جست‌وجوی استاد و پژوهشگر

• Database Systems
• Data Mining
• Bayesian Filtering
• +۵ مورد دیگر

Robert Brijder is an Assistant Professor in the Faculty of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), where he is a member of the Database Group led by Prof. George Fletcher. His office is located in MetaForum 7.091, and he can be contacted at r.brijder@tue.nl. Dr. Brijder obtained his MSc and PhD degrees in Computer Science at Leiden University. His academic journey continued with postdoctoral positions first in the Data Mining group at Leiden University, followed by a postdoc in the Databases and Theoretical Computer Science group at Hasselt University in Belgium. Prior to joining TU/e, he spent three years working at Flanders Make, a research and technology organization. His research focuses on applying database systems expertise to engineering challenges, particularly in structural health monitoring for offshore infrastructure. Dr. Brijder specializes in Bayesian filtering approaches, including various Kalman filtering techniques, to develop algorithms for corrosion detection and prognosis. His work aims to replace costly manual inspections with remote monitoring solutions that can predict maintenance needs and extend infrastructure lifespan through data-driven decision making. Dr. Brijder's recent publication in Wind journal (2023) demonstrates his innovative approach to engineering challenges through computer science methodologies. The research compared various corrosion prognosis algorithms and found that a method based on the Pourbaix corrosion model using unscented Kalman filtering outperformed alternatives based on linear and bimodal corrosion models. Funded by European Union's Horizon 2020 program (WATEREYE project, grant agreement No 851207) Addresses Sustainable Development Goals related to industry, innovation, and infrastructure Uses CC BY licensed research outputs for maximum accessibility At TU/e, Dr. Brijder contributes to academic education by teaching courses in Knowledge Engineering and Datamodeling and Databases. His position within the Database Group places him at the intersection of theoretical computer science and practical engineering applications, providing students with opportunities to work on real-world problems using cutting-edge database technologies.

Remco Renkenمشاهده پروفایل

پژوهشگر

• Neuroimaging
• Functional MRI
• Parkinson's Disease
• +۳ مورد دیگر

Dr. Remco Renken is a neuroimaging researcher at the University of Groningen's Faculty of Medical Sciences , specializing in advanced MRI and PET scan analysis. His work bridges clinical neuroscience and computational methods , with a focus on movement disorders, visual processing, and neurodegenerative diseases. Department of Radiology and Nuclear Medicine (UMCG) Clinical Cognitive Neuropsychiatry Research Program (CCNP) Perceptual and Cognitive Neuroscience (PCN) group Research Interests : Using fMRI , PET , and machine learning to study brain connectivity , visual cortex plasticity , and neurodegenerative patterns in Parkinson’s, multiple sclerosis, and glaucoma. His innovations include algorithms for 3D motion perception tracking and neuroimaging harmonization . Notable Contributions : Co-developed IRMA (Machine learning harmonization for multicenter PET scans) Co-inventor of a 3D motion perception evaluation system Co-supervisor for PhD research on auditory cortex responses and schizophrenia hallucinations Collaborations span neurology, psychiatry, and biomedical engineering, with datasets shared via Mendeley and DataverseNL.

• Artificial Intelligence
• Logic
• Human-centered Artificial Intelligence
• +۲ مورد دیگر

Dr. Natasha Alechina is an Associate Professor at Utrecht University's Faculty of Science , specializing in the Intelligent Systems subdomain. Her research focuses on multi-agent systems, formal verification, and logic-based AI, with particular emphasis on resource constraints and human-centered applications. Research Themes : Specification and verification of multi-agent systems Resource-bounded reasoning in coalitions Temporal and probabilistic logic for AI Causal modeling in autonomous agents Article Trends show consistent contributions to multi-agent systems , formal methods , and responsible AI . Her 2023-2025 work explores causal dependencies, intention progression under uncertainty, and ethical frameworks for group responsibility. Contact : n.a.alechina@uu.nl at Buys Ballot Building, Utrecht.

Raquel Garrido Alhamaمشاهده پروفایل

استادیار

• Computational modelling of language learning
• Language acquisition
• Language evolution
• +۷ مورد دیگر

Dr Raquel Garrido Alhama is an Assistant Professor of Artificial Intelligence for text and human interaction at the Institute for Logic, Language and Computation (ILLC) , University of Amsterdam. She also co-directs the /k/omputation and Language Lab and previously served as Assistant Professor at the Cognitive Science and Artificial Intelligence department of Tilburg University. She earned her PhD from the ILLC under the supervision of Jelle Zuidema, Remko Scha and Carel Ten Cate, with a dissertation entitled Computational Modelling of Artificial Language Learning . Prior post-doctoral positions include the Max Planck Institute for Psycholinguistics and the Basque Center on Cognition, Brain and Language. Research Interests: Computational modelling of language learning and evolution Language acquisition, word learning and sentence processing Neural-symbolic integration and connectionist architectures Visual and auditory word recognition, syntactic parsing and part-of-speech tagging In recent years her work has converged on validating developmental milestones—such as the onset of productive determiner–noun combinations—using large-scale child corpora and neural models. She also investigates how multi-agent referential games can lead to emergent compositional languages, moving from image to richer graph representations. Scientific Awards: Best Article Award (2019) from Psychonomic Bulletin & Review for the review on computational models of rule learning Best Poster Award (2015) at the International Conference on Cognitive Modeling Teaching & Supervision: Dr Alhama has designed and lectured courses on advanced programming for cognitive science, computational linguistics, linear algebra and language acquisition at Tilburg University, University of Amsterdam, Radboud University and the University of the Basque Country. She currently supervises eleven MSc and BSc students in AI, Logic and Cognitive Science. Labs & Teams: She is the founding co-director of the /k/omputation and Language Lab together with Phong Le, where they use Language Emergence frameworks to explore the cognitive prerequisites for displacement and compositionality in human language.

Ronald de Haanمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Parameterized Complexity Theory
• Artificial Intelligence
• +۸ مورد دیگر

Ronald de Haan is an Assistant Professor at the Institute for Logic, Language & Computation (ILLC) , University of Amsterdam, with primary affiliation in Theoretical Computer Science (TCS) and secondary affiliation in Mathematical & Computational Logic (MCL) . Since December 2019, he has held this position, following a postdoctoral role at the same institution from 2017 to 2019. He completed his PhD at the Algorithms and Complexity Group at Technische Universität Wien in 2016. Education: PhD in Computer Science, Technische Universität Wien (2016) MSc in Computational Logic, European Master's Program in Computational Logic (2010–2012) BSc in Cognitive Artificial Intelligence & BA in Linguistics, Utrecht University (2007–2010) Research Interests: His work lies at the intersection of theoretical computer science and artificial intelligence , with a strong emphasis on parameterized complexity theory . He explores the computational complexity of problems in AI, knowledge representation & reasoning, and computational logic. Specific areas include the Polynomial Hierarchy, subexponential-time complexity, the Exponential Time Hypothesis, and parameterized compilability. Scientific Awards: E.W. Beth Dissertation Prize 2017 for his PhD thesis "Parameterized Complexity in the Polynomial Hierarchy" Shortlisted for the Heinz Zemanek Prize 2018 Nominated for the GI-Dissertationspreis 2016 by the German Informatics Society Teaching & Supervision: He has taught a wide range of courses at the University of Amsterdam, including Computational Complexity , Knowledge Representation and Reasoning , and Recursion Theory for MSc Logic and MSc AI programs. He also supervises student research projects and theses, offering topics in ASP, complexity theory, and logic programming. Academic Service: He has served on the program committees of top-tier AI and logic conferences such as AAAI, IJCAI, KR, ECAI, and AAMAS, and co-organized events like PhDs in Logic VII.

• Neuroimaging
• Computational Cognitive Science
• Normative Modeling
• +۴ مورد دیگر

Richard Dinga, PhD is a Researcher at Tilburg University's Tilburg School of Humanities and Digital Sciences, specializing in the Computational Cognitive Science department. His work focuses on applying advanced computational methods to neuroimaging data to understand brain structure and function in both healthy and clinical populations. Dr. Dinga's primary research interests lie at the intersection of computational neuroscience and clinical psychiatry. His work prominently features normative modeling approaches to analyze neuroimaging data, with particular emphasis on cortical thickness, brain development, and aging patterns. He has made significant contributions to understanding first-episode psychosis through advanced neuroimaging techniques and has developed methodologies to address site variation in multi-center neuroimaging studies. Analysis of Dr. Dinga's publication record reveals a strong focus on normative modeling approaches applied to neuroimaging data, with particular emphasis on psychosis research and brain development/aging trajectories. His work consistently integrates hierarchical Bayesian methods with neuroimaging data to address clinical questions, particularly in the domain of psychosis. The publications demonstrate an evolving trajectory from methodological development (addressing site variation in neuroimaging) to clinical applications (particularly in first-episode psychosis). Dr. Dinga's research has garnered significant attention in the scientific community, with several publications accumulating notable citation counts. His 2022 paper in Elife on charting brain growth and aging has received over 100 citations, indicating substantial impact in the field. His work has been picked up by multiple news outlets and has generated discussion across various academic social media platforms. As a researcher in Computational Cognitive Science, Dr. Dinga collaborates extensively with international research teams across multiple institutions. His work involves large-scale neuroimaging datasets and requires sophisticated computational approaches to extract meaningful patterns related to brain structure and function. While specific advising relationships aren't detailed in the available information, his position suggests involvement in guiding junior researchers within his field of expertise.

Jelle Aalbersمشاهده پروفایل

استادیار

• Dark Matter
• Particle Physics
• Astrophysics
• +۵ مورد دیگر

Dr. Jelle Aalbers is an Assistant Professor at the Faculty of Science and Engineering , University of Groningen, affiliated with the Van Swinderen Institute for Particle Physics and Gravitation and the Dark Matter research group. His work focuses on Weakly Interacting Massive Particles (WIMPs) , Neutrino Physics , and Gravitational Lensing . Research Interests: He explores Dark Matter detection via Liquid Xenon Detectors , Neutrinoless Double Beta Decay , and Low-Energy Particle Interactions . His contributions include Signal Reconstruction and Background Modeling in experiments like XENONnT and XLZD. Recent Publications: His 2025 work in Physical Review Letters and European Physical Journal C highlights advancements in Neutrino Fog Analysis , Neutron Veto Systems , and Ionization Signal Discrimination . Earlier studies (2023–2024) address Gravitational Lensing Inference and Dark Matter Constraints .

Aidan Lyonمشاهده پروفایل

پژوهشگر

• Philosophy
• Cognitive Psychology
• Psychedelic Studies
• +۵ مورد دیگر

Aidan Lyon is a philosophical researcher at Leiden University's Cognitive Psychology unit and head of Research and Development for the Amsterdam Psychedelic Research Association. He bridges science and wisdom traditions through his work on meditation, psychedelics, and decision-making. His research focuses on uncertainty calibration , entropy in cognition , and non-pharmacological psychedelic methods like meditation and prānāyāma. He leads the Templeton Foundation-funded Mystical Entropy project (2023-2026) to systematize mystical experiences through information theory. Key publications include Psychedelic Experience: Revealing the Mind (OUP 2023) and articles in the same year about entropy-aesthetic connections. His work connects Bayesian epistemology with śūnyatā (Buddhist emptiness) and Platonic aporia . Scientific awards: Templeton Foundation grant for Mystical Entropy project (2023-2026) Academic affiliations: Leiden University (Cognitive Psychology unit) Munich Center for Mathematical Philosophy Amsterdam Psychedelic Research Association (head of R&D)

Erik-Jan van Kesterenمشاهده پروفایل

استادیار

• Data Science
• Computational Social Science
• Bayesian Statistics
• +۷ مورد دیگر

Dr. Erik-Jan van Kesteren is an Assistant Professor in the Methodology and Statistics department at Utrecht University , where he leads the ODISSEI Social Data Science (SoDa) team. His work bridges statistical methodology with computational social science, with a focus on synthetic data, Bayesian inference, and open research infrastructure. Current research initiatives include: Developing privacy-preserving synthetic data frameworks Improving computational approaches to structural equation modeling Addressing geospatial sampling bias in volunteer-collected datasets Exploring text-based personality computing and language association tools He maintains the department's compute server infrastructure and contributes to open-source projects like ArtScraper and osmenrich. Contact via e.vankesteren1@uu.nl or erikjanvankesteren@pm.me

• Bayesian Statistics
• Artificial Intelligence
• Natural Language Processing
• +۷ مورد دیگر

Rens van de Schoot is a full professor of Collaborative Methods in AI and Data Science at Utrecht University in the Netherlands and serves as an extra-ordinary professor at North-West University in South Africa. He directs the AI-LAB on AI-aided Knowledge Discovery and coordinates the open-source ASReview project (Active learning for systematic text reviewing), which has become a significant tool for researchers conducting systematic reviews across various disciplines. His research spans multiple domains with a strong focus on Bayesian statistics, artificial intelligence applications in systematic reviews, and psychological methodology. Van de Schoot's work bridges technical AI development with practical applications in social sciences and healthcare research, particularly in PTSD and mental health studies. His methodological contributions include significant work on measurement invariance, small sample size solutions, and the development of the WAMBS checklist for Bayesian statistics. The analysis of his recent publications reveals a clear trajectory toward increasingly sophisticated AI applications in systematic reviewing, with a strong emphasis on Bayesian methodology. His work demonstrates how machine learning can enhance traditional systematic review processes while maintaining methodological rigor. The interdisciplinary nature of his research connects statistics, computer science, psychology, and healthcare research in innovative ways. APA award for best dissertation of division 5 Recipient of prestigious VENI and VIDI research grants from the Netherlands Organization for Scientific Research Member of the Young Academy (Jonge Akademie) of the Royal Netherlands Academy of Arts and Sciences (KNAW) Elected member of the Society of Multivariate Experimental Psychology (SMEP) Van de Schoot has supervised numerous PhD candidates, as evidenced by his research on PhD delays and academic careers. His research program has been supported by multiple competitive grants, including his VENI project on integrating trauma-related background knowledge into statistical models and his VIDI project on expert knowledge and limited data. His work bridges theoretical methodological advances with practical applications across multiple domains. As director of the AI-LAB, he leads a team developing cutting-edge AI tools for knowledge discovery. His coordination of the ASReview project has created an international community of researchers applying active learning to systematic reviewing. He's also actively involved with the Utrecht Platform for Applied Data Science, fostering interdisciplinary collaboration on data-intensive research projects.

• Applied Data Science
• Network Analysis
• Longitudinal Data Analysis
• +۶ مورد دیگر

Dr. Mahdi Shafiee Kamalabad is an Assistant Professor in the Department of Methodology and Statistics at Utrecht University's Faculty of Social and Behavioural Sciences. His research focuses on developing advanced statistical and machine learning methods for complex data analysis, particularly in social and behavioral sciences, life sciences, and bioinformatics. Applied Data Science Network Analysis Bayesian Statistics Longitudinal Data Analysis He specializes in Dynamic Bayesian Network Models, Relational Event Models, and Change Point Detection algorithms. His work spans interdisciplinary collaborations, combining educational psychology, applied linguistics, and data science to improve understanding of multilingual classroom interactions and epidemic prediction models. He has contributed to R software packages like remify, remstats, and remstimate for relational event history data analysis. Notable projects include "Better Together: A Social Network Analysis of Multilingual Interactions in the Classroom" (2022) and methodological developments for malaria dynamics analysis in Cameroon. His teaching includes Data Wrangling and Data Analysis courses. Funding sources include Utrecht University's Faculty of Social and Behavioural Sciences.

Bella Struminskayaمشاهده پروفایل

دانشیار

• Survey Methodology
• Smartphone Surveys
• Data Donation
• +۶ مورد دیگر

Bella Struminskaya is an Associate Professor at Utrecht University’s Department of Methodology & Statistics, Faculty of Social and Behavioural Sciences. She is also an affiliated researcher at Statistics Netherlands and previously held a senior researcher role at GESIS - Leibniz Institute for the Social Sciences. Her work bridges survey methodology with digital data collection techniques, focusing on smartphone sensors and passive data donation. Education: PhD in Survey Methodology, Utrecht University M.A. in Sociology, University of Mannheim B.A. in Sociology, Novosibirsk State University Her research explores innovative data collection methods, including smartphone surveys, mixed-mode designs, and ethical frameworks for data donation. She investigates how mobile technologies can reduce survey burden while addressing nonresponse bias and measurement errors. Recent work emphasizes integrating digital trace data with traditional surveys to enhance accuracy and scope. Key article trends highlight applications of smartphone sensors in health studies, GDPR compliance for data access, and methodological advancements in smart surveys. Her contributions span technical tools like the Port software and theoretical insights on panel conditioning and ethical data augmentation. Scientific Awards: GOR Thesis Award 2015 She serves as a board member for the German Society for Online Research, Program Chair for the General Online Research Conference (GOR), and holds advisory roles in organizations like the European Social Survey, SHARE ERIC, and ODISSEI. She is an associate editor for multiple journals, including the Journal of Survey Statistics and Methodology and Survey Research Methods.

Mauro Salazarمشاهده پروفایل

استادیار

• Optimization Models
• Systems and Control
• Cyber-Socio-Technical Systems
• +۷ مورد دیگر

Mauro Salazar is an Assistant Professor in the Control Systems Technology section of the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e), with co-affiliations at the Eindhoven AI Systems Institute (EAISI) in both Health and Mobility domains. He leads his own research group focused on optimization models and methods for systems and control. His educational background includes: B.Sc. and M.Sc. in Mechanical Engineering from ETH Zürich (2012, 2015) Master thesis conducted at EPFL's Automatic Control Lab Ph.D. in Mechanical Engineering from ETH Zürich (2019) in collaboration with Ferrari Formula 1 Postdoctoral Scholar at Stanford University's Autonomous Systems Lab (2019-2020) Salazar's research centers on optimization models for multi-scale cyber-socio-technical systems with applications spanning mobility systems, pandemic response, and material design. His work bridges theoretical optimization with practical implementation, particularly in sustainable mobility solutions where he develops methods for optimal design and operation of transportation systems from single vehicles to entire networks. He investigates mesoscopic user behavior modeling and designs incentive schemes to align individual and system-level objectives in transportation networks. His recent publications (2025) demonstrate a strong focus on sustainable mobility systems, with recurring themes in electric vehicle fleet management, battery degradation modeling, ride-pooling algorithms, and energy management for transportation electrification. His work spans multiple disciplines including automotive engineering, control systems, energy management, and public health, characterized by rigorous mathematical optimization approaches applied to real-world challenges. His scientific recognitions include: Two ETH Medals (for Master's and PhD theses) Best Student Paper awards at ITS 2018 and ECC 2022 Best Teacher Award (2021) Nomination for TU/e Young Researcher Award (2022) Salazar actively advises research projects and secured funding through the Dutch Research Council's NEON project (Crossover research program, Grant 17628). His teaching portfolio includes Optimal Control and Reinforcement Learning, Engineering Optimization, and Advanced Full-Electric and Hybrid Powertrain Design. He leads the 'Group Salazar' within the Control Systems Technology section and contributes to EAISI Health and Mobility initiatives.

Luis Lima Valenteمشاهده پروفایل

عضو هیئت علمی

• Evolutionary Biology
• Biodiversity
• Island Biogeography
• +۴ مورد دیگر

Luis Lima Valente is an Affiliate Associate Professor at the University of Groningen , affiliated with the Faculty of Science and Engineering and the Theoretical and Evolutionary Community Ecology group led by Rampal Etienne. He is also a Senior Researcher at the Naturalis Biodiversity Center in Leiden, Netherlands. His work bridges theoretical and empirical approaches to study biodiversity patterns in island and continental hotspots. PhD in Evolutionary Biology (CSIC - Royal Botanic Garden Madrid) MSc in Ecology, Evolution, Conservation (Imperial College London) BSc in Biology (University College London) Valente's research focuses on the origins of biodiversity in geographical contexts. He combines fieldwork, phylogenetics, genomics, and computational models to unravel diversification dynamics over evolutionary timescales. His studies span plants, birds, and mammals, with particular emphasis on island ecosystems and human-driven extinctions. His recent publications highlight methodological advancements in island biogeography through tools like DAISIEprep and theoretical insights into trait-dependent diversification rates. Key themes include adaptive radiation, dispersal permeability, and climate-driven speciation patterns in mountain systems. Projects such as Untangling species diversification on islands: The role of traits (2021–2025) demonstrate his leadership in collaborative research. He is frequently involved in academic presentations and public engagement, notably regarding Madagascar's biodiversity crisis.

Hedderik van Rijnمشاهده پروفایل

استاد

• Temporal Cognition
• Interval Timing
• Cognitive Control
• +۵ مورد دیگر

Hedderik van Rijn is a Professor at the Department of Psychology, University of Groningen, and chair of "Cognitive Science and Neurosciences." His research focuses on temporal cognition, exploring how time influences cognitive processing, memory, and decision-making through behavioral, neuroimaging (fMRI, EEG, MEG), and computational modeling approaches. He develops methods like the adaptive learning system SlimStampen / RuggedLearning , which personalizes fact-learning using cognitive models and is used by 800,000+ students in the Netherlands. University: University of Groningen Department: Department of Psychology Academic Rank: Professor Van Rijn's research spans computational models of interval timing, cognitive control mechanisms, and applications in education and public health. He integrates symbolic process models (ACT-R) with neurobiological data, particularly in Drosophila studies, to uncover genetic bases of temporal processing. His work on time perception has theoretical and practical implications, from understanding neural substrates to real-world applications in skill training and language processing. His recent publications highlight interdisciplinary collaborations in temporal cognition, memory dynamics, and educational technology. Key themes include the interplay between time perception and cognitive load, the development of adaptive algorithms for learning, and the neural correlates of timing and decision-making. His lab's focus on deconvolving pupillary responses and modeling multi-tasking scenarios underscores methodological innovation. Scientific Awards: VICI grant (NWO Netherlands Organization for Scientific Research) Van Rijn has supervised numerous PhD students whose work spans pronoun processing, interval timing, Bayesian optimization of learning systems, and clinical applications of timing research. He collaborates with institutions like Noordhoff Publishers and the University Medical Center Groningen, and is a founding director of SlimStampen B.V., a spin-off commercializing adaptive learning principles. His grants include NWO, Horizon 2020, and John Templeton Foundation support.