جست‌وجوی استاد و پژوهشگر

• Geotechnical Engineering
• Soil Mechanics
• Material Point Method (MPM)
• +۷ مورد دیگر

Michael Hicks is a Professor at Delft University of Technology (TU Delft) in the Civil Engineering & Geosciences school, specializing in Geo-engineering . His research focuses on geotechnical risk assessment, soil variability, and advanced numerical methods like the Material Point Method (MPM) to model slope failures, earthquake simulations, and soil-structure interactions. Editorial board member for Géotechnique , Georisk , and Computers and Geotechnics (2019) Keynote speaker at workshops on slope failure modeling (2018) Active in public engagement about flood risk management (2017–2018) His work addresses challenges in soil heterogeneity, dynamic boundary conditions, and reliability-based design for infrastructure. Notable contributions include developing probabilistic MPM frameworks and machine learning tools for CPT data interpretation, with applications in earthquake resilience and radioactive waste repository safety. Recent publications highlight innovations in 3D slope stability analysis, nonlocal soil deformation modeling, and thermomechanical interface behavior under cyclic loading. Collaborations span academia and industry, focusing on improving numerical accuracy and understanding failure mechanisms in geotechnical systems.

Karl J. Fristonمشاهده پروفایل

استاد

• Neuroscience
• Radiology and Medical Imaging
• Computational Neuroscience
• +۴ مورد دیگر

Karl J. Friston is Professor of Imaging Neuroscience and Wellcome Principal Research Fellow at the Faculty of Brain Science, University College London. He serves as Scientific Director of the Wellcome Trust Centre for Neuroimaging and has been a leading figure in computational neuroscience for decades. Friston previously served as President of the international Organization of Human Brain Mapping in 2000 and has received numerous prestigious awards for his groundbreaking work. Professor Friston's research centers on theoretical neuroscience with a focus on the free energy principle , active inference , and predictive coding frameworks. His work spans multiple disciplines including neuroimaging methodology , computational psychiatry , and cognitive neuroscience . Friston has developed statistical approaches like Statistical Parametric Mapping (SPM) that have become standard tools in brain imaging research worldwide. His theoretical contributions provide unifying frameworks for understanding perception, action, and consciousness from a computational perspective. Analysis of Friston's recent publications reveals continued innovation in applying mathematical frameworks to complex neuroscience questions. His work increasingly bridges theoretical neuroscience with clinical applications in psychiatry and expands into artificial intelligence, exploring how brain principles can inform machine learning. The articles demonstrate consistent development of the free energy principle across diverse domains from psychedelic drug action to climate change impacts on brain health. Minerva Golden Brain Award (2003) Fellow of the Royal Society (2006) Medal, Collège de France (2008) Honorary Doctorate from the University of York (2011) Fellow of the Society of Biology (2012) As Scientific Director of the Wellcome Trust Centre for Neuroimaging, Friston leads a major research program that has secured substantial funding from the Wellcome Trust and other major research bodies. His theoretical frameworks have generated numerous research projects worldwide, influencing both basic neuroscience and clinical applications. Friston's work has been particularly influential in precision psychiatry, where computational approaches are being applied to understand and treat mental disorders through frameworks like the 'Bayesian Dysconnections' model. Friston is based at the Wellcome Trust Centre for Neuroimaging at University College London, a leading institution for brain imaging research. His work involves extensive collaborations across multiple disciplines and institutions, bringing together neuroscientists, physicists, mathematicians, and clinicians to advance our understanding of brain function through advanced imaging and computational techniques.

Mauricio Garnier Villarrealمشاهده پروفایل

استادیار

• Latent Variable Models
• Bayesian Statistics
• Social Inequality
• +۳ مورد دیگر

Dr. Mauricio Garnier Villarreal is an Assistant Professor at the Faculty of Social Sciences, Department of Sociology, and a member of the Social Inequality and the Life Course (SILC) research group at Vrije Universiteit Amsterdam. His work focuses on developing and testing data analysis methods, particularly latent variable models and Bayesian inference, with applications in sociology, education, nursing, and psychology. He emphasizes open science practices, contributing to R packages and sharing materials on GitHub and OSF. Research interests include methodological advancements in latent variable models (SEM, IRT, mixture models), Bayesian statistics, and their application to social inequality, especially intersectionality theory. Collaborations span aging, special education, and health sciences. He leads or participates in projects like DYNANSE (ERC grant) and COMBINE, addressing non-standard employment dynamics and latent variable methodologies. Recent articles explore measurement error impacts on clustering, hidden Markov models for employment mobility, and Bayesian SEM model evaluation. His work bridges methodological innovation with applied research to address societal issues.

T.J. Heimovaaraمشاهده پروفایل

استاد

• Soil Science
• Landfill Engineering
• Hydrogeology
• +۴ مورد دیگر

Prof. T.J. Heimovaara serves as a full Professor in Resource Engineering within the Civil Engineering & Geosciences faculty at Delft University of Technology. His research bridges geotechnical engineering and environmental systems with focus on practical landfill and soil management solutions. His primary research interests span soil science , landfill engineering , and hydrogeology , particularly investigating organic matter dynamics, permeability characteristics, and precipitation impacts on waste bodies. Recent work integrates advanced modeling techniques like particle filters and stochastic frameworks to quantify landfill emissions and erosion risks. Analysis of his 2025 publications reveals strong emphasis on environmental monitoring (remote sensing for cyanobacterial blooms), waste containment (low-permeability barriers against erosion), and predictive modeling of landfill behavior using Bayesian methods. His fingerprint shows dominant expertise in soil-landfill-water systems with organic matter as a recurring theme. He actively supervises graduate research with 9 documented supervised works and engages with public discourse through media appearances on topics like dike reinforcement using natural binders and subsurface engineering challenges.

• Bayesian Statistics
• Psychometrics
• Educational Psychology
• +۳ مورد دیگر

Dr. Jean-Paul G.J.A. Fox is an Associate Professor at the University of Twente with a focus on Bayesian covariance structure modeling, psychometrics, and data-based decision making. His research spans educational psychology, response time analysis, and statistical methods for nested data structures. Active researcher with recent publications (2024) in journals like Journal of Multivariate Analysis and Journal of Educational and Behavioral Statistics Recipient of the prestigious VIDI-beurs (2007) for methodological research Expert in modeling complex data patterns including negative associations and interval-censored survival data Research interests: Bayesian statistical modeling Joint modeling of response accuracy and timing Educational assessment frameworks Covariance structure analysis Data-based decision making systems Psychometric measurement theory Scientific contributions show trends in computational statistics for education and healthcare applications, with recent work on multi-way nested data structures and small-sample item response modeling. Scientific Awards: VIDI-beurs (2007) - Methodology award for innovative research Supervised work includes doctoral research on educational data analysis and Bayesian modeling applications. Collaborative network spans national and international institutions, particularly in psychometrics and educational statistics domains.

• Online Communities
• Artificial Intelligence
• Digital Economics
• +۴ مورد دیگر

Martin Quinn is an Assistant Professor at Rotterdam School of Management, Erasmus University Rotterdam, working in the Department of Technology and Operations Management. His research bridges information systems, economics, and digital platform studies with methodological expertise in causal inference and advanced econometrics. His research interests include: Online Communities and Knowledge Sharing Artificial Intelligence economics and societal impact Platform governance and digital regulation Consumer welfare in digital markets Payment systems and financial privacy Econometric methods including Difference-In-Differences and Bayesian analysis Dr. Quinn's recent work examines generative AI's impact on knowledge seeking behaviors, content moderation economics, and mobile internet welfare effects. His publications in top journals like Journal of Management Information Systems and The Economic Journal demonstrate both academic rigor and policy relevance. He actively contributes to academic discourse through conference organization, including sessions on Platform Governance and the Secondary Effects of AI Revolution at INFORMS. His role as European Commission expert on digital platforms highlights his policy influence. Dr. Quinn's research receives significant attention across academic and public spheres, with coverage in 15+ news outlets, references in Wikipedia, and discussion across social media platforms including X, Bluesky, and Reddit.

• Philosophy of Artificial Intelligence
• Cognitive Science
• Neuroscience
• +۳ مورد دیگر

Carlos Zednik is an Assistant Professor for Philosophy of Artificial Intelligence at Eindhoven University of Technology , affiliated with the Industrial Engineering and Innovation Sciences department. He leads the Eindhoven Center for Philosophy of AI and participates in the alignAI (ERC) and ROBUST AI (NWO) consortia. His work bridges philosophy with AI and neuroscience, focusing on explainable AI (XAI), mechanistic explanation, and cognitive modeling. Education: BSc in Computer Science and Philosophy, Cornell University MSc in Philosophy of Mind, University of Warwick PhD in Cognitive Science, Indiana University Bloomington Zednik’s research investigates philosophical questions about biological and artificial intelligence, emphasizing: Methodological principles in cognitive psychology and neuroscience Norms and best practices for XAI in machine learning Knowledge representation in transformer models and large neural networks His recent publications explore the integration of cognitive models into XAI, the role of Bayesian reverse-engineering in cognitive science, and the mechanistic explanation of network neuroscience. Zednik also contributes to international standardization efforts through ISO/IEC TS 6254 and DIN SPEC 92001 . Scientific Awards include fellowships from: DAAD Alexander-von-Humboldt Foundation StandICT Fellowship Zednik supervises PhD students Zeynep Kabadere , Michela Ghezzi , Céline Budding , Miriam Gorr , and Hannes Boelsen , while mentoring postdocs Manuel Barbosa de Oliveira and Philippe Verreault-Julien . His teaching spans philosophy of AI, ethics of machine learning, and decision theory, with innovation projects on generative AI in higher education.

• Geotechnical Engineering
• Soil-Structure Interaction
• Tunnel Engineering
• +۴ مورد دیگر

Dr. Anne-Catherine A.A.M. Dieudonné is an Assistant Professor in Geotechnical Field Testing and Monitoring at Delft University of Technology's Faculty of Civil Engineering and Geosciences. She holds a Ph.D. in Structural, Seismic, and Geotechnical Engineering from Politecnico di Milano, where she previously worked as a doctoral researcher and assistant professor until joining TU Delft in 2022. Her research focuses on experimental, numerical, and theoretical analysis of soil-structure interaction problems, including foundations, tunnels, embankments, and slope retaining systems. Key interests include developing simplified models for infrastructure design, improving sustainability and reliability of structures, and understanding time-dependent material responses. Her work spans geotechnical monitoring with fiber optics, constitutive modeling of geomaterials (e.g., cement-bentonite mixtures), and energy geotechnics. Notable contributions include studies on tunnel face stability, pile-supported embankments, and bio-cemented soils. Publications emphasize THM modeling for geothermal systems, fracture mechanics, and nuclear waste repository optimization. She collaborates internationally, contributing to advanced multiphysics approaches in geomechanics.

H.A.L. Kiersمشاهده پروفایل

استاد

• Statistics
• Data Analysis
• Multivariate Statistics
• +۳ مورد دیگر

Prof. Henk A.L. Kiers is a Professor in Statistics and Data Analysis at the University of Groningen's Faculty of Behavioural and Social Sciences, within the Department of Psychometrics & Statistics. His research focuses on advanced statistical methodologies, particularly in multivariate analysis, three-way component analysis, and psychometrics. He has contributed extensively to data analysis techniques for large datasets, including methods for uncovering underlying structures in complex data matrices. Key research interests include Bayesian analysis, statistical education, and the application of statistical methods in psychology and psychiatry. He has held editorial roles and received the Psychometrika Reviewer Award in 2017. His work bridges theoretical advancements with practical applications, such as improving classification methods and addressing challenges in missing data analysis. Prof. Kiers is actively involved in academic collaborations, including projects on data fusion and the development of statistical software tools like the R package ThreeWay. His contributions to the field have been recognized through numerous high-impact publications and methodological innovations.

Peter Grünwaldمشاهده پروفایل

استاد

• Machine Learning
• Statistics
• Safe Learning
• +۸ مورد دیگر

Peter Grünwald is a Full Professor of Statistical Learning at Leiden University's Mathematical Institute and heads the Machine Learning group at CWI (Centrum Wiskunde & Informatica) in Amsterdam. He serves as President of the Association for Computational Learning and has held editorial roles at Foundations and Trends in Machine Learning. His research bridges machine learning theory and statistical methodology. Research Interests Safe Learning: Ensuring robustness in statistical inference and machine learning Safe Probability: Developing probability frameworks for partial domain modeling Replicability Crisis Mitigation: Addressing statistical misinterpretations in applied sciences Learning Bounds: Quantifying data requirements for reliable conclusions MDL Principle: Advancing Minimum Description Length methodology PAC-Bayesian Methods: Bridging frequentist and Bayesian paradigms Scientific Awards Van Dantzig Prize (2010) - Highest Dutch award in statistics IMS Fellow - Recognition from the Institute of Mathematical Statistics Grants NWO TOP-1 Grant (2016) NWO VICI Grant (2010) NWO VIDI Grant (2005)

Rianne de Heideمشاهده پروفایل

پژوهشگر

• Bayesian Statistics
• Machine Learning
• Hypothesis Testing
• +۳ مورد دیگر

Rianne de Heide is a Researcher in the Machine Learning department at Centrum Wiskunde & Informatica (CWI) in Amsterdam, The Netherlands. Her work focuses on theoretical aspects of statistical learning, particularly in the areas of Bayesian inference and hypothesis testing. Dr. de Heide's research interests span several interconnected domains in statistical theory and machine learning: Bayesian statistics and inference Hypothesis testing and statistical validity E-values and anytime-valid testing Group invariance in statistical models Safe Bayesian learning under misspecification Multi-armed bandit problems Her recent publications demonstrate a strong focus on developing theoretically sound statistical methods that maintain validity under flexible conditions. A significant portion of her work explores 'safe testing' frameworks that remain valid regardless of when testing is stopped, addressing a long-standing challenge in statistical practice. Her research bridges theoretical statistics with practical applications in machine learning, with publications in top journals including the Annals of Statistics and the Journal of the Royal Statistical Society Series B. Dr. de Heide has received recognition for her work, including: Willem R. van Zwet Award (2022) Her research is supported by the project 'Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning,' funded by The Netherlands Organisation for Scientific Research (NWO). She collaborates extensively with Peter Grünwald, Wouter Koolen-Wijkstra, and other researchers in the Machine Learning group at CWI.

• Control Theory
• Agricultural Engineering
• Machine Learning
• +۳ مورد دیگر

Sjoerd Boersma is a Researcher at Biometris, Wageningen University & Research (WUR), specializing in the integration of advanced control systems with agricultural technology. His work focuses on developing predictive control models, observability analysis frameworks, and digital twin solutions to optimize greenhouse production, wind farm efficiency, and water resource management. His research spans Control Theory , Agricultural Engineering , and Machine Learning , with specific expertise in predictive control for systems with parametric uncertainty, sensor configuration optimization, and Bayesian neural methods for crop modeling. Current projects address autonomous greenhouse control using reinforcement learning and virtual crop modeling for tomato production. Recent publications reveal a strong trend toward cross-disciplinary solutions where digital twin technology bridges agricultural engineering and control systems. His work consistently targets sustainability challenges, from wind farm load reduction to precision water management and greenhouse climate optimization, demonstrating methodological innovation in applying control theory to biological systems. Boersma actively mentors PhD candidates, serving as co-promotor for van Laatum, B.'s research on autonomous greenhouse control systems. His current projects include the active Autonomous Production Control of Greenhouse Farming System with Reinforcement Learning and the recently completed Virtual Tomato Crops initiative funded by WUR. As a core member of the Biometris research group, he contributes to WUR's mission through collaborative projects like the VIOLA potato stress classification model and the digital twin framework for water management, emphasizing real-world implementation of control systems in agricultural environments.

Bart van Erpمشاهده پروفایل

پژوهشگر

• Bayesian Inference
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

Bart van Erp serves as a University Researcher within the Department of Electrical Engineering at Eindhoven University of Technology, actively contributing to the Signal Processing Systems (SPS) group and Bayesian Autonomous Intelligent Systems lab (BIASlab). He completed his PhD in June 2025 under Prof. Bert de Vries with dissertation work on automated Bayesian hearing aid design. His academic foundation includes: Bachelor of Science in Electrical Engineering, cum laude (2015-2018) Master of Science in Electrical Engineering, cum laude with honors (2018-2020), supported by ALSP scholarship Van Erp's research centers on Bayesian inference frameworks for real-world signal processing challenges, particularly in hearing aid personalization and autonomous systems. He pioneers message-passing algorithms on factor graphs to implement variational free energy minimization, enabling adaptive user preference learning and energy-efficient control systems. His work bridges theoretical probabilistic modeling with clinical applications in hearing impairment solutions. Analysis of his 2024-2025 publications reveals consistent focus on Bayesian neural networks and active inference methodologies, with increasing emphasis on practical implementations for assistive devices. Key trends include Dirichlet process applications for context-aware systems and depth estimation improvements in uncertainty-aware computer vision. Recognition includes: ALSP scholarship for Master's excellence As project member in the CoHear initiative (2017-2025), he contributed to collaborative hearing device design while securing open-source development through ReactiveMP.jl. Though newly minted as a PhD, he has not yet supervised graduate students but actively supports university education (0.3 FTE during studies). His dual role as Product Lead/Co-Founder at Lazy Dynamics BV since May 2023 demonstrates translational research impact. Within BIASlab, van Erp drives the integration of probabilistic programming with medical device innovation, maintaining strong industry partnerships in hearing technology through GN Hearing collaborations and open-source tooling.

• Public Health
• Epidemiology
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Hüseyin Küçükali is a Researcher at the Institute for Risk Assessment Sciences (IRAS) within the Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University. His work integrates epidemiology, artificial intelligence, and systems thinking to innovate non-communicable disease prevention in urban environments, with emphasis on exposome research and healthy aging initiatives. Education PhD in Public Health, Istanbul University - Cerrahpasa MD (Medicine), Istanbul University - Cerrahpasa Research Focus Dr. Küçükali's expertise spans causal inference , exposome analysis , and AI-driven health behavior modeling , particularly examining urban environmental impacts on cognitive decline and tobacco use patterns. His methodology combines accelerometry , GPS tracking , and molecular data to map mechanistic pathways in aging populations, while leveraging large language models for public health surveillance in digital spaces. Publication Trends His 15 most recent publications reveal three dominant threads: (1) urban exposome effects on cognitive health using multi-sensor data integration, (2) AI applications for tobacco control through social media content analysis, and (3) Bayesian modeling of socioeconomic disease patterns. These works consistently bridge environmental epidemiology, data science, and policy-relevant public health interventions across international contexts including Türkiye, UK, and Ireland. Professional Engagement He actively contributes to the International Human Exposome Network (IHEN) and STAGE (Stay Healthy Through Ageing) projects while leading the Utrecht Exposome Hub. Previous roles include postdoctoral research at Queen's University Belfast on urban green spaces (GroundsWell project) and health promotion leadership in Istanbul's municipal health administration. He also created educational resources like the Turkish-language Video Glossary of Public Health Concepts.

Max Hinneمشاهده پروفایل

استادیار

• Bayesian Statistics
• Machine Learning
• Neuroscience
• +۶ مورد دیگر

Max Hinne is an assistant professor at the Department of Artificial Intelligence, Radboud University, Nijmegen, The Netherlands, where he leads the Uncertainty in Complex Systems research group. His work bridges artificial intelligence, neuroscience, and statistics through advanced Bayesian methodologies. Dr. Hinne's research focuses on Bayesian modeling of brain networks using neuroimaging data. His primary interests include: Bayesian nonparametric models, particularly Gaussian processes Structural and functional brain connectivity analysis Predictive modeling of neural systems Causal inference frameworks Uncertainty quantification in complex systems Development of computational tools for neuroscience His approach emphasizes how probabilistic methods can address uncertainty in complex biological systems while providing interpretable models of brain function. Analysis of Dr. Hinne's publication trajectory reveals a consistent focus on Bayesian methods applied to increasingly diverse domains. Starting with foundational work in brain connectomics, his research has expanded to include applications in developmental psychology, medical genetics, and educational technology. His most recent work demonstrates sophisticated integration of nonparametric Bayesian methods with domain-specific challenges, particularly in handling uncertainty in complex, high-dimensional data across multiple scientific fields. Dr. Hinne actively mentors students and invites master's thesis projects focused on Bayesian nonparametric methods for neuroimaging data. He has developed several software tools including the Bayesian Connectomics Toolbox (BaCon), latent space modeling code, and GP CaKe for causal inference. His research group maintains strong connections with the Donders Institute for Brain, Cognition and Behaviour, facilitating interdisciplinary collaborations between statisticians, neuroscientists, and domain experts.