جست‌وجوی استاد و پژوهشگر

• Neuroscience
• Psychology
• Cognitive Psychology
• +۷ مورد دیگر

Johannes Fahrenfort is an Assistant Professor at the Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, with a focus on cognitive psychology and neuroscience. His research explores the neural mechanisms underlying perception, decision-making, and consciousness. Key Research Areas: Metacognition, Working Memory, Electroencephalography (EEG), Functional MRI, and the interplay between conscious and unconscious processing. Recent Projects: Templeton Grant on statistical frameworks for unconscious cognition, Advanced Human Capital Program grant on cognitive neuroscience, and PERISCOPE on visual search and peripheral vision. Research Trends: His recent articles investigate decision-making confidence, neural decoding of working memory, catecholamine effects on perception, statistical rigor in consciousness studies, and arousal-dependent cognitive performance. Keywords span Neuroscience , Psychology , and Neural Oscillations . Scientific Awards: A.F. Sandersprice (2007), Best Lecturer for the Research Master Psychology (2019), and Open Science Award FBMS (2024). Academic Contributions: Frequent lecturer in Cognitive Neuroscience, co-supervisor of PhD theses, and creator of open-access datasets and software tools for EEG analysis.

Hamdi Joudehمشاهده پروفایل

دانشیار

• Information Theory
• Communications
• Signal Processing
• +۳ مورد دیگر

Hamdi Joudeh is an Associate Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). He is affiliated with the Information and Communication Theory (ICT) Lab and the Signal Processing Systems (SPS) Group. His research focuses on information theory, communications, and signal processing, with applications in wireless networks and radar systems. He holds a Ph.D. in Electrical Engineering from Imperial College London and has held research positions at Technische Universität Berlin and Imperial College London. His research interests include quantum sensing, error exponents, MIMO systems, and channel coding. He leads projects such as the IT-JCAS (Information Theoretic Foundations of Joint Communication and Sensing) and ANTERRA (Beam Prediction for Fast-Moving LEO), addressing challenges in 5G/6G communication and radar technologies. He has received an ERC Starting Grant (2023) for his work on environment-scanning mobile networks. Education: Ph.D. in Electrical Engineering (Imperial College London), M.Sc. in Communications and Signal Processing (Imperial College London) Editorial Roles: Editorial board member of IEEE Transactions on Signal Processing , IEEE Communications Letters , and EURASIP Journal on Wireless Communications and Networking Labs/Teams: ICT Lab, SPS Group, and leads projects at TU/e’s Center for Wireless Technology Grants: ERC Starting Grant, TKI-HTSM/22.0547/TKI2212P11 RAIDAR, and others His recent work explores the intersection of communication and sensing, including quantum radar processing and robust beamforming techniques. He has published extensively on topics like error exponents, MIMO channel analysis, and interference management, with over 40 peer-reviewed articles.

• Statistical Genetics
• Graphical Models
• Genomics
• +۵ مورد دیگر

Pariya Behrouzi is an Assistant Professor in the Department of Mathematical and Statistical Methods - Biometris at Wageningen University & Research. She specializes in developing statistical methodologies with applications in genetics, genomics, and nutrition science. Her research focuses on graphical models, copula theory, and Bayesian approaches to analyze complex biological systems. Behrouzi leads or collaborates on projects such as SignAphy (plant signaling dynamics) and Sensing Age (epigenetic aging studies). She co-developed the netgwas R package for network-based genome-wide association studies. Her work bridges statistical theory with practical applications in agriculture, nutrition, and computational biology. Education: PhD in Statistics (as indicated by title) Key Research Tools: Copula graphical models, spatial statistics, and Bayesian networks Current Projects: Multi-omics network reconstruction, resilience in lettuce crops, and microbiota systems analysis Her recent publications explore topics like meat analogue texture optimization, telomere length dietary correlations, and plant disease resistance mechanisms. Behrouzi advises four PhD candidates and collaborates with international teams on interdisciplinary research.

• Explainable Artificial Intelligence
• Feature Selection
• Analytic Hierarchy Process
• +۳ مورد دیگر

Dr. Majeed Mohammadi is a Visiting Professor at the Faculty of Science, Department of Artificial Intelligence at Vrije Universiteit Amsterdam. He also holds visiting fellow roles at the Network Institute and Artificial Intelligence research groups within the same university. His research focuses on Explainable AI, feature selection, decision analysis, and sustainable urban mobility. Key methodologies include the Analytic Hierarchy Process, Quadratic Programming, and Game Theory applications. His recent work bridges transportation systems and AI through stakeholder engagement frameworks. He has published on causal explanation techniques (CAGE), high-order feature interaction detection, and fallacies in group decision-making processes. Collaborations span international teams addressing interdisciplinary challenges in AI explainability and decision science. His academic contributions include peer-reviewed articles in journals like Group Decision and Negotiation and conferences such as AAAI. Research outputs emphasize both theoretical advancements (e.g., Möbius representation algorithms) and practical applications in urban mobility and feature engineering.

Eoghan O'Neillمشاهده پروفایل

استادیار

• Econometrics
• Machine Learning in Economics
• Bayesian Statistics
• +۷ مورد دیگر

Eoghan O'Neill is an Assistant Professor in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. His research bridges econometrics, machine learning, and policy-oriented modeling, with applications in inflation forecasting, energy systems, and urban sustainability. His research interests lie at the intersection of quantitative methods and real-world economic challenges. He specializes in Bayesian statistical modeling , machine learning for economic forecasting , and system dynamics applied to urban building energy systems . His work often involves censored data models, auction design, and long-term sustainability projections, particularly in the context of China's urban development. The trend in his recent publications shows a dual focus: (1) advancing methodological econometrics using machine learning and Bayesian trees, and (2) applying these tools to pressing policy issues like inflation, renewable energy procurement, and embodied energy in buildings. His work spans disciplines including economics, statistics, environmental science, and public policy. Although no formal awards are listed in the provided text, his publications in high-impact journals such as Journal of Applied Econometrics and Energy Policy reflect strong scholarly recognition. His research has been cited multiple times and has drawn attention in policy-relevant domains. Eoghan O'Neill actively collaborates with researchers across institutions, particularly on sustainability and energy modeling projects. While no formal advising role is detailed in the text, his position as an assistant professor suggests involvement in graduate training. He has not disclosed any grant funding in the provided material, but his interdisciplinary work implies potential involvement in collaborative research initiatives. His research contributes to both theoretical econometrics and practical policy solutions, particularly in energy and urban systems. The integration of Bayesian inference with system dynamics and machine learning underscores a methodologically rigorous and application-driven approach.

• Econometrics
• Macroeconomic Forecasting
• Machine Learning in Economics
• +۷ مورد دیگر

Andreas Pick is an Associate Professor in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. His research focuses on econometric modeling, macroeconomic forecasting, and machine learning applications in economics. He is actively contributing to the field through publications in top-tier journals and co-authoring with leading scholars in econometrics. His research interests include Macroeconomic Forecasting , Machine Learning in Economics , Bayesian Econometrics , Vector Autoregressions , Markov Switching Models , and Panel Data Forecasting . These areas reflect his expertise in developing and applying advanced statistical methods to economic data. The recent articles show a consistent focus on improving forecasting accuracy using machine learning, handling structural breaks, and optimizing forecasts in nonlinear models. His work bridges econometric theory with practical macroeconomic applications, particularly in nowcasting GDP and multi-step forecasting using high-dimensional datasets. Andreas Pick has supervised at least one student, as indicated by the 'Supervised Work' entry. He has collaborated with researchers such as Timmermann, Boot, and de Winter. There is no mention of specific grants, but his open-access publications and shared datasets suggest active research funding and collaboration. He maintains a personal website at https://apick.eu where more information about his work can be found. A dataset related to his 2018 paper on Markov switching models is publicly available on Figshare, indicating transparency and reproducibility in his research.

Jason Roosمشاهده پروفایل

دانشیار

• Causal inference
• Bayesian econometrics
• Structural modeling
• +۱۰ مورد دیگر

Jason Roos is an Associate Professor of Marketing at the Rotterdam School of Management, Erasmus University. He holds a PhD in Business Administration (Marketing) from Duke University and a BA from the University of Washington. His research bridges marketing, econometrics, and behavioral science, focusing on causal inference, structural modeling, and digital consumer behavior. PhD in Business Administration (Marketing), 2012, Duke University BA in Business Administration, 2005, University of Washington His research interests include causal inference, Bayesian econometrics, experimental methods, internet and new media, entertainment goods, and digitization. He investigates how information, learning, and adaptation affect consumer choice, particularly in digital and entertainment contexts. His work develops advanced econometric methods to improve causal inference from experimental and observational data. His recent publications, appearing in journals like Marketing Science , Management Science , and Journal of Marketing Research , explore topics such as compliance promotions, internet news consumption, movie demand, and advertiser learning. The articles reflect a strong interdisciplinary trend, combining marketing theory with rigorous quantitative methods and digital data. He is a recipient of the INFORMS Society for Marketing Science (ISMS) Doctoral Dissertation Proposal Competition award. His research has practical implications for marketing strategy, digital platform design, and public policy. He supervises PhD students and contributes to the academic community through teaching and research mentorship. He maintains an active research agenda, with ongoing work on digitization of communities, fake news exposure, and experimental design. He is also engaged in public discourse on academic freedom and labor rights via social media.

• Medical Statistics
• Computer Science
• Artificial Intelligence
• +۶ مورد دیگر

Gerton Lunter is a Professor of Medical Statistics at the University of Groningen's Faculty of Medical Sciences, affiliated with the Department of Epidemiology. He holds a visiting professorship in Computational Biology and Artificial Intelligence at the Weatherall Institute of Molecular Medicine, University of Oxford. His expertise spans Bayesian statistics, statistical genetics, functional genomics, and machine learning applied to evolutionary biology and healthcare challenges. Education: PhD in Mathematics (Dynamical Systems), University of Groningen (1999) MSc Mathematics and Theoretical Physics (cum laude), University of Groningen (1994) Research Focus: Integrates computational methods with medical and genetic data to address complex biological problems. Key areas include: Statistical modeling of genetic variation Machine learning for healthcare diagnostics Evolutionary dynamics of genomic mutations Multi-morbidity and lifestyle epidemiology Recent Work Trends: Publications emphasize interdisciplinary approaches, combining genomic data analysis with clinical applications (e.g., cancer immunotherapy prediction, maternal hypertension patterns, and rare disease diagnostics). His work bridges computational innovation with real-world medical challenges. Affiliations & Roles: Current roles include group leadership at Genomics plc and former positions as a postdoctoral researcher at University of Oxford's Department of Statistics and Philips Research Laboratories. He has contributed to initiatives like the AIHACKCOVID challenge. Team & Infrastructure: Leads research groups focusing on computational biology and statistical learning, collaborating across disciplines to leverage large-scale genomic datasets.

Julia Engelmannمشاهده پروفایل

پژوهشگر

• Causal network modeling
• Metagenomics
• Marine microbial ecology
• +۵ مورد دیگر

Dr. Julia Engelmann is a Tenure-track Scientist in the Marine Microbiology and Biogeochemistry (MMB) department at the Royal Netherlands Institute for Sea Research (NIOZ) , specializing in causal network modeling, metagenomics, and bioinformatics. Her research focuses on understanding marine microbial interactions through high-throughput data analysis and computational models, particularly in the North Sea, Wadden Sea, and Antarctic waters, with implications for climate change studies. Education: PhD in Bioinformatics (2008) and Diploma in Biology (2004) from German universities. Academic Positions: Senior Scientist at NIOZ (2017-present), Assistant Professor (2013-2017), and Postdoctoral roles in computational diagnostics and oncology. Engelmann applies statistical methods to infer causal relationships between marine microorganisms from observational data, using Bayesian networks and conditional independence analysis. Her work bridges computational biology with experimental validation, aiming to design synthetic microbial consortia for applications like plastic degradation. She contributes to meta-barcoding and metagenomic analyses , developing pipelines like Cascabel for reproducible sequence data processing. Her recent publications examine methane dynamics, microbial lipid biosynthesis, and plastisphere diversity in coastal and polar systems. Scientific Awards NWO Women in Science Excel (WISE) tenure track award Engelmann's research integrates environmental factors (temperature, CO2) into microbial interaction models to predict climate change impacts, collaborating with international teams on Antarctic expeditions.

Thijs van de Laarمشاهده پروفایل

استادیار

• Bayesian Machine Learning
• Active Inference
• Probabilistic Programming
• +۳ مورد دیگر

Thijs van de Laar is an Assistant Professor at the BIASlab (Bayesian Intelligence and Autonomous Systems Laboratory) within the Department of Electrical Engineering at Eindhoven University of Technology. His research bridges advanced computational principles with real-world applications in uncertainty modeling. Education: PhD in Machine Learning (2019), Eindhoven University of Technology MSc in Natural Sciences (2010), Radboud University Nijmegen Research Focus: Bayesian machine learning, active inference, and probabilistic programming, with applications in autonomous agent control, hearing loss compensation, and efficient real-time decision-making systems. His work integrates insights from physics and neuroscience to develop novel computational frameworks. Software Contributions: Key contributor to ForneyLab.jl, a Julia toolbox for automated Bayesian inference through message passing on factor graphs. Also developed Herring.jl, a Poisson node extension for probabilistic modeling. Publications: His 15 most recent articles focus on variational message passing, active inference agents, epistemic value in graphical models, and factor graph-based algorithm design. These works span disciplines including artificial intelligence, control theory, and biomedical engineering.

Quentin Changeatمشاهده پروفایل

استادیار

• Exoplanets
• Atmospheric Modeling
• Telescope Observations
• +۳ مورد دیگر

Quentin Changeat is an Assistant Professor at the Kapteyn Astronomical Institute, University of Groningen, and an Honorary Research Fellow in the Department of Physics and Astronomy at University College London. His research bridges exoplanet atmospheric studies and computational astrophysics, with affiliations spanning multiple international institutions. Research Interests: Dr. Changeat focuses on the physics and chemistry of exoplanet atmospheres, leveraging data from the James Webb Space Telescope (JWST), Hubble Space Telescope (HST), and Spitzer. His work emphasizes atmospheric retrieval techniques, machine learning applications, and the development of open-source tools like TauREx for spectral analysis. He is actively involved in the ESA Ariel mission, which aims to survey 1000 exoplanets by 2029. Publication Trends: His 15 most recent articles (2019–2024) reveal a strong emphasis on JWST/HST data analysis, atmospheric retrievals of hot Jupiters, and population-level studies. Key themes include thermal inversions, phase-curve degeneracies, and the development of statistical frameworks for large-scale exoplanet characterization. Machine learning and interdisciplinary collaborations feature prominently in his methodology. Awards: Honorary Research Fellow, University College London Academic Contributions: He leads educational initiatives like the ARES Summer School and ORBYTS program, mentors PhD students in exoplanet research, and develops datasets for community use (e.g., Ariel Data Challenge). His lab focuses on atmospheric retrieval algorithms and collaborates with teams at UCL, Flatiron Institute, and ESA.

Andrew Robert Grayمشاهده پروفایل

دانشیار

• Biostatistics
• Epidemiology
• Public Health
• +۷ مورد دیگر

Andrew Robert Gray is a biostatistician and Associate Professor at the Centre for Biostatistics within the Division of Health Sciences , University of Otago. His career spans two decades, with prior experience in software metrics and machine learning. He focuses on enhancing biostatistical rigor in health research, collaborating across disciplines like pediatric obesity , respiratory epidemiology , and dentistry . University of Otago (2004–present) Department of Information Science (prior to 2004) His research integrates biostatistics , machine learning , and public health , emphasizing methodological innovations and interdisciplinary applications. Recent work explores epidemiological modeling , health systems , and statistical transparency . Over the past five years, Gray has contributed to studies in colorectal cancer screening , urology journal policies , and adolescent health , with methodological expertise in Bayesian estimation and predictive analytics . PeerJ Editor and Reviewer Co-author on 45+ publications Gray leads workshops on biostatistical collaboration and mentors researchers in health sciences and data analysis . His affiliations include the Biostatistics Centre , where he bridges computational methods with clinical research.

• Statistical Inference with Nonprobability Samples
• Survey Data Integration
• Non-response Analysis
• +۵ مورد دیگر

Dr. Camilla Salvatore is an Assistant Professor in Statistics at Utrecht University , specializing in statistical inference with nonprobability samples and survey data integration . She previously held roles as a lecturer in Computational Social Science at the University of Amsterdam and was a postdoctoral researcher under the Understanding Society Research Data Fellowship at Utrecht University. Education: PhD in Statistics (2021), University of Milano-Bicocca Visiting PhD (2019-2021), Utrecht University MSc in Economics and Global Markets (2018), University of Bergamo Research Interests focus on combining traditional survey data with digital trace sources (e.g., social media, web surveys) to develop efficient statistical frameworks. Her work addresses challenges in non-response analysis , Bayesian modeling , and composite indicator construction for socio-economic and corporate sustainability studies. Article Trends reveal expertise in Bayesian survey integration , nonprobability sampling , and text mining for CSR analysis . Recent publications emphasize small area estimation , Twitter data quality , and hybrid data methodologies to reduce survey costs while enhancing analytical accuracy. Scientific Awards 2024 Honorable Mention for Best Ph.D. Thesis in Applied Statistics 2023 Understanding Society Fellowship 2022 VADISTAT Award for CSR Communication Analysis Collaborations include research groups at Utrecht University, University of Milano-Bicocca, and international institutions like Duke University and the Institute for Employment Research (IAB). She serves as Associate Editor for the Journal of Survey Statistics and Methodology .

• Dynamic Modeling
• Bayesian Statistics
• Multilevel Analysis
• +۵ مورد دیگر

Dr. Noémi Schuurman is an Assistant Professor in the Department of Methodology and Statistics at Utrecht University's Faculty of Social Sciences. Specializing in Bayesian multilevel time series analysis and intensive longitudinal data, she leads the Dynamic Modeling Lab under Ellen Hamaker's supervision. Her work bridges psychological theory with advanced statistical techniques, particularly through N=1 modeling and person-specific reliability estimation. Co-founder of dynamic VAR networks in psychology Developer of methods implemented in Mplus software Supervisor of four PhD candidates in longitudinal data analysis Her research focuses on: Measurement error in intensive longitudinal studies Statistical standardization techniques Time series analysis for psychological dynamics Bayesian estimation frameworks Person-specific reliability modeling Recent publications address reliability estimation, night gap challenges in ESM data, and multi-timescale modeling. She actively contributes to methodological training through: Utrecht's summer school programs Workshops on R and Mplus Development of e-learning courses Mentorship for early-career researchers

• Cognitive Modelling
• Artificial Intelligence
• Decision-Making
• +۳ مورد دیگر

Dr. Leendert van Maanen is an Associate Professor at the Department of Experimental Psychology, Utrecht University, where he studies cognitive mechanisms underlying information processing and decision-making. His research focuses on developing and validating computational cognitive models, with applications in human-machine interaction and cognitive neuroscience data analysis. Research Themes: Cognitive Modeling, Decision-Making, Human-Centered Artificial Intelligence, Applied Data Science Labs: Leads the COBRA (Cognitive and Behavioral Research in Artificial Intelligence) lab His work spans three levels of cognitive integration in AI: model development, human-machine deployment, and neural data analysis. Recent projects include cognitive strategy detection, digital twin user modeling, and neural event detection using multivariate time series. For collaborations and publications, see leendertvanmaanen.com or Utrecht University Research Portal.