Prof. Ronald Meester is a Full Professor of Mathematics at the Faculty of Science, Vrije Universiteit Amsterdam. He specializes in mathematical statistics, probability theory, and their applications in legal and environmental contexts. His current positions include director of Meester Advies (Leiden) and expert for Landelijke Deskundigheidsmakelaar Politie (Apeldoorn). He has supervised 14 PhD theses and contributes to interdisciplinary research bridging statistics with law, epidemiology, and environmental policy. Research focuses on Bayesian reasoning, likelihood ratio analysis, and statistical methodologies for legal evidence evaluation. Recent work addresses nitrogen deposition policy critiques and epidemiological study design limitations. His ancillary activities include authorship (since 2003) and teaching roles at SSR Utrecht. Media engagements include commentaries on scientific integrity and environmental policy. Teaching includes the course 'Mathematical Modelling of Stochastic Systems' (2024-2025 academic year). Active in international collaborations and has produced 111 research outputs spanning articles, books, and encyclopedia entries. His work contributes to UN SDGs related to sustainable development through environmental statistical analysis.
Antonia Krefeld-Schwalb is an Assistant Professor at the Department of Marketing Management, Rotterdam School of Management, Erasmus University. With a background in cognitive science and management, her research bridges computational modeling, eye-tracking, and consumer decision-making to address sustainability challenges. Current Affiliation: Assistant Professor, Rotterdam School of Management Research Focus: Sustainable consumer behavior, decision-making processes, and methodological improvements Key Collaborations: Columbia University, University of Geneva, Erasmus Sustainability Program Her cognitive science training informs methodological approaches like mouse/eye tracking and computational modeling applied to marketing problems. She investigates structural parameter interdependencies, external validity threats in surveys, and climate risk communication effectiveness. Recent research trends include climate adaptation strategies, sustainable behavior interventions, and meta-scientific analyses of statistical practices in consumer research. She advocates for heterogeneous population sampling and preregistration to enhance validity. Scientific Honors: Veni Grant (NWO) She develops targeted sustainability interventions through collaborations like the Erasmus Sustainability Program. Her work appears in journals such as PNAS, Journal of Marketing Research, and Psychological Review.
Joris M. Mooij is a Professor of Mathematical Statistics at the Korteweg-De Vries Institute of the University of Amsterdam, Netherlands. His research focuses on causality, spanning causal modeling, discovery, and inference with applications in biology, medicine, fairness, and business analytics. He combines mathematical modeling with statistical and algorithmic approaches in his work. Dr. Mooij received his PhD with honors from Radboud University Nijmegen in 2007, focusing on approximate inference in graphical models. After postdoctoral work at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, he obtained an NWO VENI grant in 2011 for further postdoctoral research at Radboud University. He became Assistant Professor at the University of Amsterdam's Informatics Institute in 2013, was promoted to Associate Professor in 2017, and became a full Professor of Mathematical Statistics in 2020. Dr. Mooij's research centers on causal inference, with particular expertise in structural causal models, cyclic causal systems, and causal discovery algorithms. His work addresses fundamental questions about when causal relationships can be identified from observational data and how to develop robust causal discovery methods that work in complex real-world settings with latent variables, cycles, and selection bias. He has made significant contributions to understanding the limitations of existing causal discovery approaches and developing new methods that overcome these limitations. His research group organizes the Amsterdam Causality Meeting series and develops theoretical frameworks for causal modeling that encompass both acyclic and cyclic systems. Dr. Mooij has collaborated extensively on applications of causal methods in biological systems, including protein signaling networks and gene expression data. The group's recent work explores performative predictions, causal domain adaptation, and robust causal discovery methods that account for selection bias and missing data. Dr. Mooij has received numerous awards for his research, including: Best paper award at UAI for "Establishing Markov equivalence in cyclic directed graphs" IEEE Geoscience and Remote Sensing Society 2011 Letters Prize Paper Award ICML Test of Time Honorable Mention Best student paper award at UAI 2010 He has secured competitive research funding through an NWO VENI grant, NWO VIDI grant, and an ERC Starting Grant, which supported the establishment of his research group consisting of 3 PhD students and 3 postdocs focused entirely on causality. Dr. Mooij has supervised several PhD students, including Tineke Blom, whose work on "Causality and Independence in Perfectly Adapted Dynamical Systems" significantly influenced his thinking about causality in complex systems. He has co-taught the MasterMath course on Causality and published lecture notes titled "A Mathematical Introduction to Causality." His research continues to push the boundaries of causal inference methodology and its applications across diverse scientific domains.
Chen Zhou is a Full Professor of Mathematical Statistics and Risk Management at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. He is a member of the Research Advisory Committee of Erasmus School of Economics and actively contributes to academic leadership and research governance. His research focuses on extreme value statistics and financial risk management , with significant contributions to the theoretical and applied understanding of extreme events in financial and statistical contexts. His work bridges mathematical rigor with practical applications in finance and econometrics. The recent publications highlight a strong trend in advancing methodologies for extreme value estimation, including bootstrapping techniques, tail copula modeling, dimension reduction for extremes, and semi-supervised frameworks. These works are published in high-impact journals such as the Journal of the American Statistical Association , Bernoulli , and the Journal of Finance , indicating broad disciplinary relevance across statistics, econometrics, and finance. Editorial work: Editor, Extremes (since 2015) He teaches in the Bachelor program of Econometrics and Management Science and the MSc program in Quantitative Finance, and is affiliated with the Tinbergen Institute. He has supervised multiple doctoral students, reflecting his active role in academic mentorship and research training. Chen Zhou leads a research network focused on extreme value theory, systemic risk, and statistical inference, collaborating with leading scholars in the field. His work continues to shape methodological developments in the analysis of rare and high-impact events.
Prof. Dr. Sjoerd Beugelsdijk is a Professor of International Business at the Faculty of Economics and Business, University of Groningen. He also serves as Research Director and holds a visiting professorship at KU Leuven. His expertise spans globalization, cultural differences, and national identity, with a focus on their economic implications. He earned his PhD from Tilburg University in 2003, focusing on cultural diversity and regional development. Research Interests: Beugelsdijk’s work explores cultural diversity’s impact on economic globalization, firm internationalization, and national identity. He has published over 70 refereed articles in top journals like the Journal of International Business Studies and Journal of Economic Geography. His recent projects include a government advisory report on Dutch national identity (SCP 2019) and the JIBS Silver Medal (2019) for contributions to international business research. Education: PhD (Tilburg University, 2003), MSc (Tilburg University, 1999) Grants: Over €1 million in funding from NWO (Rubicon, Veni, Vidi) and others Editorial Roles: Editor of Journal of International Business Studies (2016–present) Advising: Supervised 11 PhD students and served on numerous doctoral committees Key Achievements: Awarded the JIBS Silver Medal (2019), top teacher awards (2016–2018), and recognition for best papers in Academy of Management and Academy of International Business conferences. His research bridges economic geography, management, and sociology, addressing global value chains, cultural distance, and institutional hazards. Labs/Teams: Leads the Global Economics & Management research group at Groningen, collaborating internationally with scholars from Copenhagen Business School, Bocconi University, and others.
Dr. Tom Boot is an Associate Professor at the Department of Economics, Econometrics & Finance at the University of Groningen. He holds a PhD in Econometrics from Erasmus University Rotterdam (2017) and an MSc in Econometrics from the same institution (2012), along with an MSc in Physics from the University of Groningen (2010). His research focuses on econometric theory applied to macroeconomic forecasting, high-dimensional data analysis, and causal inference. He has been recognized with the Veni grant (2021–2024) for his work on forecasting methodologies. Boot’s research interests include improving forecast accuracy through methods like subspace projections, structural break modeling, and privacy-aware marketing analytics. His recent work explores privacy-utility trade-offs in data-driven marketing and unbiased estimation techniques for clustered errors. He has supervised PhD students including Jhordano Aguilar Loyo and Gilian Ponte, whose theses addressed panel data heterogeneity and differential privacy applications. Boot is also a program director for the MSc Econometrics, Operations Research, and Actuarial Studies (since 2024). His contributions to econometrics span over a dozen peer-reviewed publications, with a focus on advanced statistical techniques for economic forecasting and policy analysis. Collaborations include work with institutions like Harvard/MIT and the organization of workshops on causal inference and machine learning.
Rianne de Heide is an Assistant Professor in the Statistics group (STAT) within the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science. She maintains collaborative arrangements with LUXs Data Science in Leiden, CWI, and VU Mathematics in Amsterdam as a guest researcher while working partly remotely during her family's relocation. Her academic journey includes a previous position as Assistant Professor at Vrije Universiteit Amsterdam. PhD Dissertation: 'Bayesian Learning: Challenges, Limitations and Pragmatics' (2020) MSc Thesis: 'The Safe-Bayesian Lasso' (2016) De Heide's research spans multiple interconnected domains within statistics and machine learning, with particular emphasis on developing mathematically rigorous frameworks that remain accessible to diverse audiences. Her work bridges theoretical foundations with practical applications, focusing on hypothesis testing with e-values, Bayesian learning methodologies, and best-arm identification problems in multi-armed bandit settings. She demonstrates exceptional interdisciplinary range, connecting statistical theory with philosophical inquiry and even theological discussions as evidenced by her publications on biblical authorship verification and mathematical beauty. Analysis of her publication trajectory reveals a clear evolution toward developing anytime-valid statistical methods, particularly through e-values and e-processes for multiple testing scenarios. Her recent work shows increasing focus on foundational questions in statistical inference while maintaining strong connections to practical machine learning applications. The 2024 'Safe Testing' paper in the Journal of the Royal Statistical Society represents a significant contribution that generated a formal discussion meeting. VENI project 'E-values for Multiple Testing' NWO M2 grant of €742,708 with Jelle Goeman (funding 2 PhD students and a scientific programmer) 2025 Bernoulli Society New Researcher Award De Heide actively supervises research through her VENI project and the NWO M2 grant, while also contributing to broader academic service through the 'Kindness and Excellence in Academia' initiative she co-founded. This initiative addresses critical cultural issues in academic environments through opinion pieces, resources, and community building around compassionate academic practices. She has organized specialized events like the E-Day meet-up for e-value researchers at CWI in Amsterdam, demonstrating leadership in her niche research community. Her research activities are centered around the Statistics group at the University of Twente, with significant external collaborations through the E-mailing list for e-value researchers and partnerships with institutions including CWI, VU Amsterdam, and Leiden's LUXs Data Science. The interdisciplinary nature of her work creates connections across mathematics, computer science, philosophy, and even religious studies.
Wouter M. Koolen-Wijkstra is a Professor of Mathematical Machine Learning at the University of Twente (Statistics group) and a Scientific Staff Member at Centrum Wiskunde & Informatica (CWI), Amsterdam, in the Machine Learning department. His research bridges theoretical machine learning, game theory, and statistics, with active projects on multi-armed bandits, online learning, and safe inference methodologies. He co-leads INRIA-CWI associate teams (6PAC and 4TUNE) and is an ELLIS Scholar. His work emphasizes provable guarantees in learning algorithms, including: Regret minimization under risk-averse scenarios Multi-scale adaptation in online decision-making Game-theoretic equilibria computation Anytime-valid statistical inference via e-processes Recent publications demonstrate a focus on robust learning frameworks , particularly in bandit problems, hypothesis testing, and Nash equilibrium characterization, often leveraging information-theoretic and optimization principles. Awards include: Veni Grant (2015) for 'Learning at the Intrinsic Task Pace' QUT Vice-Chancellor's Fellowship (2013) for multitask learning Rubicon Grant (2010) for game-theoretic online learning ELLIS Scholar recognition He teaches graduate courses on Machine Learning Theory and Graphical Models at CWI. Current grants include collaborations with INRIA (4TUNE and 6PAC teams) and industry partnerships (e.g., PPS Booking.COM).
Dr. Lennert Coenen is an Assistant Professor at the Department of Communication and Cognition within the Tilburg School of Humanities and Digital Sciences at Tilburg University. His research focuses on media effects, statistical methodology in communication science, and the philosophical foundations of research design. He has held a visiting professor position at KU Leuven (Feb–Sept 2021). His work critically examines issues like moderation analysis, omitted variable bias in mediation studies, and the implications of algorithmic platforms on media exposure. Research Interests include: Media effects and epistemological challenges in communication research Statistical methodologies such as strong-form frequentist testing and mediation analysis Philosophical issues in hypothesis testing and theoretical interpretation Family technoference and digital media's societal impacts Recent publications highlight critical examinations of research methodology and the theoretical implications of statistical findings in communication science. Collaborations include work with Tim Smits on frequentist testing frameworks and interdisciplinary projects on algorithmic governance. Teaching responsibilities include courses on statistics, data visualization, and media effects analysis.
Alain Hecq is a Full Professor in the department of QE Econometrics at the School of Business and Economics, Maastricht University. His research focuses on econometric methodologies, particularly in time series analysis, noncausal models, and financial econometrics. He has contributed significantly to the understanding of volatility dynamics, cryptocurrency markets, and inflation targeting regimes. His work often addresses policy-relevant questions in macroeconomics and financial markets. Key research interests include mixed causal-noncausal autoregressive models, volatility modeling with MARMA-GARCH frameworks, and the application of these techniques to real-world phenomena such as oil price bubbles and cryptocurrency volatility. He has also explored the credibility of central banking policies during crises, such as the Brazilian inflation-targeting regime during the pandemic. His recent work emphasizes methodological advancements in high-dimensional time series analysis, including spectral estimation, hierarchical regularizers for mixed-frequency data, and reduced-rank matrix autoregressive models. These contributions reflect a blend of theoretical rigor and practical applicability in addressing complex economic and financial problems. While no formal awards are listed, his extensive publication record and focus on cutting-edge econometric techniques underscore his scholarly impact. Advising and grant activities are not detailed in the provided information, but his research demonstrates sustained engagement with both academic and policy-oriented audiences.
Alexey Ivashchenko is an Assistant Professor of Finance at VU Amsterdam's School of Business and Economics. He holds a PhD in Finance from the University of Lausanne and the Swiss Finance Institute, along with a Master's in International Finance from HEC Paris and a Master's in Macroeconomics from Lomonosov Moscow State University. His research focuses on empirical asset pricing and market microstructure, particularly in corporate bond markets. Notable contributions include studies on liquidity dry-ups' pricing implications, callable bond price dynamics, and corporate bond price reversals. He has received the INQUIRE Europe 2021 Research Grant for his work. Teaching includes the core master’s course 'Financial Markets and Institutions' at VU Amsterdam and previously 'Fixed Income and Credit Risk' at the University of Lausanne. His collaborative research spans 323 international scholars, reflecting his active role in global finance academia.
Ling Chang is an Associate Professor at the University of Twente, affiliated with the Faculty of Geo-Information Science and Earth Observation (ITC) and the Department of Earth Observation Science. She holds a Ph.D. from Delft University of Technology (2015) and an M.S.E. from Tongji University (2010). Her research focuses on statistical hypothesis testing, time series modeling, and change detection using satellite remote sensing, particularly InSAR techniques. Key projects include AlignSAR (ESA Open SAR library) and RailRadar (TUDelft-ProRail collaboration). Education: M.S.E. in Geodesy and Survey Engineering, Tongji University, China (2010) Ph.D. in Geodesy, Delft University of Technology, Netherlands (2015) Research interests emphasize InSAR applications in infrastructure monitoring, environmental geology, and disaster risk assessment. Notable achievements include the Prof. J.M. Tienstra Research Prize (2022) and leadership in the NCG talent program (2020). Teaching responsibilities include coordinating the Radar Remote Sensing course. Her work contributes to UN SDGs related to sustainable infrastructure and climate action.
Dr. Andreas Alfons is an Associate Professor in the Department of Econometrics at Erasmus School of Economics, Erasmus University Rotterdam. His research focuses on robust statistical methods, machine learning, psychometrics, and software development for high-dimensional data. He leads the NWO Vidi project on robust analysis of rating-scale data and contributes to the interdisciplinary project on digital decision support. He is an editor for the Journal of Statistical Software and Journal of Data Science, Statistics, and Visualization. His research interests include robust statistical learning, high-dimensional data analysis, and open science practices. Key contributions include R packages like robmed , robustHD , and simFrame . Recent work addresses careless responding in surveys and robust mediation analysis. Publications span journals such as Computational Statistics & Data Analysis , Econometrics and Statistics , and Journal of Statistical Software . His work emphasizes reproducibility and software tools for statistical analysis.
Daniël Lakens is an Associate Professor in the Human Technology Interaction group at Eindhoven University of Technology (TU/e), with expertise in meta-science, research methods, and applied statistics. His empirical research focuses on conceptual thought, similarity, meaning, study design, and reward structures in science. He has contributed to replication research and open science initiatives. Academic Background: PhD in Experimental Psychology (2010) from Utrecht University Grants: NWO VIDI grant (2017), pilot project for replication research grants Teaching: MOOC on 'Improving Your Statistical Inferences', TU/e Teacher of the Year (2014), over 40 workshops on open science His research spans methodologies to optimize study structures, meta-statistics, and interdisciplinary collaborations. Recent publications include datasets and interventions in esports, behavioral modeling, and methodological frameworks. Scientific Awards: Ammodo Science Award for Fundamental Research (2023) Leamer-Rosenthal Prize for Open Social Science (2017) NWO Vidi Award (2017) Datasets and projects highlight reproducibility in psychology, computational models for goal-directed behavior, and open data practices. He serves on editorial boards and has supervised 23 students, with current courses in advanced research methods and ethics.
Katharina Riebel is an Associate Professor at the Institute of Biology Leiden (IBL) within Leiden University, specializing in animal behavior, cognition, and vocal learning. Her work bridges evolutionary biology and experimental psychology, focusing on sexual selection and multimodal signaling in birds. Her research explores how phenotypic plasticity and learning shape mating signals and preferences, with a particular emphasis on acoustic communication cultural transmission in songbirds urban ecological impacts on avian acoustics . She has contributed to foundational studies in bird song, including temporal variation in song structure and cultural evolution. Recent work, such as the 2019 publication on Frontiers in Ecology and Evolution , examines multimodal percepts in mating signals. Her 2004 Journal of Avian Biology paper quantified trill-flourish dynamics in chaffinch song. Scientific awards include the Human Frontier Science Program award (2016) . She advises PhD candidates like Jiangnan Sun and former advisee Jing Wei .
