bioinformatics, and statistical analysis of omics data. Awards include the Bayer Lindau Fellowship, Geoffrey Beene Challenge win, and recognition for contributions to SARS-CoV-2 drug discovery. His interdisciplinary approach bridges computational methods with clinical applications, driving advancements in neurodegenerative disease understanding and treatment strategies.

• Machine Learning
• Bioinformatics
• Biomedical Data Science
• +3 more