David Martens is a Professor of Data Science at the University of Antwerp , where he directs the Applied Data Mining Research Group within the Faculty of Business and Economics . He also serves as Chair of the Department of Engineering Management and Director of the Antwerp Center on Responsible AI . His academic work spans data mining , interpretable machine learning , and the societal impact of AI . PhD in Applied Economic Sciences (KU Leuven, 2008) Director, Antwerp Center on Responsible AI Chair, Department of Engineering Management Martens' research focuses on responsible AI and data ethics , with applications in finance, public policy, and behavioral analysis. His recent publications emphasize counterfactual explanations , LLM interpretability , and privacy implications in AI systems. His articles reveal trends in Explainable AI (XAI) , including narrative-driven explanations , graph neural networks , and ethical challenges like monetization risks and algorithmic bias. Keywords span Computer Science , Artificial Intelligence , and Behavioral Data . Martens is a leading voice in data science ethics , authoring the book Data Science Ethics: Concepts, Techniques, and Cautionary Tales (Oxford University Press, 2022). He combines academic rigor with industry experience, having consulted for banks, telecom firms, and startups in fraud detection and digital advertising .
Matthew B. Blaschko is a Professor in the Department of Electrical Engineering at KU Leuven, Belgium. He serves as director of the KU Leuven ELLIS unit and is a fellow in the ELLIS Health program. He is a Core PI in the Flanders AI Research Program, working as a workpackage lead for Decision Support Systems and Medical Imaging. Blaschko is also a member of the KU Leuven Institute for Artificial Intelligence and one of the leaders of the working group on Machine Learning and Data Science. Professor Blaschko received his B.S. from Columbia University, M.S. from the University of Massachusetts Amherst, and Dr. rer. nat. from Technische Universität Berlin (awarded for work at Max Planck Institutes Tübingen). He was a Newton International Fellow at the University of Oxford and received his Habilitation (HDR) from École Normale Supérieure de Cachan. Prior to joining KU Leuven, he was a Permanent Research Scientist in the INRIA Saclay Research Center and a Faculty Member at Ecole Centrale Paris. His research focuses on machine learning techniques applied to visual data, with particular emphasis on calibration in deep learning, medical image analysis, and federated learning. Blaschko's work bridges theoretical foundations with practical applications, as evidenced by technology developed in his research being incorporated into MONA, software for ophthalmic image analysis. His research group has made significant contributions to the fields of model calibration, uncertainty estimation, and medical imaging analysis, with recent publications showing strong trends toward improving reliability of AI systems in medical contexts and advancing theoretical understanding of calibration metrics. Professor Blaschko has been recognized with several awards including the Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award, Best Paper Award at CVPR 2008, Main Award at DAGM 2008, and Best Student Paper Award at ECCV 2008. Professor Blaschko has supervised numerous PhD and Master's students, with current and former students including Deniz Soysal, Claire Marchal, Dongli Xu, Sebastian Gruber, Jiameng Li, Marco Mezzina, and many others working on diverse topics from Alzheimer's disease analysis to surgical phase recognition. His research has been supported by various funding sources including the Flanders AI Research Program. He has co-organized several influential workshops including the "Another Brick in the AI Wall: Building Practical Solutions from Theoretical Foundations" at CVPR 2025, Commands 4 Autonomous Vehicles workshop at ECCV 2020, and the Learning from Limited Labeled Data workshop series at NIPS 2017 and ICLR 2019. His laboratory focuses on machine learning for medical image analysis, with applications in ophthalmology, neurology, and surgical robotics. The group maintains active collaborations with medical institutions and participates in international challenges such as the KNee OsteoArthritis Prediction (KNOAP2020) challenge.
Geert Deconinck is a full professor at KU Leuven , leading the Electrical Energy Systems and Applications (ELECTA) research group within the Department of Electrical Engineering (ESAT). He also serves as scientific leader of the EnergyVille research center's algorithms domain, focusing on smart electrical networks and thermal systems. M.Sc. and Ph.D. from KU Leuven Head of ELECTA since 2012 (10 professors, 8 postdocs, 70+ PhDs) Over 8 million EUR research budget in last 5 years 44 completed PhDs and 10 current advisees IEEE Transactions editorial board member His research spans smart grid architectures , distributed control , and cyber-physical security , with recent focus on EV-grid integration , renewable energy democratization , and multi-carrier energy systems . Current projects include: Smart Charging - E-Mobility meets Renewable Energy Early Detection and Defense Systems for Smart Grids Open-source P2P energy sharing platforms Microgrid control strategies for PV-battery systems Awarded IET Fellow and IEEE Senior Member status, his work combines machine learning with power systems engineering through both theoretical modeling and experimental validation . He has contributed over 575 publications with 9800+ Google Scholar citations.
Patrick Willems is a full Professor at KU Leuven's Faculty of Engineering Sciences, Department of Civil Engineering. He serves as the head of the Hydraulics Subdivision within the Hydraulics and Geotechnics unit. Professor Willems holds multiple significant roles including chairman of the ADS Bureau, member of the Faculty Council of Engineering Sciences, and participant in the Leuven One Health Institute and KU Leuven Institute for Urban Studies (LUSI). His work addresses critical global water management challenges through advanced hydrological modeling and climate change adaptation strategies. Professor Willems' research spans several critical areas in water resources engineering. His primary expertise includes urban hydrology and river engineering, statistical hydrology with focus on flood prediction and risk analysis, integrated river basin management, precipitation analysis, and climate change impacts on hydrological extremes. He employs both traditional hydrological modeling approaches and innovative machine learning techniques to develop practical solutions for flood warning systems, urban water management, and climate adaptation planning. His research integrates statistical methods, numerical modeling, and data science to address complex water management problems across multiple geographical contexts. Professor Willems leads multiple major international research projects examining hydrological extremes in transboundary river basins, natural climate adaptation measures, hydrological modeling of peatland areas, and deep learning-based prediction of hydrological extremes. His work spans various geographical contexts including Belgium, Vietnam, Bolivia, Tanzania, and the Congo Basin, demonstrating both local relevance and global applicability of his research. Within KU Leuven, Professor Willems teaches diverse courses including Environmental Problems and Techniques, Statistics and Data Science, Stochastic Hydrology, Urban and River Hydrology and Hydraulics, River Modeling, and Probability and Statistics. He also leads the Hydraulic Engineering Project course and an Artificial Intelligence Project course, reflecting his commitment to integrating traditional engineering knowledge with modern computational approaches. As head of the Hydraulics Subdivision, he oversees research activities focused on developing advanced water engineering tools and methodologies that bridge theoretical advancements with practical applications for water management authorities.
Frédéric Vrins is a Professor at the Louvain School of Management (LSM) , UCLouvain , affiliated with the Louvain Institute of Data Analysis and Modeling in economics and statistics (LIDAM) and Louvain Finance (LFIN). His work bridges theoretical and applied finance, with a focus on risk modeling, portfolio optimization, and machine learning applications. His research interests include: Quantitative Finance: Derivatives pricing, stochastic processes, and model calibration. Risk Management: Credit concentration risk, recovery rates, and wrong-way risk in financial markets. Portfolio Optimization: Mean-variance strategies, diversification metrics, and robustness under parameter uncertainty. Machine Learning in Finance: Applications to recovery rate prediction and option pricing frameworks. Recent publications highlight trends in: Credit risk modeling for Collateralized Loan Obligations (CLOs) and consumer credit. Machine learning integration in derivatives pricing and portfolio construction. Stochastic methods for Brownian bridges, CDS spreads, and recovery rates. Empirical studies on Loan-to-Value policies and business cycle impacts. Affiliations and locations: Louvain School of Management (LSM) - Building B, Chaussée de Binche 151, 7000 Mons Louvain Finance (LFIN) - Traverse d'Esope 1, 1348 Louvain-la-Neuve Louvain School of Management (LSM) - BATA Building, Chaussée de Binche 151, 7000 Mons
Maarten De Vos is a Professor at the Department of Electrical Engineering (ESAT) , KU Leuven , with dual appointments in the Faculty of Medicine and Faculty of Engineering Science . He leads interdisciplinary research at the intersection of artificial intelligence and biomedical signal processing.
Yves Rosseel is a Professor at Ghent University (UGent) specializing in Structural Equation Modeling (SEM) , Psychometrics , and Statistical Methodology . With over 15 recent publications (2024-2025), he focuses on small-sample SEM solutions, factor score regression, measurement error, and Bayesian extensions. His work bridges Statistics with applications in Psychology , Education , and Neuroimaging . Research Trends Developed Mixture Multigroup SEM for cross-group comparisons Proposed Information-Theoretic Hypergraphs in psychometrics Advanced Two-Stage Estimation for round-robin data Created blavaan R package for Bayesian SEM Investigated Measurement Error in hypothesis testing Scientific Contributions Published 84 Social Sciences papers, 37 Statistics works, and 11 Neuroimaging studies Promoted 8 PhDs including Sara Dhaene and Julie De Jonckere Co-authored 12+ works with Marijke Welvaert and 10+ with Stijn Vanheule
Nathan Lassance is a Lecturer at the Louvain School of Management (LSM), Université catholique de Louvain (UCLouvain), and a member of the Louvain Finance (LFIN) research division. His work bridges financial theory, statistical modeling, and data-driven portfolio optimization, with a focus on addressing parameter uncertainty and improving risk-return tradeoffs in asset allocation. Research Interests: Portfolio management, covariance matrix estimation, financial econometrics, risk analysis, quantitative finance, and non-Gaussian return distributions. Publications: His recent work explores shrinkage methods for high-dimensional portfolio selection, sentiment-aligned covariance matrices, and the economic value of statistical metrics like mean squared error. He has also contributed to understanding the limitations of factor-based mispricing models and the statistical properties of mean-variance portfolios. Labs/Teams: Affiliated with the Louvain Institute of Data Analysis and Modeling (LIDAM) and the Louvain Finance (LFIN) group.
Catherine Legrand serves as Professor of Biostatistics at UCLouvain (Louvain-la-Neuve, Belgium), affiliated with the Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) and the Louvain School of Statistics, Biostatistics and Actuarial Sciences (LSBA) within the Faculty of Science. She chaired ISBA from 2019-2022 and currently presides over the Louvain Institute for Data Analysis and Modeling (LIDAM). Her academic foundation includes: Master Degree in Mathematics from Université Libre de Bruxelles (1998) PhD in Statistics from Hasselt University (2005) supervised by Prof. Paul Janssen and Prof. Luc Duchateau, specializing in survival analysis and frailty models Her research program centers on Survival Data Analysis with emphasis on frailty models, cure models, and Joint Models for Longitudinal and Survival Data. She develops methodologies for clinical trial design and analysis with direct applications in oncology data, bridging theoretical statistics and clinical practice through collaborations with medical researchers. Analysis of her recent publications (2023-2025) reveals three dominant trends: 1) Advanced survival modeling techniques (semi-Markov, cure rate models) for disease progression; 2) Validation frameworks for surrogate endpoints in clinical trials using joint modeling; 3) Emerging applications in health insurance analytics and public health systems. Her work increasingly addresses real-world healthcare challenges while maintaining methodological rigor. No scientific awards were documented in the available sources. Information regarding student supervision and research grant funding was not provided in the current materials. She leads research within LIDAM and ISBA, building on her prior role as primary statistician for the EORTC Lung Cancer Group where she contributed to multiple Phase II/III trials in lung cancer and mesothelioma. Her current work integrates biostatistical methodology development with practical applications across oncology, public health, and actuarial science domains.
Marian Verhelst is a Professor at KU Leuven's Faculty of Engineering Science, renowned for her research in hardware-efficient computing and dedication to STEM education. Her work spans hardware acceleration for machine learning, edge AI, and in-memory computing, with a focus on energy optimization and algorithm-hardware co-design. Her research interests include: Designing flexible hardware for ultra-low-power edge AI systems Optimizing sparsity-aware architectures for deep learning workloads Advancing chiplet-based and 3D memory technologies Co-designing algorithms and hardware for probabilistic AI Pioneering STEM outreach through KU Leuven InnovationLab Recent publications (2023–2025) demonstrate strong trends in: Hardware-software co-optimization for edge ML systems Efficient data movement in heterogeneous accelerators Low-precision and sparse computation techniques RISC-V based customizable SoCs Sustainable AI accelerator design Awards & Honors: Young Academy of Europe Award (2021) for science communication and STEM advocacy She leads significant educational initiatives, including the KU Leuven InnovationLab which has engaged 150 schools and 13,000 students since 2014. The program develops hands-on STEM projects (e.g., AI-powered wheelchairs, sustainable energy systems) and provides teacher training to inspire youth in engineering.
Ingrid Van Keilegom is a Full Professor at the Faculty of Economics and Business (FEB) at KU Leuven. She is a member of the LISS – KU Leuven Institute of Sports Science and serves as a program director for the POC Master in Statistics. Senior academic staff member of FEB Council and Campus Council Observer in Faculty of Science Council Member of LStat General Assembly Her research focuses on Survival Analysis and Nonparametric Regression , with expertise in: Dependent Censoring Cure Models Measurement Error Correction Quantile Regression Multivariate Statistical Inference Causal Surrogacy Metrics Recent publications emphasize copula-based methods, quantile regression for censored data, and applications to biomedical and economic forecasting. Key projects include instrumental variable approaches for endogeneity in cure models, bankruptcy prediction via machine learning, and measurement error modeling in multivariate settings. Notable affiliations: Centre for Efficiency and Productivity Analysis Collaborations in pharmaceutical statistics and actuarial science
Frederik Questier is a Researcher in Educational Science at Vrije Universiteit Brussel (VUB), located at Pleinlaan 2 in Brussels, Belgium. He holds a PhD in clustering and feature selection methods, demonstrated through his supervision of doctoral theses such as 'Contributions to Clustering and Feature Selection Methods for Clustering' (2005). His work focuses on educational technology, blended learning models, and international ICT initiatives in education systems. Research Interests: Questier explores the intersection of technology and education, including digital media literacy, mobile-assisted language learning (MALL), open-source software implementation, and public health education. His recent work on face masks during the COVID-19 pandemic highlights interdisciplinary collaboration between education and healthcare sectors. Projects & Grants: Active in both fundamental and applied research, he leads projects like the FOD27 e-health initiative (2016–2019) and MarMOOC (2016–2020), which developed hybrid learning systems in Moroccan universities. He also oversees Ghana's ICT education project (2013–2015) and manages international collaborations through VLIR-UOS in Ethiopia. Awards & Recognition: While no explicit awards are listed, his extensive publication record (113+ outputs) and h-index of 17 reflect peer recognition. His work on digital media literacy (2024) and MALL (2019) have garnered significant citations. Labs & Teams: Collaborates with institutions like Routledge (peer-review committee), Vlaams Forum voor Onderwijsonderzoek, and partners in Morocco, Ghana, and Ethiopia through projects addressing ICT integration in education systems.
Jan Ryckebusch is a Senior Full Professor and Department Chair at Ghent University's Faculty of Sciences, Department of Physics and Astronomy . His research bridges Nuclear Physics and Interdisciplinary Physics , with notable contributions to quantum mechanics, statistical mechanics, and machine learning applications. Key research areas include Short-Range Nuclear Correlations , Neutrino-Nucleus Scattering , and Social Network Dynamics . He has supervised multiple PhD students in projects related to Quantum Computing , Agent-Based Modeling , and Statistical Physics of Social Systems . His recent work explores Econophysics (e.g., wealth-income mobility studies) and Opinion Dynamics in social networks. Scientific Awards : No specific awards mentioned in the provided data. Grants & Collaborations : Active in interdisciplinary projects with co-authors across Physics , Economics , and Computer Science , including Luis E C Rocha, Koen Schoors, Wim Cosyn, and others.
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.
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 .
