جست‌وجوی استاد و پژوهشگر

• Digital Archaeology
• Computational Archaeology
• Machine Learning in Archaeology
• +۱۳ مورد دیگر

Karsten Lambers is Professor of Digital and Computational Archaeology at the Faculty of Archaeology, Leiden University, where he leads research and teaching in the application of computational methods to archaeological data. His work integrates machine learning, remote sensing, text mining, and citizen science to advance archaeological prospection and heritage management. He is affiliated with the Department of Archaeological Sciences and plays key roles in research groups and university-wide initiatives such as SAILS and ARCHON. His research interests span Digital Archaeology , Machine Learning in Archaeology , Remote Sensing , Geoarchaeology , and Human-Environment Interaction . He investigates how computational tools can extract meaningful archaeological information from large datasets, including LiDAR imagery and excavation reports. His fieldwork spans Central Europe and Latin America, with a focus on prehistoric landscapes and cultural heritage. The analysis of his recent publications reveals a strong trend toward automated detection using deep learning (e.g., R-CNN, WODAN), named entity recognition in archaeological texts (e.g., ArcheoBERTje), and citizen science integration for data validation. His work bridges archaeology with computer science, geomatics, and environmental science, emphasizing interdisciplinary collaboration and methodological rigor. His scientific awards include: Best Thesis Award (University of Zurich, 2005) EUROPA NOSTRA Award (2020, 2022) Membership in the German Archaeological Institute (since 2022) Lambers actively supervises students and leads major research projects such as ABMA, EXALT, and Heritage Quest. He has secured substantial research funding and collaborates widely with computer scientists, geophysicists, and palaeoecologists. His teaching includes digital methods, modeling, and simulation, often linked to ongoing research. He has also contributed to open educational resources and digital textbooks in archaeology. He leads or participates in several research labs and teams, including the Digital Archaeology Research Group (which he chairs), the Heritage Quest citizen science project, and interdisciplinary teams focusing on alpine terraces and Iraqi prospection. His work emphasizes the integration of digital tools into practical archaeological workflows, advocating for complementary human-computer strategies.

• Photovoltaics
• Solar Cells
• Silicon Solar Cells
• +۵ مورد دیگر

Olindo Isabella serves as a Full Professor within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. She heads the Photovoltaic Materials and Devices (PVMD) research group, driving innovation in solar energy conversion technologies. Her academic role encompasses teaching, research leadership, and extensive collaboration with industry and international institutions to advance photovoltaic science and engineering. Professor Isabella's research spans multiple domains of photovoltaics, including silicon and perovskite solar cells, thin-film technologies, offshore floating systems, and agrivoltaics. She investigates material properties, device physics, and system performance to enhance efficiency, reliability, and environmental sustainability of solar energy solutions. Her work integrates experimental and computational approaches for comprehensive analysis. Analysis of her recent publications indicates a strategic focus on machine learning for PV-climate classification, impedance spectroscopy of silicon solar cells, offshore floating platform engineering, and perovskite crystallization processes. These studies collectively address key barriers to large-scale solar deployment, such as performance prediction, structural integrity in marine environments, and novel material synthesis. Scientific Awards: The available information does not mention any specific awards or honors for Professor Isabella. She has guided the research of 20 students and secured competitive funding for impactful projects. Currently, she leads SYMBIOSYST (2023-2026), which explores symbiotic relationships between solar PV and agriculture, and recently completed TRUST-PV (2020-2024), aimed at improving PV plant integration across market segments through machine learning and monitoring technologies. The PVMD group under her direction operates state-of-the-art laboratories for solar cell fabrication and characterization. The team collaborates with global partners on field trials, data analysis, and technology development, contributing to both fundamental knowledge and practical applications in renewable energy.

• Latent Variable Modeling
• Machine Learning
• Measurement Equivalence
• +۳ مورد دیگر

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.

E.I. Friedمشاهده پروفایل

دانشیار

• Clinical psychology
• Psychiatry
• Epidemiology
• +۶ مورد دیگر

Dr. Eiko Fried is an Associate Professor at Leiden University's Faculty of Social and Behavioural Sciences, where he works at the intersection of clinical psychology, psychiatry, epidemiology, methodology, and complexity science. His research focuses on improving psychological science through open science practices and innovative measurement approaches. PhD in clinical psychology, Free University of Berlin Postdoctoral training at KU Leuven and University of Amsterdam Promoted to Associate Professor at Leiden University in 2021 Key research areas include: Psychopathology measurement and classification Network analysis in mental health research Ecological momentary assessment (EMA) methodology Open science advocacy and implementation Dynamic systems modeling in psychology Transdiagnostic approaches to mental disorders Recent publications demonstrate expertise in: Symptom network analysis across disorders Improving depression measurement standards Transdiagnostic assessment protocols Mental health data integration challenges Psychological theory construction Methodological innovations in clinical research

• Neuromorphic Computing
• Memristor-Based Architectures
• Energy-Efficient AI Hardware
• +۷ مورد دیگر

Said Hamdioui serves as a full Professor in the Department of Computer Engineering within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His research focuses on cutting-edge hardware architectures for neuromorphic computing and energy-efficient AI acceleration, with particular emphasis on memristor-based systems, emerging memory technologies, and fault-tolerant designs for edge applications. His research interests span Neuromorphic Computing , Memristor-Based Architectures , and Energy-Efficient AI Hardware , addressing critical challenges in hardware security, computation-in-memory, and reliable edge AI deployment. Recent work demonstrates significant advancements in RRAM/FeFET testing methodologies, spiking neural network implementations, and spin wave computing alternatives to traditional CMOS. His publications reveal strong trends toward real-world deployment of brain-inspired hardware with practical constraints like power efficiency, testability, and security. Award highlights include: DATE'20 Best Paper Award DFT'21 Outstanding Student Paper ETS 2021 Best Paper Award LATS 2018 & 2022 Best Paper Awards Professor Hamdioui actively contributes to the research community through editorial roles at IEEE Transactions on VLSI Systems , IEEE Design & Test , and Journal of Electronic Testing from 2017-2018. His leadership in multi-partner projects like CONVOLVE and NEUROKIT2E demonstrates strong industry-academia collaboration for edge AI solutions. Current work shows increasing focus on practical deployment challenges including in-field fault monitoring, security vulnerabilities in neuromorphic systems, and realistic brain simulation frameworks.

• Technology Innovation
• Strategy and Entrepreneurship
• Collaboration, Competition & Cognition
• +۷ مورد دیگر

Dr. Anindya Ghosh is an Associate Professor at Tilburg University’s Tilburg School of Economics and Management (TiSEM), specifically in the Department of Strategy and Entrepreneurship. His research focuses on Technology Innovation, Strategy and Entrepreneurship, Collaboration, Competition & Cognition, and Applied Machine Learning, with industry expertise in Imaging, Semiconductors, Wireless, Pharmaceuticals, AI, and Venture Capital. He holds a PhD from the Wharton School (2011) and has held academic roles at the Indian School of Business (2015–2018) and IESE Business School (2011–2015). His work explores themes such as technology sourcing dynamics, ecosystem governance via standards-setting, and alliance management. Recent studies address how firms navigate external technology sourcing preferences, shape ecosystem rules through complementarities, and resolve conflicts during multi-firm coordination. He has also contributed to debates on generalizability of archival-based research findings in strategic management. His research employs mixed methods, including computational analysis of standards-setting interactions and longitudinal alliance data. While no formal grants or labs are listed, his affiliations with TiSEM and collaborations with institutions like the Indian Institute of Technology underscore his interdisciplinary reach.

Lotty Hooftمشاهده پروفایل

استاد

• Evidence-Based Medicine
• Systematic Reviews
• Diagnostic Test Accuracy
• +۳ مورد دیگر

Lotty Hooft is a Full Professor at Utrecht University, affiliated with the University Medical Center Utrecht (UMC Utrecht), where she contributes to strategic research programs in Child Health and Methodology development. She holds key external roles as a member of the independent advisory committee for the Advenzo funding scheme at Zorginstituut and the Cochrane Council and Methods Groups for Prognosis, Screening, and Diagnostic Tests. Her research focuses on Evidence-Based Medicine, specializing in Systematic Reviews and Diagnostic Test Accuracy, with clinical applications in Paediatric and Perinatal Medicine. She investigates methodological challenges such as bias in diagnostic studies using expert panels, stakeholder engagement in paediatric trial reporting, and neonatal outcomes in preterm infants. Her work bridges rigorous methodology with real-world clinical impact across diverse medical domains. Analysis of her 2025 publications reveals a cohesive trajectory in advancing diagnostic methodology—particularly in reference standard development and bias mitigation—while expanding into patient-centered paediatric research and neonatal care. Her contributions to sleep medicine diagnostics and workforce well-being further demonstrate cross-disciplinary relevance in improving healthcare systems and clinical practice. No scientific awards were documented in the provided materials. Professor Hooft actively shapes research funding through her Advenzo committee role, evaluating project proposals and subsidy applications, though specific grant awards or student mentorship details remain unreported. Her Cochrane involvement underscores leadership in global evidence synthesis standards, yet no laboratory or dedicated research team structure is specified in the current data.

Elisa Perroneمشاهده پروفایل

استادیار

• Statistics
• Mathematical Statistics
• Dependence Modeling
• +۴ مورد دیگر

Elisa Perrone is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. Her research focuses on dependence modeling, copula theory, and their applications in fields such as public transport analysis, environmental risk assessment, and renewable energy forecasting. Academic Rank: Assistant Professor University: Eindhoven University of Technology (TU/e) Department: Mathematics and Computer Science Elisa’s work explores discrete copulas, zero-inflated data, optimal experimental design, and uncertainty quantification. She has contributed to modeling dependence structures in complex datasets, particularly in transportation systems and climate science. Recent research outputs highlight copula-based statistical post-processing for weather forecasts, analysis of multi-way contingency tables, and uncertainty reduction in LED health management. Her publications span top-tier journals and conferences in statistics and applied mathematics. Scientific Award : Second Best Poster Presentation Award (2015) Elisa actively organizes workshops like the Eurandom Workshop on Dependence Modeling and contributes to editorial activities. She teaches courses on linear statistical models, regression models, and dependence modeling.

Peter van der Heijdenمشاهده پروفایل

استاد

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

Prof. Peter van der Heijden is a Professor of Statistics for the Social and Behavioural Sciences at Utrecht University's Department of Methodology and Statistics. He also holds a professorship in Social Statistics at the University of Southampton. His roles include chairing the Ethical Review Board and the Committee for Policy on Integrity at Utrecht's Faculty of Social and Behavioural Sciences. He chairs the Advisory Council on Methodology and Quality of Statistics Netherlands and serves on the Executive Board of the European Statistical Advisory Committee (ESAC). Since 2017, he has led Utrecht's Applied Data Science focus area, focusing on human-centered AI and data-driven solutions. His research emphasizes population size estimation, fraud detection, and categorical data analysis, with applications for Dutch ministries and international bodies like the UN. He has pioneered methods for estimating human trafficking victims and optimizing healthcare treatments using multilevel models and neural networks. Key projects include the AI for Health initiative with Utrecht Medical Center and Wageningen University. His work bridges statistical rigor with societal impact, addressing challenges in criminal justice, public health, and policy-making through innovative methodologies. Universities: Utrecht University (Primary), University of Southampton Key Committees: European Statistical Advisory Committee, UN Human Trafficking Monitoring Research Themes: Multiple Systems Estimation, Data Science for Social Issues Research interests span statistical methods for complex societal problems, including: Register linkage and fraud detection Machine learning applications in healthcare Human trafficking prevalence estimation His publications (2019-2023) highlight advancements in multilevel modeling, randomized response techniques, and AI-driven clinical data classification. He has advised on policy frameworks for official statistics and contributed to global initiatives like the UN Sustainable Development Goals (Target 16.2). Grants and collaborations include projects with Dutch ministries, the EU, and international organizations. Current initiatives involve optimizing Hepatitis C treatment networks and improving criminal recidivism prediction models. His leadership in interdisciplinary teams ensures methodological innovation addresses real-world challenges.

Muirne Paapمشاهده پروفایل

دانشیار

• Psychometrics
• Clinical Diagnostics
• Item Response Theory
• +۴ مورد دیگر

Dr. Muirne Paap is an Associate Professor with Ius Promovendi at the Faculty of Behavioural and Social Sciences, University of Groningen. She specializes in psychometrics, with expertise in test theory, item response theory, and computerized adaptive testing. Her research focuses on enhancing clinical decision-making through advanced measurement methodologies. Education: PhD in Psychiatry from the University of Oslo (2011), MSc in Clinical Psychology and Psychometrics from the University of Groningen (2006, cum laude). Research Interests: Development of reliable clinical assessment tools Computerized adaptive testing (CAT) applications in healthcare Measurement of personality disorders and quality of life Notable Projects: SEALS Project (2024–2028): Methodology for progressive tests in education PersoniCAT Project (2019–2022): Adaptive diagnostic interviews for personality pathology Awards: FRIPRO Young Research Talents Grant (2018) Member of the Young Academy Groningen (2021) Teaching: Test theory, item response theory, and applied statistics at undergraduate and master's levels. Collaborations: Extensive international partnerships, including with Oslo University Hospital, Harvard Medical School, and the Netherlands Cancer Institute.

• Evolutionary Computation
• Genetic Algorithms
• Optimization
• +۵ مورد دیگر

Dirk Thierens is an Associate Professor in the Department of Computer Science at Utrecht University's Faculty of Science, specializing in Intelligent Systems within AI & Data Science. His academic career spans over 25 years, with continuous publications from 1996 through 2025, demonstrating sustained research activity and leadership in his field. He maintains an active research program with numerous collaborations, most notably with Peter A.N. Bosman, indicating a long-standing productive research partnership. Thierens' research focuses on evolutionary computation, particularly model-based evolutionary algorithms, genetic algorithms, and optimization techniques. His work has evolved from foundational genetic algorithm research in the late 1990s and early 2000s to more specialized model-based approaches in recent years, including significant contributions to Gene-pool Optimal Mixing Evolutionary Algorithms (GOMEA). His expertise spans single-objective and multi-objective optimization, permutation problems, mixed-integer problems, and real-valued optimization. In recent years, his research has expanded into applications in machine learning, particularly semi-supervised learning and neural network optimization. His publication record shows a consistent output of high-quality research, with numerous papers in top conferences like GECCO and journals in evolutionary computation. His most recent work (2023-2025) demonstrates continued innovation in synthetic data generation, neural network combination techniques, and parameterless evolutionary algorithms. The breadth of his work spans theoretical algorithm development, benchmarking methodologies, and practical applications in healthcare and other domains. While no specific scientific awards are mentioned in the available information, his extensive publication record, tutorial contributions at major conferences, and sustained research productivity over multiple decades indicate recognition within the evolutionary computation community. His tutorial work at GECCO conferences suggests he is considered an authority on model-based evolutionary algorithms. Thierens maintains an active research laboratory focused on evolutionary algorithms and their applications, with recent work exploring the intersection of evolutionary computation and deep learning. His research continues to advance both theoretical understanding and practical applications of optimization techniques in complex problem domains.

• Colorectal Cancer
• Infectious Diseases
• Biomarkers
• +۳ مورد دیگر

Christian Ramakers is a Researcher in the Department of Clinical Chemistry at Erasmus University Medical Center's Faculty of Medicine. His work bridges laboratory science and clinical practice, focusing on diagnostic innovation and patient-centered biomarker applications across colorectal cancer, infectious diseases, and kidney disorders. His research spans critical medical domains: Colorectal Cancer: Developing home-based monitoring through capillary carcinoembryonic antigen measurements and refining screening methodologies using occult blood tests. Infectious Diseases: Creating rapid mRNA-based diagnostics to differentiate bacterial/viral infections and predict mortality in emergency settings. Nephrology: Investigating dietary impacts (oxalate, potassium) on kidney function in renal disease patients. Endocrinology: Establishing sex-specific reference intervals for serum phosphate in population health. Analysis of his 2024-2025 publications reveals a consistent theme of translating biomarker research into practical clinical tools. Key trends include home-based cancer monitoring systems, emergency department infection diagnostics using host-response mRNA signatures, and precision dietary management for kidney disease. These studies emphasize patient convenience, diagnostic accuracy, and individualized treatment protocols. No scientific awards were documented in the provided materials. The available information does not specify students supervised by Dr. Ramakers or research grants secured. His collaborative work involves multidisciplinary teams across surgery, oncology, nephrology, and emergency medicine, though specific laboratory structures or team compositions remain unreported in the source text.

Arianna Bisazzaمشاهده پروفایل

دانشیار

• Natural Language Processing
• Multilingualism
• Low-resource Language Modeling
• +۷ مورد دیگر

Arianna Bisazza is an Associate Professor in the Computational Linguistics Group at the University of Groningen, where she leads the InClow research group focused on Interpretable, Cognitively inspired, Low-resource language models. Her work bridges computational linguistics, cognitive science, and language acquisition to develop more robust and interpretable language processing algorithms that can adapt to diverse linguistic phenomena worldwide. Dr. Bisazza's research interests span statistical modeling of human languages in multilingual contexts, with particular focus on improving language model performance for "challenging" or low-resource languages. Her work explores how insights from human language acquisition can inform better language modeling techniques, and she investigates methods to make state-of-the-art NLP systems more interpretable and transparent. As a cross-disciplinary researcher, she actively seeks to enhance our understanding of human language processing and evolution through computational modeling tools. Her recent publications reveal a strong emphasis on multilingual evaluation frameworks (like TurBLiMP and MultiBLiMP), interpretability of language models, and connections between human language acquisition and neural network learning. Her work consistently addresses the challenge of making language technology more robust across diverse linguistic structures and typological features. Outstanding Paper Award at the BabyLM Challenge (CoNLL'24 Shared Task) for "BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency" Dr. Bisazza currently leads a Vidi project funded by the Dutch Research Council (NWO) on improving low-resource language modeling through child language acquisition insights. She is also part of two national consortium projects funded by NWA-ORC initiatives: InDeep (Interpreting deep learning models for language, speech & music) and LESSEN (Low Resource Chat-based Conversational Intelligence). She supervises multiple PhD students, including two China Scholarship Council (CSC)-funded researchers working on simulating human patterns of language learning and change. Her earlier research was supported by a Veni grant (2017-2021) focused on understanding and improving the encoding of linguistic structure in Neural Machine Translation models. As head of the InClow research group, Dr. Bisazza oversees a team investigating interpretable, cognitively inspired approaches to low-resource language modeling. The group's work combines insights from cognitive science and linguistics with cutting-edge NLP techniques to develop language models that better reflect human language processing capabilities, particularly in resource-constrained settings.

Floor Middelمشاهده پروفایل

استادیار

• Child Protection
• Decision-Making in Child Protection
• Bias in Decision-Making
• +۵ مورد دیگر

Floor Middel serves as an Assistant Professor in the Department of Pedagogical and Educational Sciences at the Faculty of Behavioural and Social Sciences, University of Groningen. Her academic work bridges research, teaching, and practical application in child protection systems with a focus on decision-making processes and addressing systemic biases. Dr. Middel's research interests center on understanding and improving decision-making in child protection systems through rigorous examination of how ethnicity, migration background, and gender influence child welfare investigations and outcomes. Her work employs intersectional analysis to uncover disparities in decision-making processes and examines how stereotypes might mediate these disparities. She also explores the role of digital technologies in modern social work practice and develops assessment tools to measure meaningful participation of children in protection systems. Analysis of Dr. Middel's scholarly output reveals a cohesive research trajectory focused on equity and evidence-based practice in child protection. Her publications demonstrate growing international recognition, with work spanning comparative analyses of child protection systems across Europe, investigations into decision-making ecology, and development of practical assessment tools. The research consistently addresses the UN Sustainable Development Goals related to reducing inequalities and promoting just, peaceful, and inclusive societies. Fulbright scholarship (2019) Jo Kolk travel grant (2019) NWO idea generator (2019) Travel grant for U4 Summer school 'Methods and Methodologies: Complexities and Responsibilities in Gender Research' (2018) Travel grant Swiss National Science Foundation (2018) As an educator, Dr. Middel mentors undergraduate students and teaches across multiple courses including Interview Practical, Report Practical, Statistical Models 2, and Qualitative Research Methods. She delivers guest lectures on Decision-making in Child Protection for master's students, supervises traineeships and theses in Orthopedagogy, and coordinates the international summer school 'The Future of Child and Family Welfare Policy: Looking Through Different Lenses.' Her teaching directly connects with her research expertise, providing students with evidence-based perspectives on contemporary child protection challenges.

• Wind Energy
• Wind Turbine Engineering
• Condition Monitoring
• +۳ مورد دیگر

Simon J. Watson is a Professor in the Faculty of Aerospace Engineering at Delft University of Technology, specializing in Wind Energy through the TU Delft Wind Energy Institute (DUWIND). His work focuses on advancing wind turbine technology, wind farm optimization, and renewable energy integration within the university's aerospace framework. His research spans wind turbine engineering, condition monitoring systems, atmospheric effects on energy production, and wind farm design. Key investigations include damage detection in turbine components (blades, drivetrains), simulation of atmospheric gravity waves for improved energy forecasting, and hybrid wind-storage systems for grid stability. Recent work emphasizes machine learning applications for predictive maintenance and high-fidelity modeling of boundary layer conditions. Professor Watson's 2025 publications reveal a strong trend toward AI-driven condition monitoring and refined atmospheric simulations, with consistent focus on operational reliability and damage detection across wind energy systems. His work bridges computational fluid dynamics, structural health monitoring, and energy storage integration. He actively supervises students and leads the €4.2M MERIDIONAL project (2022-2026) on multiscale wind farm modeling, developing advanced toolchains for performance assessment and load prediction. This EU-funded initiative involves collaboration with Siemens Gamesa, Vestas, and ENEL. As a core member of DUWIND, he co-develops industry partnerships and experimental facilities including wind tunnel testing and field monitoring systems for offshore wind farms. His team maintains close ties with the Netherlands Wind Energy Association and European Wind Energy Technology Platform.