جستجوی استاد راهنمای دانشگاهی

• Late Antiquity
• Epigraphy
• Religious History
• +۳ مورد دیگر

Konstantin Klein is a Visiting Professor at the University of Amsterdam 's Faculty of Humanities, specializing in History, European Studies, and Religious Studies. His research focuses on Late Antiquity, particularly the intersection of epigraphy, religious history, and urban development in Constantinople and Jerusalem. Research interests include: Epigraphic analysis of ancient Semitic inscriptions Digital humanities applications in text restoration Urban spatial organization in religious contexts Material culture of Late Antique cities Gender roles in epigraphic patronage Recent publications demonstrate expertise in: Machine learning for damaged inscription recovery Comparative urbanism of Byzantine capitals Analysis of Palmyrene funerary art Epigraphic patterns in Christian holy sites Interpretation of Late Antique decay processes Gender-regulated epigraphic expressions

• Digital Humanities
• Visual Culture
• Computational History
• +۵ مورد دیگر

Dr. Thomas Smits is a faculty member at the Faculty of Humanities of the University of Amsterdam , specializing in Digital Humanities , Visual Culture , and Computational History . His research bridges artificial intelligence with historical image analysis, focusing on media history, colonial visual representations, and algorithmic approaches to archival studies. Recent publications highlight his work on multimodal datasets, generative AI in protest memory reconstruction, and the ethical implications of machine learning for historical research. Smits employs computational methods like CLIP and computer vision to analyze large-scale visual collections, including magic lantern slides, historical advertisements, and news images. Key research areas: Digital Humanities, Visual Memory, Algorithmic Analysis, Colonial Media, Gender Studies, Historical Epistemology. Methodological focus: Stochastic modeling, multimodal machine learning, distant reading of visual datasets, and AI-driven archival enhancement.

• Pharmacoepidemiology
• Epidemiology
• Observational Research
• +۹ مورد دیگر

D.L. (Daniala) Weir is an Assistant Professor at Utrecht University's Science college, specifically within the Pharmacoepidemiology and Clinical Pharmacology department. Her research bridges clinical pharmacology , epidemiology , and applied data science to enhance medication safety for patients with multimorbidity. Education: B.Sc. in Biological Sciences (University of Alberta, 2012) M.Sc. in Epidemiology (University of Alberta, 2014) Ph.D. in Epidemiology (McGill University, 2019) Postdoctoral Fellowship (University of Toronto, 2021) Her work combines causal inference frameworks with machine learning to address clinical challenges using real-world data from Canada, the UK, and the Netherlands. She collaborates across disciplines with biostatisticians , data scientists , and healthcare professionals , emphasizing patient-centered approaches through engagement with patients and caregivers. Key research areas include: Dynamic treatment regimens Medication-related harm prevention Adverse drug event detection mHealth applications for adherence Her publications demonstrate expertise in applied data science methodologies for time-to-event analysis , predictive modeling , and multimorbidity treatment optimization .

Gijs van Dijckمشاهده پروفایل

استاد

• Law and Artificial Intelligence
• Data-Driven Analysis
• Empirical Legal Research
• +۳ مورد دیگر

Gijs van Dijck is a Professor of Private Law at Maastricht University's Faculty of Law, where he heads the Maastricht Law and Tech Lab and serves as a Principal Investigator at the Brightlands Institute for Smart Society (BISS). His work bridges law and technology, focusing on artificial intelligence, data-driven legal analysis, and empirical research in contract, tort, and insolvency law. He has published in top-tier journals like the Oxford Journal of Legal Studies and collaborated with institutions such as Stanford University and the Netherlands eScience Center. His research explores the intersection of legal theory and computational methods, including network analysis of human rights precedents, hybrid retrieval systems for multilingual legal texts, and ethical frameworks for AI in law. Recent publications emphasize regulatory challenges in cybersecurity, open-texture in legal concepts, and responsible AI applications in jurisprudence. Van Dijck actively contributes to legal scholarship as a journal editor and advisor, while his teaching covers computational legal research and core private law subjects. His work reflects a commitment to interdisciplinary innovation in legal studies.

Jeroen van den Hovenمشاهده پروفایل

استاد

• Ethics of AI
• Responsible Innovation
• Value-Sensitive Design
• +۱۱ مورد دیگر

Jeroen van den Hoven is a Professor at the Faculty of Technology, Policy and Management at Delft University of Technology . He serves as Editor in Chief of Ethics and Information Technology (Springer) and Founding Chair of the CEPE conference . His affiliations include advisory roles in the EU Commissioner Reding's ICT group , grants from the Dutch Research Council , and collaborations with industry leaders like SUN, IBM, and Getronics . Research Interests Van den Hoven's work bridges ethics, artificial intelligence, and public policy . He focuses on value-sensitive design , responsible innovation , and moral philosophy applied to emerging technologies. His research addresses AI governance , privacy , and ethical frameworks for autonomous systems , with applications in healthcare, military technology, and smart cities . Scientific Contributions His recent publications explore trust in foundation models , transparency in AI , and human control over autonomous systems . Themes include epistemic dependence in healthcare AI , democratic integrity in digital eras , and ethical guidelines for military applications . Awards & Grants Recipient of multiple Dutch Research Council grants , he advocates for ethical design principles and chairs international conferences on computer ethics . His advisory roles span Dutch government commissions and EU ICT policies .

Bruna Goveia da Rochaمشاهده پروفایل

استادیار

• Textile Innovation
• Digital Craftsmanship
• Research-Through-Making
• +۳ مورد دیگر

Bruna Goveia da Rocha is an Assistant Professor at the Department of Industrial Design, Eindhoven University of Technology. Her research focuses on advancing research-through-making practices, particularly through textile innovation and material experimentation from a more-than-human perspective. She explores how samples and sample-making can drive creative and technological breakthroughs in digital craftsmanship. Education: PhD in Industrial Design, TU/e (2023), thesis: 'Becoming Travelers: Reflecting on the Emerging Practices of Sample Making in Digital Craftsmanship' MSc in Industrial Design, TU/e (2015), thesis: 'Flow: In the path towards embodied guidance' BSc in Product Design, Universidade Positivo (2012) Research interests include: Material agency and serendipity in design Documentation strategies for digital craftsmanship Embodied interaction through textiles and smart materials Sample archives as dynamic knowledge repositories Her recent work investigates how 'diffractive re-interpretations' of material samples can uncover hidden potentials, as seen in projects like 'Making in the Dark' (2024). She also explores negotiations between designers and fabrication machines, such as in 'Entering the 3D printer' (2024). Lab affiliations include the 'Making With…' group at TU/e, focused on collaborative material exploration. Her work bridges traditional craftsmanship with digital fabrication techniques, emphasizing iterative and embodied design processes.

• Computational Neuroscience
• Neurophysics
• Brain Oscillations
• +۲ مورد دیگر

Paul Tiesinga is Professor of Neuroinformatics at Radboud University's Faculty of Science, where he directs the Neuroinformatics group at the Donders Centre for Neuroscience. His research focuses on understanding information processing in the brain, particularly the role of oscillations ('brain waves') in cognitive functions and disease mechanisms. He combines computational modeling with experimental approaches including optogenetics to investigate cortical dynamics. As Director of Education for the Institute for Mathematics, Physics and Astronomy, he oversees academic programs while maintaining active research in: Computational modeling of cortical circuits Oscillation mechanisms in neural coding Data sharing policies for neuroscience Information transfer between brain areas His recent publications reflect advances in neurotechnology development, thalamocortical connectivity analysis, and novel methodologies for neural data interpretation.

Lambert Schomakerمشاهده پروفایل

استاد

• Artificial Intelligence
• Pattern Recognition
• Machine Learning
• +۶ مورد دیگر

Prof. dr. Lambert Schomaker is a Full Professor of Artificial Intelligence at the University of Groningen, leading the Artificial Intelligence and Cognitive Engineering (ALICE) institute within the Faculty of Science and Engineering. His work spans perceptual intelligence, machine learning, and neuromorphic computing, with significant contributions to handwriting recognition, robotics, and historical document analysis. Affiliations: Bernoulli Institute, CogniGron (Cognitive Systems & Materials), and Data Science & Systems Complexity (DSSC) centers. Education: M.Sc. (1983) and Ph.D. (1991) in Psychophysiology/Psychology from Nijmegen University. Research Interests: AI, pattern recognition, neural networks, autonomous systems, and applications in cultural heritage (e.g., the MONK system for handwritten archive indexing). He has pioneered handwriting recognition methods used in modern devices like tablets and led the 30MEuro Target project for large-scale data mining. Grants & Projects: Includes NWO-funded initiatives (e.g., Catch, TriGraph) and EU projects (MANTIS, MANIC). Current focus areas include neuromorphic computing with electronic materials and AI-driven analysis of the Dead Sea Scrolls. Awards: IBM Faculty Awards (2011, 2012), IAPR Best Paper Award (2012), Ching Yee Suen Special Award (2012). Advising: Supervised over 20 PhD students in AI, robotics, and document analysis. Active in industry collaborations (e.g., HP, Microsoft). Labs/Teams: Director of ALICE, contributor to CogniGron, and leader of the MONK system team. Current research includes neuromorphic hardware development and AI applications in maintenance systems.

Kerstin Bunteمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• XAI (Explainable Artificial Intelligence)
• +۷ مورد دیگر

Kerstin Bunte is a Professor of Machine Learning for interdisciplinary data analysis at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Bernoulli Institute's Intelligent Systems Group. She holds an Honorary Fellowship at the University of Birmingham and leads the Intelligent Systems Group. Her research focuses on interpretable machine learning, interdisciplinary applications (e.g., astrophysics and biomedical data), and visualization techniques. Research Interests: - Machine Learning - Artificial Intelligence - Explainable AI (XAI) - Interpretable Models - Dimensionality Reduction - Data Visualization - Astrophysical Data Analysis - Medical Imaging Awards & Grants: - DSSC XS funding (2023) - NWO VIDI grant (2020) - Rosalind Franklin Fellowship (2016–present) Advising & Students: Supervised PhD students include Elisa Oostwal, Janis Norden, Matteo Marcantoni, and Petra Awad. Research spans topics like tumor segmentation in medical imaging, astrophysical structure detection, and autonomous navigation systems. Labs & Collaborations: Leads the Intelligent Systems Group, collaborating with institutions like the University of Birmingham and the University of Warwick. Work involves interdisciplinary projects combining machine learning with astronomy, biomedical sciences, and robotics.

• Machine Learning
• Neural Networks
• Uncertainty Quantification
• +۴ مورد دیگر

Dr. Matias Valdenegro Toro is an Assistant Professor of Machine Learning at the University of Groningen within the Faculty of Science and Engineering and the Artificial Intelligence department of the Bernoulli Institute. He holds a PhD from Heriot-Watt University (2019) and a Master's in Autonomous Systems from Bonn-Rhein-Sieg University of Applied Sciences (2014). His research focuses on trustworthy machine learning models , particularly in uncertainty quantification , medical AI , and robotics , with applications in computer vision and explainable AI. He teaches courses like Introduction to Machine Learning and Deep Learning at the Bachelor and Master levels. His work emphasizes robustness in AI systems, including uncertainty estimation for medical applications, super-resolution techniques, and neuromorphic robotics. He has published widely on topics like Bayesian neural networks, prompt tuning, and sanity checks for explanations. Notable awards include Best Reviewer at ICML (2024) and Highlighted Reviewer at ICLR (2022). He collaborates with institutions like the German Research Center for Artificial Intelligence and actively contributes to open-source datasets (e.g., the Japanese Uncertain Scenes Dataset ). Key grants and activities include organizing the ENLIGHT BIP Course on Deep Learning for Forestry and teaching at the European Summer School on AI . His research also addresses regulatory challenges like the EU AI Act's implications for uncertainty quantification in general-purpose AI.

• Semantics
• Computational Linguistics
• Natural Language Processing
• +۷ مورد دیگر

Dr. Denis Paperno is an Assistant Professor of Computational Linguistics at Utrecht University in the Department of Languages, Literature and Communication within the Humanities faculty. He maintains a dual affiliation, working at Utrecht University while on détachement from a researcher position at CNRS (the National Center of Scientific Research). His academic background includes a PhD in Linguistics from the University of California Los Angeles and prior work at the University of Trento in Marco Baroni's research group. Dr. Paperno's research expertise spans several critical domains in computational linguistics: Semantics and meaning representation Artificial Intelligence applications to language Distributional and neural semantic models Word embeddings and semantic compositionality Cross-modal and cross-lingual language understanding Language typology and lexical semantics His research program investigates fundamental questions about computational meaning representation, exploring how word meanings interact to form complex expressions and how to integrate diverse information sources like images, knowledge bases, and linguistic resources into unified frameworks. He has developed significant benchmarks for evaluating language models and made important contributions to semantic typology and quantification theory. Dr. Paperno's publication record demonstrates consistent contributions to top-tier computational linguistics venues, with recent work spanning theoretical linguistics to practical deep learning applications. His articles reveal expertise bridging formal semantic theory and cutting-edge neural network approaches to linguistic problems. He is actively involved in teaching courses including 'Formal and Natural Languages,' 'Machine Learning I,' 'Models for Language Processing,' and 'Semantics.' Current research projects include work with Pablo Mosteiro funded by an Applied Data Science research grant from Utrecht University, and he is scheduled to deliver a keynote at the 16th International Conference on Computational Semantics (IWCS) in Düsseldorf in September 2025.

Patrick J.F. Groenenمشاهده پروفایل

استاد

• Data Science
• Multidimensional Scaling
• Optimization Algorithms
• +۵ مورد دیگر

Patrick J.F. Groenen is a full Professor of Statistics at the Erasmus School of Economics (ESE), Erasmus University Rotterdam , and currently serves as its Dean. His academic career spans leadership roles, including Director of the Econometric Institute (2014-2020) and President of the International Association for Statistical Computing (2015-2017). He has held visiting positions at Stanford University and contributed extensively to multidimensional scaling (MDS), data science , and statistical genetics . His research focuses on numerical algorithms for MDS, support vector machines , and optimization techniques applied to diverse fields. He is a founding editor of the open-access journal Journal of Data Science, Statistics, and Visualisation and has authored major textbooks including Modern Multidimensional Scaling and Applied Multidimensional Scaling and Unfolding . His work appears in top journals like Nature Genetics , Psychometrika , and Journal of Machine Learning Research . Recent publications highlight advancements in convex clustering , genomic prediction , and robust statistical methods . He has developed influential R packages such as GenSVM , SVMMaj , and smacof for multiclass classification and MDS software. His supervisory record includes 14 PhD students across economic psychology, genetics, and data science. Academic activities include editorial roles at Advances in Data Analysis and Classification and Psychometrika , alongside organizing international conferences like CARME 2007 on correspondence analysis. His contributions to nonlinear biplots and response style modeling have advanced visual data interpretation in social sciences.

• Digital Soil Mapping
• Soil Organic Carbon
• Machine Learning Applications
• +۳ مورد دیگر

Laura Poggio serves as a Research Associate at ISRIC - World Soil Information, which operates under Wageningen University & Research. Her work focuses on advancing digital soil mapping methodologies and global soil information systems through interdisciplinary research combining remote sensing, machine learning, and soil science. Her primary research interests include: Digital Soil Mapping at multiple scales (local to global) Soil Organic Carbon monitoring using satellite imagery Machine learning applications for soil property prediction Global soil information systems development (notably SoilGrids) Integration of remote sensing data with soil databases Analysis of her 15 most recent publications reveals strong emphasis on continental-scale soil monitoring systems, particularly using Sentinel-2 satellite data for soil organic carbon assessment across Europe. Her work consistently addresses methodological challenges in handling spatial uncertainty, integrating multi-source data, and developing scalable models applicable from local to global contexts. Key technological approaches include machine learning algorithms (particularly Random Forest), survival probability models for censored data, and multi-sensor remote sensing integration. Her research output demonstrates significant collaboration across European institutions through projects like EJP SOIL and contributions to the Global Soil Partnership. While no specific awards are documented in the available materials, her work shows substantial scholarly impact through high citation counts and dataset adoption. Dr. Poggio actively contributes to soil science through conference presentations and supervised research work, particularly focusing on operational implementation of digital soil mapping for environmental monitoring and policy support.

• Meteorology
• Climate Modeling
• Radiative Transfer
• +۷ مورد دیگر

Chiel van Heerwaarden is an Associate Professor of Meteorology at Wageningen University & Research. His work focuses on cloud dynamics, radiative transfer, and their impacts on climate processes. He leads projects on solar irradiance variability, convective cloud organization, and atmospheric modeling. He has received a NWO Vidi grant for his research on cloud shadows and radiative effects. His research integrates machine learning, large-eddy simulations, and observational data to advance understanding of boundary layer processes and climate feedbacks. Key research areas include: irradiance variability under broken clouds, 3D radiative effects in shallow cumulus clouds, and the interaction between clouds and land surface processes. He collaborates on field experiments like CloudRoots-Amazon22 and Fesstval, advancing multiscale modeling approaches. His team addresses challenges in kilometer-scale Earth system modeling and wildfire-atmosphere interactions. Recent achievements include developing efficient radiative transfer parameterizations and analyzing extreme weather phenomena like heatwaves linked to soil drought. He advises five PhD candidates on topics ranging from convective self-aggregation to forest fire plumes. His work bridges computational science and observational data to improve climate projections and atmospheric understanding. Grants: NWO Vidi Grant (2019) Labs/Teams: Meteorology and Air Quality Group at Wageningen University Key Projects: CloudRoots-Amazon22, Shedding Light on Cloud Shadows, Real-Weather Large Eddy Simulation

Hendrik Baierمشاهده پروفایل

استادیار

• Artificial Intelligence
• Planning
• Search Algorithms
• +۵ مورد دیگر

Hendrik Baier is an Assistant Professor in the Information Systems group at Eindhoven University of Technology (TU/e), where he joined in 2022. His research focuses on creating agents capable of succeeding in complex decision-making tasks to help human users solve real-world problems. His work spans planning for long-term goals, learning in unknown environments, and explainability of AI systems for effective human-AI interaction. Dr. Baier's research interests center on planning and search algorithms, reinforcement learning, and explainable AI systems. His work investigates how AI can think ahead and explain its reasoning process, particularly in sequential decision-making contexts. He applies these techniques to practical domains including logistics and transportation, smart manufacturing, and sustainable energy systems. His research bridges theoretical foundations with real-world applications through collaborative projects with industry partners. Analysis of his recent publications reveals a strong focus on explainability in sequential decision-making, with increasing integration of large language models to enhance traditional planning algorithms. His work spans theoretical foundations of Monte Carlo Tree Search, programmatic policy generation, multi-agent reinforcement learning, and practical applications of these techniques. A notable trend is the growing emphasis on human-AI collaboration, where AI systems must not only perform well but also effectively communicate their reasoning to human users. Dr. Baier actively collaborates with researchers across multiple institutions, including CWI Amsterdam where he maintains an affiliation, and has participated in significant interdisciplinary efforts such as the Dagstuhl Seminar on Explainable AI for Sequential Decision Making. His research group at TU/e works closely with industry partners to translate fundamental research into practical applications. He is affiliated with EAISI (Eindhoven Artificial Intelligence Systems Institute) and contributes to the Decision Making with Artificial Intelligence educational program at TU/e. His laboratory work focuses on developing benchmark environments and frameworks that enable rigorous evaluation of decision-making algorithms, with recent contributions including MOMAland for multi-objective multi-agent reinforcement learning.