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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

• Survival Analysis
• Nonparametric Regression
• Semiparametric Regression
• +4 more

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.

• Educational Technology
• Blended Learning
• Digital Media Literacy
• +3 more

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

Rishikesh Yadav is a postdoctoral researcher at the Department of Mathematics and Mathematical Statistics, Umeå University, Sweden , and previously held a postdoctoral position at the Namur Institute for Complex Systems (naXys), University of Namur, Belgium . He earned his Ph.D. in Approximation Theory from Sardar Vallabhbhai National Institute of Technology Surat, India. His research focuses on Approximation Theory, Functional Analysis, Dynamical Systems, Operator Theory, and Optimization Theory. Notable contributions include work on Koopman operators, control theory, and compressed sensing. He has received awards such as the V. M. Shah Prize (2020) and Best Oral Presentation Award (2019). Recent research emphasizes developing algorithms for convex optimization on measure spaces, supported by the Kempe Foundation (2024–2026). His work bridges approximation theory with dynamical systems, including publications on Szász-Mirakjan operators, statistical convergence, and Koopman operator approximations via Bernstein polynomials. Grants/Fellowships: Kempe Foundation (2024–2026) Labs/Teams: naXys Institute (Belgium), Umeå University's Department of Mathematics

• Approximation Theory
• Functional Analysis
• Dynamical Systems
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Francisco Jara Avila is a Researcher at Vrije Universiteit Brussel's Faculty of Engineering Sciences within the Engineering Technology department, specializing in wind energy systems and machine learning applications. His work focuses on improving wind turbine performance monitoring and predictive capabilities. His research interests center on wind turbine analytics , where he develops advanced methodologies for power prediction, fault detection, and performance optimization. Using techniques like deep learning, Gaussian process regression, and signal processing, his work addresses critical challenges in wind farm operations including uncertainty quantification, spatial correlation analysis, and drivetrain health monitoring. His publication record shows a clear trajectory in wind energy analytics, with increasing sophistication from basic fault detection (2023) to sophisticated uncertainty-aware power prediction frameworks (2025). His work consistently bridges theoretical machine learning approaches with practical wind farm operational challenges. While no specific awards are listed in the available information, his publications in reputable journals like IOP Journal of Physics: Conference Series and Energies demonstrate recognition in the wind energy research community. As a researcher focused on technical development rather than academic mentoring, there's no indication of student supervision activities in the available information. His work appears to be conducted within the Acoustics & Vibrations Research Group, collaborating with researchers like Helsen, Verstraeten, and Nowe on wind energy analytics projects.

• Wind turbines
• SCADA systems
• Wind power
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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
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Florian Hoffmann is an Associate Professor of Finance at the Faculty of Economics and Business (FEB) of KU Leuven. He holds a Ph.D. from the University of Frankfurt and previously held academic positions at the University of Bonn and Erasmus University Rotterdam. His research focuses on corporate finance, governance, financial regulation, contract theory, and information economics. He is a co-promotor for projects on ESG information quality and multivariate statistics in finance. Education & Positions: Ph.D. in Finance, University of Frankfurt Associate Professor, KU Leuven FEB (since Fall 2022) Previous roles at University of Bonn and Erasmus University Rotterdam Teaching: Capital Investment Policy (Leuven, Bachelor) The Economics of Insolvency (Leuven, Master) Advanced courses in corporate finance, behavioral finance, and contract theory Research Interests: His work explores dynamic contracting, deferred compensation mechanisms, selective disclosure in marketing, and regulatory frameworks for financial institutions. Recent studies address worker runs in banking, auction vs. negotiation payment structures, and ESG information trustworthiness. Grants & Projects: ESG Information Quality Project (2022–2026) Multivariate Statistics in Finance (2020–2024) Labs/Teams: Active in the Finance Research Group at KU Leuven, collaborating on interdisciplinary projects involving regulation, corporate governance, and financial markets.

• Corporate Finance and Governance
• Financial Regulation
• Contract Theory
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Thomas Neyens is an Associate Professor at the Department of Public Health and Primary Care, KU Leuven, affiliated with the Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat). His work focuses on spatial statistics, hierarchical modelling, ecology, and public health, with notable contributions to epidemiological and ecological data analysis during the COVID-19 pandemic. He holds roles as promotor/co-promotor in interdisciplinary projects such as 'Trajectories of newly emerging evolving metacommunities' and 'A geostatistical framework to forecast malaria incidence'. His research integrates statistical methodologies with real-world applications in environmental health, disease mapping, and clinical data analysis. Recent publications highlight advancements in joint modelling approaches, analysis of opportunistic ecological datasets, and spatio-temporal epidemiology of respiratory pathologies. He teaches courses like Linear Models and Scientific Methodology at KU Leuven. Neyens collaborates with institutions like the One Health Institute and has contributed to projects evaluating vaccination effects, pandemic interventions, and environmental health impacts. His work emphasizes methodological developments for complex healthcare and ecological data.

• Spatial Statistics
• Hierarchical Modelling
• Ecology
• +4 more

Dr. Alexander Bertrand is a Professor at the Faculty of Engineering Sciences , KU Leuven, heading the Dynamic Systems, Signal Processing and Data Analysis (STADIUS) division. He leads the Department of Electrical Engineering (ESAT) and contributes to Leuven.AI institute, with expertise spanning wireless sensor networks, brain-computer interfaces (BCI), and biomedical signal processing. Research Focus : Wireless acoustic/EEG sensor networks, distributed signal enhancement, adaptive filtering, neural decoding of auditory/visual attention, and AI-driven time series analysis. Key Projects : EEG-Linx platform for modular brain recordings (2025-2027) Calibration-free BCI systems (2025-2029) AI quality assessment for time series data (2024-2028) Wireless EEG patches for hearing technology (2024) Publications (2023-2025) demonstrate leadership in distributed signal processing , auditory attention BCI , and scalable sensor architectures , with applications in education, healthcare, and wearable tech. Teaching includes courses on digital signal processing, biomedical data analysis, and medical technology design.

• Wireless Sensor Networks
• Brain-Computer Interfaces (BCI)
• EEG Signal Processing
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Stefan Van Aelst is a Professor at the Department of Mathematics, KU Leuven, and heads the Statistics and Data Science unit. He is actively involved in research on robust statistical methods for complex and high-dimensional data. Affiliation: Faculty of Science, KU Leuven Research: Robust statistics, functional data, fuzzy data, and applications in chemometrics and bioinformatics Teaching: Advanced Statistical Methods, Robust Statistics, Statistical Data Analysis His recent publications focus on robust dispersion estimation, sparse logistic regression, functional data analysis, and multilinear PCA. Collaborations include researchers like Rousseeuw, Zamar, and Verdonck. He contributes to software development, including the MATLAB library LIBRA and R packages like robustbase and cellWise , and participates in organizing seminars and conferences.

• Robust statistics
• Analysis of complex data
• Functional data analysis
• +5 more

Steven Vanduffel is a Professor at Vrije Universiteit Brussel (VUB) in Belgium, affiliated with the Department of Finance, Insurance, Risk & Economics. He heads the FIRE (Finance, Insurance, Risk & Economics) research group and holds additional administrative positions including Rector, Vice Rector, Dean, or Department Chair roles. His office is located at Pleinlaan 2, 1050 Brussels. Professor Vanduffel's research focuses on quantitative risk management , actuarial science , and financial decision-making with specialized expertise in portfolio optimization, dependence modeling, longevity risk, pension systems, and Value-at-Risk methodologies. His work bridges theoretical finance with practical applications in insurance and pension design. His extensive publication record shows consistent focus on: Advanced risk measurement techniques and model uncertainty Optimal risk-sharing mechanisms in pensions and insurance Portfolio construction under complex constraints Statistical methods for financial applications Awards & Recognition: Robert I. Mehr Award (2022) Robert C. Witt Award (2018) PRMIA Frontiers in Risk Management Award (2014) Johan de Witt Prijs (2012) Lloyds Science of Risk Prize (2011) He actively advises graduate students, with recent Master's theses supervised on topics including COVID-19 impacts on businesses, pension systems, investment behavior, and insurance risk analysis. Current research projects (2024-2029) focus on systemic risk, longevity risk sharing, and dependence modeling applications in finance and insurance.

• Financial Risk Management
• Actuarial Science
• Portfolio Optimization
• +4 more

Davy Paindaveine is a Professor of Mathematical Statistics at the Université Libre de Bruxelles (ULB), affiliated with ECARES and the Department of Mathematics. He is also an Associate Member of the Toulouse School of Economics. His academic career includes visiting professorships at Université Pierre-et-Marie Curie (2009-2014), KU Leuven (2015), and the Toulouse School of Economics (2015-2016, 2019-). Research Interests: Paindaveine’s work focuses on asymptotic statistics , nonparametric inference , and high-dimensional statistics . He explores Le Cam’s theory of asymptotic experiments, contiguity, and optimal statistical procedures. His contributions to directional statistics address inference on high-dimensional spheres, while his research on statistical depth and multivariate quantiles develops robust methods for complex data structures. Recent Publications highlight advancements in principal component directions under weak identifiability, spatial quantiles in high dimensions, and optimal tests for rotational symmetry. These works span journals like the Annals of Statistics , Bernoulli , and Journal of the American Statistical Association , reflecting his expertise in nonparametric and high-dimensional inference. Scientific Awards: Fellow of the Institute of Mathematical Statistics (2018) Fellow of the American Statistical Association (2019) Gottfried E. Noether Young Scholar Award (2007) Prix Adolphe Wetrems (2007) Prix MJLD (2003) Elected member of the International Statistical Institute Editorial Roles: Paindaveine served as Editor-in-Chief of the Bernoulli (2022-2024) and former editorial boards of the Annals of Statistics and Journal of the American Statistical Association . His teaching portfolio includes courses in Mathematical Statistics , Graduate Statistics , Probability , and Regression Models .

• Asymptotic Statistics
• Nonparametric Inference
• High-Dimensional Statistics
• +3 more

Michel Meulders serves as Associate Professor of Statistics at the Operations Research and Statistics Research Group (ORSTAT) within KU Leuven's Faculty of Economics and Business. His dual-campus appointments (Brussels and Leuven) reflect his interdisciplinary work bridging statistical methodology with psychological applications, particularly in discrete choice modeling and pain research. Education: 1994: Licentiate in Psychology 2000: PhD in Psychology Research Focus: Dr. Meulders' work centers on discrete choice modeling and multivariate statistics , with significant contributions to quantitative psychology and psychometrics . His methodological innovations address sample size determination , preference heterogeneity , and attribute non-attendance in choice experiments, while applied research explores pain perception and avoidance behavior modulation through positive affect and social support. Publication Trends: Recent work (2022-2025) reveals two converging streams: (1) advanced discrete choice methodologies (machine learning integration, experimental design optimization) and (2) psychological applications (VR pain interventions, social media impact on health behavior). This interdisciplinary approach uniquely connects econometric modeling with clinical psychology through rigorous statistical frameworks. Academic Leadership: PhD Supervision: Primary advisor for S.Y. Assele (discrete choice experiments) and A. Gutierrez Vargas (heterogeneity modeling) Current Projects: Topics in Discrete Choice Modeling (2022-2026) - Co-promotor Heterogeneity in Discrete Choice Models (2019-2023) - Co-promotor Discrete Choice Experiments: Screening & Framing (2018-2023) - Co-promotor Administrative Roles: Head of Subdivision 29 (Brussels Campus), Council member for FEB and Campus Council Research Ecosystem: Dr. Meulders operates at the intersection of ORSTAT's operations research expertise and the Quantitative Psychology group's behavioral science focus, facilitating cross-campus collaborations on statistical methodology development and psychological application testing. His work with the LStat General Assembly further extends methodological influence across KU Leuven's research infrastructure.

• Discrete Choice Modelling
• Multivariate Statistics
• Quantitative Psychology
• +3 more

Tine Verdonck is a Professor and medical epidemiologist at the Institute of Tropical Medicine (ITM) in Antwerp, Belgium. She holds a PhD from the University of Antwerp, focusing on HTLV-1 epidemiology in Peru. She has been involved in research and teaching since 1998, specializing in neglected tropical diseases, diagnostics, and public health approaches to zoonotic diseases. She serves as Vice Course Director of ITM’s Master of Public Health and leads modules in evidence-based decision-making, multivariable statistics, and zoonosis control. Her teaching spans international collaborations, including the MSc in Tropical Animal Health with the University of Pretoria and Berlin’s Charité Institute. Research interests include HTLV-1, leishmaniasis, and syndromic approaches to neglected diseases, with a focus on epidemiology and evidence synthesis. She has co-supervised two PhD students and contributed to major projects like NIDIAG (integrated diagnostics) and FA5 PERU (infectious disease policy). She has authored over 120 publications, reviews manuscripts for top journals (e.g., Clinical Infectious Diseases), and serves on ITM’s Research Integrity Committee and PLoS ONE’s editorial board. Her service activities include organizing conferences (e.g., 2009 Colloquium on Neglected Tropical Diseases in Latin America), scholarship selection committees, and participation in Belgian-Peruvian development forums. Her work integrates clinical, epidemiological, and public health perspectives to address global health challenges.

• Epidemiology
• Tropical Medicine
• Infectious Diseases
• +7 more

Daniele Marinazzo is a Full Professor in the Department of Data Analysis at Ghent University (Belgium), with a background in statistical physics and a focus on computational neuroscience and complex systems. He serves as Deputy Editor at PLOS Computational Biology , Co-Editor-in-Chief at Neurons, Behavior, Data Analysis, and Theory , and academic editor at Network Neuroscience and Imaging Neuroscience. His work spans theoretical physics, neuroscience methodology, and experimental neurophysiology. Academic Affiliation: Ghent University (Belgium) Editorial Roles: 3+ journals Research Initiatives: EBRAINS representative Research Interests combine computational neuroscience, complexity science, and network analysis. He investigates higher-order interactions in brain networks, synergistic information processing, and methodological advancements in neuroimaging. Recent work includes MEG/EEG analysis, gene expression modeling, and developing open-source tools like SPM-Python. Scientific Contributions include 15+ recent publications on topics ranging from individual brain fingerprinting to multisensory integration models. His work appears in venues like Nature Communications , Physics of Life Reviews , and Entropy , with methodological innovations in hidden factor localization and network synchronization analysis. Current projects include PhD/postdoc recruitment for fast-sampled fMRI analysis and organizing the Erice workshop Higher-Order Interactions: Mechanisms, Behaviors, and Networks (August 2025).

• Computational Neuroscience
• Complex Systems
• Neurophysics
• +4 more