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Martina Vandebroek is Full Professor of Statistics and Operations Research at KU Leuven, Faculty of Economics and Business, where she leads methodological work within the Operations Research and Statistics Research Group (ORSTAT). She also serves as senior academic staff on the Faculty Council and the Campus Councils for Leuven and Kortrijk, and is a member of the LStat General Assembly. Research Interests Discrete choice experiments (design & analysis) Optimal and sequential experimental design Multivariate statistics and modelling of preference heterogeneity Random regret minimisation and attribute non-attendance Applications in health economics, transport, marketing, and food science Her methodological innovations enable more efficient data collection and richer behavioural insights in stated-preference surveys, while her applied projects translate patient and consumer preferences into actionable evidence for policy makers and industry. Recent Publications Overview Between 2022 and 2024 Vandebroek (co-)authored 15 key articles. These contributions advance both the statistical machinery of choice modelling—such as mixed random regret models, design-efficient sample-size rules, and consideration-set heuristics—and substantive applications in oncology patient preferences, inflammatory bowel disease treatments, meat-substitute adoption, and food-quality valuation. The work repeatedly integrates sophisticated econometric techniques with real-world stakeholder data, reflecting an overarching commitment to methodological rigour and societal impact. Scientific Awards & Recognition No specific awards are listed in the provided material; however, her sustained publication record in top journals (Journal of Choice Modelling, Food Quality and Preference, Frontiers in Oncology, Stata Journal) attests to significant scholarly recognition. Advising & Grant Activities Promotor (PI) of FWO project “Discrete choice models including screening rules: modeling and design” (2017-2021) Promotor of KU Leuven project “Efficient Online Choice Experiments” (2017-2020) Co-promotor of IWT/FWO project “Empirical and methodological challenges in choice experiments” (2017-2023) Co-promotor of VLAIO project “Development, Validation, and Valorization of a Patient Preference Platform” (2023-2026) Teaching & Service Vandebroek teaches master-level courses “Applications of Statistics” (Dutch and English iterations) and contributes to the Leuven Statistics Research Centre (LStat) educational programme.

• Discrete choice experiments
• Optimal experimental design
• Multivariate statistics
• +7 more

Ingrid Moerman is a part-time Professor at Ghent University and a staff member at the Internet Technology and Data Science Lab (IDLab), a core research group of imec embedded within Ghent University and the University of Antwerp. She coordinates mobile and wireless networking research and leads a team of over 30 researchers at Ghent University, with extensive involvement in European and national funding initiatives. She received her Electrical Engineering degree (1987) and Ph.D. (1992) from Ghent University. Her research spans collaborative networks, cognitive radio, software-defined radio, IoT, LPWAN, and high-density wireless access, emphasizing experimentally-supported development of next-generation wireless systems with practical implementations in spectrum management and real-time control. Recent publications (2024-2025) reveal a strong pivot toward AI-integrated wireless networking, featuring OFDMA scheduling innovations, Wi-Fi 6/7 interference mitigation, and time-sensitive networking for industrial applications. Key trends include 5G/6G convergence, vehicular communication enhancements, and digital twin frameworks for network observability, reflecting her focus on mission-critical industrial use cases. Her accolades include: 9 Best Paper Awards 2 FWO Prizes (Research Foundation - Flanders) IMEC Prize of Excellence 2001 MSc Thesis Award (as promoter) Best Demo/Exhibit Award at ICT 2013 DARPA Spectrum Collaboration Challenge Prize ($750,000) She has coordinated major EU projects (FP7/H2020: CREW, WiSHFUL, eWINE, ORCA) with industry partners, securing substantial funding for experimental wireless research. Her grant portfolio emphasizes collaborative innovation in spectrum sharing and neutral-host architectures for multi-operator environments. At IDLab, she directs advanced wireless testbeds supporting real-world validation of technologies like openwifi and White Rabbit, with active experimentation in time-sensitive networking and spectrum collaboration for industrial IoT deployments.

• Wireless Networking
• Cognitive Radio Networks
• Internet of Things
• +4 more

Wout Weijtjens is a Research Fellow at Vrije Universiteit Brussel, affiliated with the Acoustics & Vibration Research Group in Applied Mechanics. His research focuses on structural health monitoring (SHM) of offshore wind turbines, fatigue analysis, and vibration-based damage detection using advanced signal processing and machine learning techniques. Current projects include FIRMEST (fatigue assessment of offshore wind turbine substructures) and FOOS (Forced Oscillations in turbines). His research interests span: Operational modal analysis for offshore structures Machine learning applications in SHM Fatigue life prediction under environmental variability Sensor networks for infrastructure monitoring Wind turbine dynamics under harsh conditions Recent publications demonstrate a consistent focus on developing predictive maintenance frameworks through multivariate sensor data analysis, uncertainty quantification in SHM systems, and validation of computational models against full-scale field measurements. Article trends emphasize machine learning integration with physical models for improved fatigue life assessment. Awards and recognitions include: Best Paper Award (2nd place, 2022) Poster Award (2017) Solvay Award (2015) As principal investigator on multiple grants including VLADBC7 and VLADBC9 projects, he supervises PhD candidates in vibration-based SHM and leads experimental validation at OWI-Lab's Large Climate Chamber. His team develops IoT monitoring solutions for civil infrastructure through the SMART TOWERS initiative.

• Structural Health Monitoring
• Offshore Wind Turbines
• Fatigue Analysis
• +2 more

Pierre-Antoine Absil is a Full Professor at the Louvain Polytechnic School (EPL) , part of the Université catholique de Louvain (UCLouvain) , affiliated with the Mathematical Engineering Center (INMA) and the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM) . His research spans Riemannian optimization , matrix manifolds , and low-rank approximation with applications in astronomical imaging , signal processing , and environmental data imputation . His work emphasizes Riemannian optimization algorithms matrix and tensor completion geometric data analysis applications in astrophysics and bioinformatics Recent publications focus on Stiefel and Grassmann manifolds , variable projection methods , and exoplanet detection via alternating minimization . While no explicit scientific awards are listed, his collaborations with institutions like IEEE and Springer highlight his impact in computational mathematics and engineering. He has supervised researchers such as Simon Vary , Guillaume Olikier , and Valentin Christiaens through projects in direct imaging , economic dispatch , and graph-based data processing . His laboratory, INMA , drives methodological advances in manifold-valued data analysis and nonlinear optimization .

• Riemannian Optimization
• Matrix Manifolds
• Low-Rank Matrix Completion
• +4 more

Tomas Norton is a Senior Lecturer at the Faculty of Bioscience Engineering, Department of Biosystems at KU Leuven. He is actively affiliated with multiple research units including the Division of Bio-Environmental Control, the KU Leuven Brain Institute (LBI), and the KU Leuven Institute for Integration of Micro- and Nano-scale Technologies (LIMNI). His work is centered at the Animal and Human (A2H) research unit located at Castle Park Arenberg. Dr. Norton's research focuses on precision livestock farming with particular expertise in computer vision, machine learning, and sensor technologies applied to animal monitoring. His current projects span sustainable agriculture, animal vocalization analysis, and bio-response monitoring systems across multiple species including poultry, pigs, and buffalo. He serves as promotor on numerous research initiatives funded through 2028-2029 that address critical challenges in sustainable food production while improving animal welfare. His recent publications demonstrate a strong trend toward AI-driven solutions for animal monitoring, with particular emphasis on behavior recognition, health assessment through sound analysis, and robotic management systems. These works appear primarily in agricultural technology journals with a focus on practical implementation of advanced computational methods in farming contexts. As an academic leader, Dr. Norton serves on the Faculty Council of Bioscience Engineering, the Doctoral Committee for Bioscience Engineering, and the Departmental Council for Biosystems. He teaches courses including Modelling of Biosystems, Bioresponse Measurements and Process Control, and Sustainable Precision Livestock Farming, integrating his research expertise directly into his educational practice. His laboratory work within the A2H unit represents a multidisciplinary approach that bridges engineering, computer science, and animal science to develop practical technological solutions for modern agricultural challenges, with particular emphasis on sustainability and welfare improvements in livestock production systems.

• Precision Livestock Farming
• Animal Monitoring Systems
• Machine Learning Applications in Agriculture
• +5 more

Tamas Vadasz is an Associate Professor of Finance at KU Leuven , affiliated with the Finance Research Group and the Faculty of Economics and Business (FEB). His research focuses on financial intermediation, banking theory, regulation, financial stability, FinTech, and green finance. Education: PhD in Finance, Warwick Business School (2019) Research Interests include: Banking regulation and competition Financial stability and risk-taking FinTech innovations and their impact on traditional banking Green finance and environmental sustainability in corporate finance His recent work explores how technological advancements and regulatory frameworks influence banking practices, including information sharing in branch networks, prudential regulation effects, and the intersection of green finance with corporate strategies. He has published in top journals like the Review of Finance and Journal of Financial Intermediation . Scientific Awards: 2021 SANFI Award for Best Paper on Banking at the XXVIII Finance Forum (AEF IN) Projects span financial stability, fintech, and green banking. He also teaches courses in Investments and FinTech , and contributes to institutional governance as a member of the Campus Council FEB and the Faculty of Economics and Business Council.

• Financial Intermediation
• Banking Theory
• Financial Regulation
• +5 more

Moharram Challenger is a tenure-track Assistant Professor in the Department of Computer Science at the University of Antwerp's Faculty of Sciences. Previously, he served as an assistant professor at Ege University (2017-2018) and as a post-doctoral researcher at the University of Antwerp (2019-2020) working on Flanders Make projects PACo and DTDesign. His academic journey includes R&D leadership roles at UNIT IT Ltd. (2012-2016), post-doctoral research at Wageningen University (2016-2017), and tenure-track faculty positions at IAU-Shabestar University (2005-2009). His research spans Cyber-physical Systems , Multi-agent Systems , and Domain-specific Modeling Languages , with recent publications focusing on quantum machine learning, digital twinning, and IoT optimization. Key projects include ITEA ModelWriter, ITEA Assume, and Flanders Make initiatives. His work demonstrates strong integration of model-driven engineering with emerging technologies like quantum computing and reinforcement learning. Challenger actively contributes to the academic community as a member of IEEE and ACM . His publication record shows consistent output across top venues, with 2025 featuring significant work in quantum-enhanced learning and CPS security. Current research emphasizes practical applications in drone energy modeling, medical diagnostics, and industrial IoT systems. His advising activities focus on cyber-physical systems and agent-based modeling, supported by grants from TUBITAK and Flanders Innovation & Entrepreneurship. Key collaborations include European ITEA projects and partnerships with industrial entities through UNIT IT Ltd. Challenger maintains active development through GitHub repositories related to code refactoring, model-driven engineering, and legacy system modernization, reflecting his commitment to practical software engineering solutions.

• Cyber-physical Systems
• Multi-agent Systems
• Domain-specific Modeling Languages
• +5 more

Steve Muylle is a Full Professor of Digital Strategy and Business Marketing at Vlerick Business School and Ghent University, and serves as Associate Dean Digital Learning. His expertise spans digital transformation, B2B marketing, and leveraging technologies like AI, blockchain, and IoT. He holds a Doctor in Business Engineering from Solvay Management School and has held visiting roles at SMU’s Cox School of Business and Vanderbilt University’s Owen Graduate School. Education: PhD in Business Engineering (Solvay Management School) Visiting Doctoral Researcher (Owen Graduate School of Management, Vanderbilt University) Research Interests: Digital strategy, B2B customer experience, AI-driven marketing, and digital innovation frameworks. His work emphasizes actionable insights for executives in sectors like energy, healthcare, and finance. Recent trends in his publications focus on agentic marketing, VR in education, and competitive digital innovation. Impact: Designs executive programs like the Digital Strategy Open Programme and the Executive Master Class in B2B Marketing/Sales. Recognized for contributions to digital learning and open banking strategies. Board member at multiple firms.

• Digital Transformation & AI
• Digital Strategy
• B2B Marketing
• +4 more

Wim Dehaene is a Full Professor at KU Leuven's Department of Electrical Engineering (ESAT), Faculty of Engineering Science. His research focuses on advanced digital circuit design, in-memory computing, and biomedical applications. He leads projects like Variability Resilient Design of In-Memory Compute Architectures and DECIBUS: Communication in the Body by Ultrasound . Member of MICAS (Micro Electronics and Sensors) division Head of Electronic Circuits and Systems (ECS) group Active in interdisciplinary education and curriculum development His work bridges flexible electronics, low-voltage design, and AI accelerators, with recent publications on neural network hardware and biomedical wearables. Collaborations span institutions like IEEE and projects involving RISC-V controllers and IGZO TFT-based SRAM. Teaching roles include courses on digital integrated circuits, biomedical measurements, and interdisciplinary design methodologies. He contributes to research training in biomedical technology and participates in IEEE conferences as organizer and speaker.

• Integrated Circuit Design
• Digital Electronics
• Biomedical Engineering
• +3 more

Dr. Vincent Ginis is a prominent academic at Vrije Universiteit Brussel (VUB), associated with the Applied Physics Department and the Data Analytics Lab. His roles include doctoral scholarship supervision and research leadership in interdisciplinary projects. He holds a strong background in applied physics, metamaterials, and AI-driven solutions for societal challenges. Current affiliations include: Principal Investigator in 19 funded projects (2010–2029), focusing on AI ethics, sustainable transitions, and historical data analysis Supervisor of 60+ student theses across master's and doctoral levels Recipient of 15+ prestigious awards including the Agathon De Potter Award and FWO/Barco Prize Research interests span: Applied Physics: Metamaterials, optics, and photonics AI Applications: Ethics, bias mitigation, and historical data digitization Social Sciences: Wealth inequality, intergenerational mobility, and policy impact analysis Recent work highlights include groundbreaking studies on: Large Language Models' performance in OCR tasks Bias patterns in AI citation practices Ethical frameworks for human-centered AI systems Notable collaborations span institutions in Europe and beyond, with 1295 citations across 120+ publications. His work is amplified through platforms like Strava data for urban planning and historical datasets from 19th-century archives.

• Applied Physics
• Artificial Intelligence
• Data Analytics
• +4 more

Jesse Davis is a Professor at the Department of Computer Science , KU Leuven , actively contributing to the Machine Learning group and the Sports Analytics Lab . He is part of the Faculty of Engineering Science and the Leuven.AI Institute . Ph.D. in Computer Sciences from University of Wisconsin-Madison (2007) M.S. in Computer Sciences from University of Wisconsin-Madison (2005) B.A. in Computer Science from Williams College (2002) His research focuses on machine learning, data mining, big data analytics, and sports analytics, with significant work in: Transfer learning and Markov logic networks Anomaly detection and semi-supervised learning Medical NLP and biomechanical data analysis Soccer performance metrics and tactical analysis His recent work explores spatio-temporal data analysis in sports and explainable AI for medical applications, with collaborations spanning finance, healthcare, and semiconductor manufacturing. Notable scientific awards include: Best Paper Award (Applied Data Science Track) at KDD 2019 Best Technical Paper Award at Intelligence Analysis Workshop He advises numerous PhD and Master's students in areas like: Football analytics Tree ensemble compression Medical question-answering systems Biomechanical load prediction His lab develops tools such as: GSSL for Markov network structure learning TODTLER for transfer learning Alchemy system for Markov logic networks

• Machine Learning
• Data Mining
• Big Data
• +4 more

Tom Francart is a Professor at the Department of Neuroscience, Faculty of Medicine, KU Leuven. He leads research in auditory attention, EEG decoding, and speech processing, with affiliations to iSi Health, LBI (KU Leuven Brain Institute), and Leuven.AI. His research focuses on: Neural decoding of auditory attention using EEG Objective measures for aphasia and hearing impairment Brain-computer interfaces for hearing aids Audiovisual integration in speech perception Machine learning applications in auditory neuroscience Recent publications analyze neural tracking of speech in aphasia, transformer-based EEG decoding, and audiovisual context integration. He supervises projects on cochlear implants, attention decoding algorithms, and post-stroke recovery.

• Auditory Attention
• EEG Decoding
• Cochlear Implants
• +5 more

Gerda Claeskens is a full professor of Statistics at the Faculty of Economics and Business (FEB) at KU Leuven, Belgium. She holds positions in the Operations Research and Statistics Research Group (ORSTAT) and is affiliated with the Leuven Statistics Research Center. Her academic journey includes a Licentiate in Mathematics (Summa Cum Laude) from the University of Antwerp and a Ph.D. in Statistics from Limburgs Universitair Centrum (now Hasselt University). Research Interests: Her work focuses on model selection, post-selection inference, nonparametric methods, and high-dimensional statistics. She has contributed extensively to methodologies like focused information criteria, penalized splines, and quantile regression. Awards and Honors: Notable accolades include Fellow of the American Statistical Association (2019), Goodeve Medal (2017), and Medallion Lecturer (2016). She has also held editorial roles in top journals such as Biometrika and Journal of the American Statistical Association . Teaching and Mentoring: She has advised over 20 PhD students and mentored postdoctoral researchers. Her teaching spans advanced statistical methods, probability theory, and business statistics. She has delivered invited lectures globally, including at the European Meeting of Statisticians and the International Society for NonParametric Statistics. Key Contributions: Her book Model Selection and Model Averaging (2008) is a seminal work in statistical methodology. Her research addresses challenges in high-dimensional data, survival analysis, and model averaging, with applications in finance, insurance, and biostatistics.

• Model Selection
• Model Averaging
• Post-Selection Inference
• +4 more

Timothy Verstraeten is a postdoctoral researcher at the Vrije Universiteit Brussel , affiliated with the Engineering Technology Acoustics & Vibrations Research Group . His work bridges Wind Energy and Machine Learning , focusing on optimization, condition monitoring, and control systems for wind farms. Role: Researcher in acoustics, vibrations, and intelligent monitoring Key Projects: IOF Gear (2018–2023), Robust Fleet-Dedicated Reinforcement Learning (2017–2020) Research Interests include: Wind turbine operations (power prediction, balancing schemes) Reinforcement learning for hybrid energy systems and fleet control Condition monitoring using Gaussian processes and anomaly detection Deep learning for predictive maintenance and wake loss modeling Scientific Contributions show a focus on Wind turbines (100% Scopus), Farms (58%), and Reinforcement learning (37%), with recent publications on uncertainty quantification and hybrid energy systems. Award: ICTAI Best Student Paper (2018) He has supervised research proposals and presented at conferences like ACM E-Energy and Wind Energy Science, emphasizing control systems , power optimization , and structural health monitoring .

• Wind turbines
• Condition monitoring
• Reinforcement learning
• +4 more

Ariel Basso Madjoukeng is a researcher at the Research Center on Information Systems Engineering within the Namur Digital Institute . His work focuses on advancing machine learning techniques for computational challenges in data analysis and sign language recognition. Research Interests Development of contrastive learning frameworks for sign language recognition Optimization of clustering algorithms (e.g., k-means) with robustness to anomalies Data augmentation strategies for improving model generalization Efficient convergence methods in unsupervised learning Recent Contributions include benchmarking augmentation techniques in contrastive learning and enhancing clustering stability. He actively presents at international conferences like ESANN and IJCNN , with outputs spanning 2024-2025.

• Algorithms
• Data Augmentation
• Contrastive Learning
• +2 more