Lourens Waldorp is an Associate Professor at the University of Amsterdam within the Faculty of Social and Behavioural Sciences , specifically the Department of Psychological Methods . His research focuses on network theory, causal inference, and statistical modeling in psychology and neuroscience. University of Amsterdam IAS Fellow (2024) His research interests include: Network psychometrics Causal inference in psychological models High-dimensional statistical methods Dynamical systems in psychopathology Graph theory applications Signal processing for biophysical data The trends in his recent publications center on causal modeling, network analysis of psychopathology, and statistical techniques for time-series data. He has developed methods for perturbation graphs, moderated network models, and dynamic intervention frameworks. Notable scientific awards : IAS Fellowship for 6 months (2024) He advises PhD students like Kyra Evers and collaborates with researchers across disciplines, including J. Haslbeck , D. Borsboom , and O. Ryan . His work intersects with network theory and clinical psychology .
Aad van der Vaart is a Professor of Stochastics at Leiden University's Mathematical Institute. He was awarded the prestigious NWO Spinoza Prize in 2015 for groundbreaking work in mathematical statistics, particularly Bayesian methods applied to medical imaging, genetic data, and complex models. His research bridges pure mathematical theory with applied domains like neuroscience and astronomy. Research Interests : Van der Vaart focuses on infinite-dimensional Bayesian statistics, nonparametric models, and statistical genetics. His work emphasizes rigorous mathematical analysis of prior distributions and their impact on data-driven conclusions. Applications include gene network modeling and PET scan image reconstruction. Key Contributions : Authored influential books on estimation theory; pioneered modern Bayesian approaches to high-dimensional data. His Spinoza Prize funds will support interdisciplinary research and hiring new talent in statistical methods. Awards : NWO Spinoza Prize (2015), recognized as a global leader in statistical theory. Future Directions : Expanding into astronomical data analysis and medical applications, leveraging Bayesian frameworks for big datasets.
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.
Dr. Peter J.F. Lucas is a Full Professor specializing in Datamanagement & Biometrics with over 35 years of experience in artificial intelligence, probabilistic graphical models, and clinical decision support systems. His research spans intelligent systems, machine learning, and eHealth, with a focus on applying Bayesian networks and probabilistic logic to medical and non-medical domains.
Dr. David Goretzko is an Assistant Professor in the Department of Methodology and Statistics at Utrecht University's Faculty of Social and Behavioural Sciences. He leads the Measurement and Machine Learning Lab and specializes in the integration of data science techniques with psychometric theory. His academic journey includes: Ph.D. in Psychological Methods from LMU Munich (2020) M.Sc. in Statistics from LMU Munich (2018) M.Sc. in Psychology from LMU Munich (2016) B.Sc. in Physics from LMU Munich (2015) B.Sc. in Psychology from LMU Munich (2014) Dr. Goretzko's research primarily focuses on the intersection of machine learning and psychometrics. His work addresses critical challenges in factor analysis, measurement invariance, and model fit assessment. He develops innovative methods that combine traditional psychometric approaches with modern data science techniques, particularly in the areas of exploratory factor analysis trees, regularized factor analysis, and cost-sensitive machine learning applications in psychological assessment. His research has significant implications for improving the validity and reliability of psychological measurements across diverse populations. His recent publications reveal a strong trend toward integrating machine learning methodologies with traditional psychometric approaches. A significant portion of his work focuses on factor analysis techniques, particularly addressing the challenge of determining the appropriate number of factors. His research also extensively covers measurement invariance testing across multiple covariates using tree-based approaches, and he has made notable contributions to evaluating model fit in confirmatory factor analysis. The interdisciplinary nature of his work spans psychology, statistics, and computer science. Dr. Goretzko serves as an Associate Editor for the European Journal of Psychological Assessment and is an active reviewer for numerous prestigious journals including Psychological Methods, Behavior Research Methods, and Structural Equation Modeling. He also reviews grant proposals for major funding agencies such as the German Research Foundation (DFG), National Science Foundation (NSF), and Dutch Research Council (NWO). He currently holds a Project Grant from the German Research Foundation (DFG GO 3499/1-1) since 2021. His research program focuses on developing and validating new methodologies for psychological assessment that incorporate machine learning techniques while maintaining psychometric rigor. His work has practical applications in educational measurement, clinical psychology, and organizational assessment. Dr. Goretzko leads the Measurement and Machine Learning Lab at Utrecht University, which focuses on developing innovative methodologies that bridge the gap between traditional psychometrics and modern data science. The lab's research has particular relevance for improving measurement practices in cross-cultural research, educational assessment, and clinical psychology settings where measurement invariance and factor structure validation are critical concerns.
Charles E.H. Berger serves as Professor by Special Appointment in Criminalistics at Leiden University's Institute for Criminal Law and Criminology since November 2011, a position funded by the Stichting Leerstoel Criminalistiek. He concurrently holds a principal scientist position at the Netherlands Forensic Institute (NFI), where he contributes to education, R&D strategy, and research on forensic evidence interpretation. His research program centers on logically sound interpretation of forensic evidence through probability theory and computational methods. Berger specializes in applying Bayesian statistics to forensic anthropology, personal identification, and evidential evaluation. His work emphasizes moving forensic science toward activity-level interpretations while managing contextual information to prevent bias. Berger plays a pivotal international role as member of ISO technical committee TC272, serving as lead editor for Part 4 (Interpretation) of the ISO-21043 Forensic Sciences standard. His scholarly contributions focus on improving forensic reasoning frameworks and establishing objective evaluation methodologies. His publications demonstrate consistent engagement with foundational forensic science challenges, particularly in developing statistically rigorous approaches to evidence interpretation that maintain scientific integrity within legal contexts. Berger actively promotes scientifically sound practices across the criminal justice system, emphasizing the importance of clear communication between forensic scientists, legal professionals, and other stakeholders to ensure proper understanding and application of forensic evidence.
Nezihe Merve Gürel is an Assistant Professor in Computer Science at Delft University of Technology (TU Delft), affiliated with the Pattern Recognition & Bioinformatics Group within the Intelligent Systems Department of the Faculty of Electrical Engineering, Mathematics and Computer Science. Her research focuses on developing robust, reliable, and efficient machine learning methods with enhanced reasoning capabilities, bridging theoretical rigor and practical applications. She emphasizes data-centric approaches to improve ML systems. Education: PhD in Computer Science from ETH Zurich, MSc from EPFL (Switzerland). Research Interests: ML robustness, reliability, reasoning, data-centric ML, federated learning, and explainable AI. Her recent work includes certified robustness for retrieval-augmented models and time-efficient learning algorithms. She has contributed to the Journal of Data-centric Machine Learning Research as an executive editor and served as a reviewer for top ML conferences (NeurIPS, ICML, ICLR). She previously held roles at IBM Research, Stanford University's Human-Centered AI Lab, and Westlake Institute for Advanced Study. Her awards include the Generation Google Scholarship and Cisco Research Funding . Scientific Awards : Generation Google Scholarship (2021) Cisco Research Center University Funding Labs & Teams : She leads research in the Pattern Recognition Laboratory at TU Delft and collaborates with international institutions like Stanford and Westlake Institute for Advanced Study.
Rob van Beers is an Assistant Professor at the Faculty of Behavioural and Movement Sciences at Vrije Universiteit Amsterdam, with affiliations to Neurocontrol, IBBA, and AMS - Sports. His research focuses on human motor control, spatial perception, and computational modeling using Bayesian approaches to understand sensory-motor integration under uncertainty. He holds ancillary roles as a Researcher at Radboud University (Nijmegen) since 2015 and serves on the Editorial Board of the Journal of Neurophysiology since 2015. His work contributes to UN Sustainable Development Goals related to health and well-being. Key research interests include motor learning dynamics, sensorimotor adaptation, and the neural basis of spatial orientation. Recent studies explore Alzheimer’s impacts on motor adaptation and Bayesian inference in vestibular path integration. Teaching responsibilities include courses on linear systems dynamics, physical measurement techniques, and motor systems regulation. His work spans 42 peer-reviewed articles, with datasets published on platforms like Dryad and Zenodo.
Michel Mandjes is a Professor at the University of Amsterdam's Faculty of Science and holds a Visiting Professor position at the Faculty of Economics and Business (FEB). His research focuses on stochastic processes, queueing theory, and probability theory, with applications in risk modeling, network analysis, and operations research. Recent publications highlight his contributions to multivariate Hawkes processes , Lévy-driven systems , and dynamic random graphs , emphasizing large deviations, rare event simulation, and statistical inference. His work bridges theoretical probability with practical challenges in traffic flow, financial risk, and social network modeling. The trends in his research include the development of stochastic models for network stability, appointment scheduling optimization, and inference techniques for non-stationary processes. His methodological innovations often leverage advanced probability theory and queueing frameworks to address real-world problems in transportation, healthcare, and finance.
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.
Dr. Serhan Sadikoglu is an Assistant Professor at Tilburg University's Tilburg School of Economics and Management (TiSEM), Department of Econometrics and Operations Research. He earned his PhD in Econometric Theory from Tilburg University in 2019. His research focuses on econometric theory, semiparametric estimation, robust statistics, and panel data analysis. He has published in prestigious journals such as Econometric Reviews and Econometrics Journal , with recent work addressing nonseparable panel models and robust estimation techniques. Sadikoglu teaches courses including Quantitative Methods, New Venture Creation and Growth, and Statistics for Econometrics. His research emphasizes methodological contributions to econometrics, particularly in handling complex data structures and improving estimation robustness.
Sible Andringa is Professor of Second Language Pedagogy at the University of Amsterdam's Faculty of Humanities, officially inaugurated on June 16, 2023. Dr. Andringa serves as Academic Director of the Institute for Dutch Language Education (INTT), Coordinator of the Language Learning, Literacy and Multilingualism research group, and Coordinator of the Master's program in Dutch as a Second Language and Multilingualism. Dr. Andringa's research focuses on second language acquisition and bilingualism, specifically investigating the added value of explicit instruction, how input distribution affects language learning outcomes, and the role of awareness in language learning trajectories. Key ongoing projects include the Meta-LLL project examining how literacy shapes language learning, the SLA4All initiative for reproducing SLA research with non-academic samples, and the OASIS project creating accessible research summaries for practitioners. Previously, Dr. Andringa led Project MIND studying bilingual daycare effects and contributed to the Stilis project on listening proficiency. As General Editor of the Dutch Journal of Applied Linguistics (DuJAL), Dr. Andringa promotes open science principles in language research. Recent publications demonstrate a focus on addressing sampling biases in SLA research, open access publishing ethics, and practical applications of language acquisition research for educational settings. Academic Director, Institute for Dutch Language Education (INTT) Coordinator, Language Learning, Literacy and Multilingualism research group Coordinator, Master's program Dutch as a Second Language and Multilingualism General Editor, Dutch Journal of Applied Linguistics (DuJAL) Member, Mastery Team for Modern Foreign Languages Member, OASIS project team Member, IRIS database advisory group Dr. Andringa supervises PhD candidates including Kyra Hanekamp and Darlene Keydeniers, particularly in research related to bilingual daycare environments and language development. The research program has received funding from the Dutch ministry of Social Affairs for Project MIND and continues to secure support for ongoing projects examining language learning mechanisms. Dr. Andringa leads the Language Learning, Literacy, and Multilingualism research group which investigates language and literacy acquisition across the lifespan, with emphasis on how language skills are learned, maintained, and used in educational contexts. The group meets weekly to discuss projects, plans, funding opportunities, and research topics while promoting collaboration, methodological innovation, and open science principles.
Marie-Colette van Lieshout is a Professor of Spatial Stochastics at the Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, and a Scientific Staff Member in the Stochastics group at Centrum Wiskunde & Informatica (CWI), Amsterdam. She has been active in research since 1997 and is a leading expert in stochastic geometry, spatial statistics, and image analysis. Her educational and professional background includes positions at the University of Warwick and the Free University Amsterdam. She is currently engaged in advanced research on point processes, random fields, and tessellation models, with applications in seismic hazard, fire risk, and machine learning. Her research interests include: Stochastic Geometry Spatial Statistics Image Analysis Point Process Modeling Seismic Risk Assessment Machine Learning for Spatial Data Her recent publications (2023–2025) focus on spatial intensity estimation, marked point processes, and data-driven risk modeling, showing a strong integration of classical spatial statistics with modern computational and machine learning techniques. Key themes include adaptive kernel smoothing, infill asymptotics, and applications in environmental and public safety domains. She has received significant recognition, including: Elected Fellow, International Statistical Institute (ISI) She has been awarded multiple research grants from NWO and other agencies, including the KLEIN grant for fire risk management and the DeepNL grant for seismicity prediction in Groningen. She has supervised or collaborated with researchers such as C. Lu, Z. Baki, and R. Markwitz. She is also active in academic service, serving on editorial boards (e.g., Methodology and Computing in Applied Probability), advisory boards (InHolland University), and councils of learned societies (Bernoulli Society, KWG). She leads and participates in research clusters such as STAR and contributes to outreach and education through courses and public lectures on earthquake modeling and spatial statistics.
Valentijn M.T. de Jong is an Assistant Professor at Utrecht University, specializing in methodological advancements in biostatistics and epidemiology. His research focuses on causal inference, missing data analysis, and meta-analytical techniques in medical studies. Research Trends: Recent publications highlight his expertise in statistical methods for handling missing data (e.g., Heckman selection models), causal inference in individual-participant data meta-analyses, and enhancing prediction model discrimination in healthcare research. His work spans disciplines like epidemiology, biostatistics, and health data science.
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.
