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.

Raquel Fernándezمشاهده پروفایل

استاد

• Computational Linguistics
• Dialogue Systems
• Cognitive Science
• +۳ مورد دیگر

Raquel Fernández is Full Professor of Computational Linguistics and Dialogue Systems at the University of Amsterdam, where she leads the Dialogue Modelling Group at the Institute for Logic, Language & Computation (ILLC). As Vice-Director for Research at ILLC and a Fellow of the ELLIS Society, she bridges computational linguistics, cognitive science, and artificial intelligence through her research on language use in multimodal and conversational contexts. PhD in Computational Linguistics from King's College London Prior research positions at University of Potsdam and Stanford University's CSLI Her work explores how cognitive constraints, social interaction, and perception shape language use, with a focus on: Visually-grounded language processing Multimodal dialogue modeling Model uncertainty and calibration Language grounding in multimodal data Language learning and semantic change Dialogue reference resolution Recent publications analyze multimodal reasoning limitations, cross-lingual knowledge consistency, and uncertainty modeling in dialogue systems. She has received multiple accolades including an ERC Consolidator Grant , NWO VENI/VIDI/Aspasia fellowships , and EMNLP/GenBench awards . Outstanding Paper Award (EMNLP 2023) Best Data Award (GenBench Workshop 2023) ELLIS Society Fellow ERC Consolidator Grant #819455 recipient NWO VENI/VIDI/Aspasia awardee As a leader in academic service, she serves on the SIGDAT Executive Committee and chairs multiple conference committees. Her lab develops models for multimodal dialogue, visual storytelling, and grounded language understanding.

Pascal Mettesمشاهده پروفایل

استادیار

• Hyperbolic Deep Learning
• Computer Vision
• Vision-Language Models
• +۳ مورد دیگر

Pascal Mettes is a tenured Assistant Professor at the University of Amsterdam within the Informatics Institute, specializing in Artificial Intelligence. He leads groundbreaking research in hyperbolic deep learning, a field he has significantly advanced through theoretical developments and practical applications in computer vision and multimodal learning. His research focuses on three primary domains: hyperbolic vision-language models that address the hierarchical nature of language-vision relationships; hierarchical deep learning using hyperbolic embeddings that naturally accommodate exponential growth patterns; and robust deep learning in hyperbolic space that improves out-of-distribution detection and network resilience. Mettes has established himself as a leading figure in this emerging field through numerous publications at top-tier conferences including CVPR, ICCV, ICML, NeurIPS, and ICLR. His recent work demonstrates how hyperbolic geometry provides natural solutions to fundamental limitations in modern deep learning, particularly regarding hierarchical data structures that cannot be adequately represented in Euclidean space. The publication trends show increasing impact and recognition in the computer vision and machine learning communities, with multiple papers receiving oral presentations and best paper nominations. Best paper nomination ESWC25 for 'Designing Hierarchies for Optimal Hyperbolic Embedding' Finalist MM 2023 Best Open-Source Software Competition (for HypLL) Multiple reviewer awards across major conferences including CVPR, ICLR, ECCV, ICML, and NeurIPS MM 2016 Best Doctoral Student Award TRECVID 2015 Winner Multimedia Event Detection Benchmark Mettes actively mentors eight PhD students working on hyperbolic learning and related topics, while also securing significant research funding including ELLIs PhD Award, NWO ClickNL, Google Perception Academic Funding, and Data Science Centre PhD Grants. He serves in prominent academic roles as Program Chair for International Conference on Multimedia Retrieval 2026 and has organized multiple workshops on hyperbolic learning at major conferences. His leadership in establishing hyperbolic deep learning as a recognized research direction is evident through his survey paper in IJCV 2024 and the development of the HypLL library for hyperbolic learning.

• Robotics
• Control Systems
• Multi-Agent Systems
• +۶ مورد دیگر

Herman Bruyninckx is a Part-Time Full Professor at Eindhoven University of Technology (TU/e) in the Mechanical Engineering department, specifically within the Control Systems Technology group and EAISI High Tech Systems initiative. He also serves as a professor (Hoogleraar) at KU Leuven in Belgium. Academic focus on robotics, control systems, and multi-agent coordination Active research in model predictive control , semantic mapping , and dynamic constraint algorithms Recent publications address industrial automation , agro-food robotics , and haptic technology Research Highlights : Developed hybrid decision-making frameworks for multi-agent navigation Innovated swing-free control methods for robotic pick-and-place operations Formulated constrained dynamics algorithms with LQR-Gauss principle integration Created ExoTen-Glove for haptic feedback in virtual environments Collaborative Projects : Coordinated with researchers like René van de Molengraft , Elena Torta , and Koen de Vos Contributed to NWO/TTW FlexCRAFT project for cognitive robotics in agro-food technology

George Vosselmanمشاهده پروفایل

استاد

• Photogrammetry
• Remote Sensing
• Laser Scanning
• +۷ مورد دیگر

George Vosselman is a Full Professor at the University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), specializing in Geo-Information Extraction with Sensor Systems. Educated with honours at Delft University of Technology (1986) and PhD in Photogrammetry from Rheinische Friedrich Wilhelms University of Bonn (1991), he has held academic roles at the University of Stuttgart, University of Washington, and Delft University of Technology (1993–2004). Since 2004, he has been a key figure at ITC, serving as department head (2012–2018, 2023–). Education: Delft University of Technology (BSc with honours, 1986), Rheinische Friedrich Wilhelms University of Bonn (PhD with honours, 1991) His research focuses on leveraging sensor technology advancements for large-scale geo-information production. Key expertise includes quality analysis of laser altimetry data, point cloud segmentation/classification, 3D building/road modeling, and model-driven imagery analysis. He has published over 220 papers and co-edited the textbook Airborne and Terrestrial Laser Scanning (2010). Recent work integrates deep learning with geospatial data, addressing semantic segmentation, visual question answering, and drone-based mapping. Recent publications (2025–2023) highlight trends in deep learning for remote sensing , including multimodal question answering benchmarks (HRVQA), vectorized building extraction (RoIPoly), latent diffusion for road modeling (LDPoly), and drone obstacle avoidance systems. His work bridges photogrammetry , computer vision , and robotic mapping , with applications in urban planning, disaster management, and informal settlement monitoring. Scientific Awards : Hansa Luftbild (1993), ISPRS Otto von Gruber (2000), Schwidefsky Medal (2012), Karl Kraus Medal (2012), ASPRS Fairchild Award (2015), ISPRS Fellow (2020) As an educator, Vosselman has taught photogrammetry, remote sensing, and laser scanning at Delft University of Technology and globally. He chaired the ITC Examination Board (2015–2023) and modernized geo-information education in Asia/Africa. His software for point cloud processing is commercialized in Europe, and he currently leads ISPRS working groups on point cloud methodologies. Labs/teams include the Earth Observation Science Chair Group at ITC, collaborating on UAV-based datasets (UAVid, UAVPal) and indoor laser scanning systems. Recent activities (2025) involve invited talks on pulse matching limitations in laser scanning and deep learning for point cloud classification.

Daniel Fruminمشاهده پروفایل

استادیار

• Logic
• Type Theory
• Program Verification
• +۵ مورد دیگر

Daniel Frumin is an Assistant Professor in the Department of Fundamental Computing Science at the University of Groningen, affiliated with the Bernoulli Institute. His research focuses on Logic, Type Theory, and Program Verification, with a particular emphasis on formal methods for concurrency, type systems, and homotopy type theory. He has contributed to foundational work in denotational semantics, modular programming language design, and mechanized verification of concurrent systems. His expertise includes the application of logical frameworks to concurrency models, such as ReLoC (Relational Logic for Concurrent Programming) and the integration of type theories with operational semantics. Recent work explores guarded interaction trees, interval domains in homotopy type theory, and compositional security properties for fine-grained systems. Frumin has published extensively in top-tier venues like ESOP, CONCUR, and ACM POPL, with peer-reviewed contributions on topics ranging from bunched implications in session-based concurrency to formal verification of data structures like concurrent queues. His research often bridges theoretical computer science and practical formal verification, leveraging tools like Coq for mechanized proofs. Collaborations include projects with institutions such as Aarhus University (Denmark) and the University of Bologna, focusing on univalent foundations, categorical semantics, and security verification. His work is supported by grants from the Dutch Research Council (NWO) and industry partnerships like Meta's Folly Library verification efforts. Labs/Teams: Active contributor to the Bernoulli Institute's Formal Methods Group and the Univalent Foundations initiative. His research group specializes in applying type-theoretic and categorical methods to concurrency and verification challenges.

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.

Twan Bastenمشاهده پروفایل

استاد

• Embedded Systems
• Cyber-Physical Systems
• Model-Driven Engineering
• +۳ مورد دیگر

Twan Basten is a Full Professor in the Electronic Systems group at Eindhoven University of Technology (TU/e). He leads research on embedded and cyber-physical systems, focusing on model-driven design, computational models, and system dependability. He holds an MSc (1993) and PhD (1998) in Computing Science from TU/e, advancing from Assistant to Full Professor by 2009, and became the Electronic Systems group chair in 2013. His research spans international projects (FP5-7, H2020, ECSEL) and Dutch initiatives (STW, NWO, RVO), with over 200 publications and seven best paper awards. He has co-supervised 21 PhD students and actively participates in program committees and conferences. His work contributes to UN Sustainable Development Goals through innovations in smart systems. Education: MSc in Computing Science, TU/e (1993) PhD in Computing Science, TU/e (1998) Research Interests: Explores design methodologies for embedded systems, including scenario-based design, real-time scheduling, and performance analysis. Specializes in model-driven engineering and computational models to ensure system dependability. Active in projects like TRANSACT (real-time systems) and SAM-FMS (flexible manufacturing). Key Contributions: Co-author of 1 book and over 200 scientific publications Recipient of seven best paper awards Co-supervised 21 PhD degrees Senior member of IEEE and lifetime member of ACM Labs & Teams: Leads the Model-Based Design Lab and contributes to EAISI High Tech Systems initiatives. Collaborates on tools like TRACE4CPS for execution trace analysis and CReTS for vehicle platooning simulation.

• Computational Semantics
• Natural Language Processing
• Formal Semantics
• +۳ مورد دیگر

Johan Bos is a Professor of Computational Semantics at the University of Groningen. His research focuses on formal semantics, natural language processing, and semantic parsing, with contributions to tools like Boxer and the C&C parser. He leads the Groningen Meaning Bank project, creating multilingual semantic annotations. His work bridges computational linguistics and AI, emphasizing structured meaning representations. Key contributions include foundational textbooks such as Representation and Inference for Natural Language and Learn Prolog Now! . His research spans semantic analysis, discourse representation theory, and cross-lingual meaning parsing. Collaborations include projects on annotated corpora and neural semantic parsing advancements. Publications address challenges in semantic parsing, discourse structures, and interdisciplinary applications like tombstone inscription analysis. He actively contributes to computational semantics conferences and workshops, driving semantic tagging and interlingual meaning representation initiatives.

Wan Fokkinkمشاهده پروفایل

استاد

• Distributed Systems
• Formal Analysis of Protocols
• Concurrency Theory
• +۳ مورد دیگر

Prof. Wan Fokkink is a Full Professor in Theoretical Computer Science at Vrije Universiteit Amsterdam (VU) and holds a part-time position as Professor of Model-Based System Engineering at Eindhoven University of Technology (TU/e). His research focuses on distributed systems, formal analysis of protocols, and supervisory control synthesis. He leads the Theoretical Computer Science group at VU and has authored three influential textbooks: Introduction to Process Algebra , Modelling Distributed Systems , and Distributed Algorithms: An Intuitive Approach . Education: MSc in Mathematics (University of Amsterdam, 1990) and PhD in Computer Science (University of Amsterdam, 1994). Postdoctoral work at Utrecht University and lectureship at Swansea University preceded his leadership roles at CWI (2001–2004) and VU (since 2004). Teaching includes courses on logic, concurrency, and distributed algorithms. He is editor of Logical Methods in Computer Science and co-founder of the Electronic Proceedings in Theoretical Computer Science . Active in professional organizations: co-founder of IFIP WG 1.8 (Concurrency Theory) and steering committee member of the CONCUR conference. Research interests span formal verification, concurrency theory, and practical applications of supervisory control in engineering systems (e.g., traffic management, ship locks). His work aligns with sustainable development goals through contributions to reliable system design and optimization. Recent articles explore model-based specification, distributed control under communication delays, and tools like Eclipse ESCET for supervisory control synthesis. Collaborations span international teams in Europe and beyond.

• Computational Musicology
• Computational Humanities
• Music Information Retrieval
• +۵ مورد دیگر

Peter van Kranenburg is an Assistant Professor in Music Information Computing at Utrecht University (The Netherlands) and a guest researcher at the KNAW Meertens Institute in Amsterdam. His work bridges computer science and musicology with a focus on computational approaches to music analysis. His research spans computational musicology, computational humanities, and music information retrieval. Van Kranenburg specializes in developing computational models for analyzing musical structures, particularly melodic similarity measures, folk song transmission patterns, and large-scale analysis of song traditions. His work often involves interdisciplinary collaboration between computer scientists and musicologists. His recent publications demonstrate consistent research in computational approaches to music analysis, with a focus on melodic similarity, folk song transmission, and computational modeling of musical traditions. His work spans both technical computer science aspects of music information retrieval and substantive musicological applications. Van Kranenburg has been involved in several significant research projects including the H2020 Polifonia-project (2021-2024), which focused on large scale analysis of European song traditions and curation of historic data on musical instruments, particularly pipe organs. His educational background combines technical and musicological expertise, having earned master's degrees in both Electrical Engineering (Delft University of Technology, 2003) and Musicology (Utrecht University, 2004), followed by a PhD from Utrecht University. His doctoral research developed melodic similarity measures and software tools for analysis of audio recordings of religious chant.

• Service-Oriented Computing
• Business Process Management
• Scientific Workflows
• +۴ مورد دیگر

D. (Dimka) Karastoyanova is a Professor of Information Systems at the University of Groningen , Faculty of Science and Engineering, Computer Science Department. She leads the Information Systems Group and serves as vice-chair of the Bernoulli Institute Board since 2024. Previously, she was Head of the Computer Science Department (2020-2024) and a member of the Informatics Europe Board since 2023. Dimka's research focuses on data-driven, service-based process automation for cross-organizational collaboration, emphasizing runtime adaptability, security, and sustainability . Her work spans applications in manufacturing, logistics, healthcare, and eScience , integrating Scientific Workflows, Cloud Computing, and Middleware Systems to enhance process flexibility. Her scientific contributions include 15+ publications on topics like Choreography Lifecycle Management, BPEL Adaptation, and Cloud Migration Frameworks , reflecting her expertise in Service-Oriented Computing, Workflow Technologies, and Distributed Systems . She received the Rosalind Franklin Fellowship and has served on program committees for IEEE, ICWS, and BPM conferences . Dimka holds a PhD in Computer Science (TU Darmstadt, 2006), an MSc in Computational Engineering (University of Erlangen-Nuremberg), and MSc/BSc in Industrial Engineering (Technical University of Sofia). She previously held roles as Associate Professor of Data Science at KLU Hamburg and Senior Researcher at Hasso Plattner Institute, University of Potsdam.

• Material-Driven Design
• Materials Experience
• Design for Wellbeing
• +۵ مورد دیگر

Stefano Parisi is an Assistant Professor at TU Delft , affiliated with the Faculty of Industrial Design Engineering and Department of Sustainable Design Engineering . His research focuses on Materials Experience , Material-Driven Design , and regenerative material ecologies. In 2024, he co-founded the Centre of Design Research for Regenerative Material Ecologies (DREAM) , advancing bio-based material applications. Previously working on DATEMATS , DESTEX , and MaDe projects, he explores hybrid material systems through European-funded initiatives. BSc/MSc in Product Design, Politecnico di Milano PhD in Design (2021), Politecnico di Milano His research interests span: Material-Driven Design methodologies Human-planet wellbeing through materials Biobased/smart material interactions Temporal material design Recent publications analyze Interactive Connected Smart (ICS) Materials across textile, wearable, and archival domains. He teaches Material-Driven Design at the Master’s level and has secured industry funding from lululemon athletica . No scientific awards are explicitly mentioned in the provided data.

J.C. Scholtesمشاهده پروفایل

استاد

• Natural Language Processing
• Information Retrieval
• Text Mining
• +۵ مورد دیگر

J.C. Scholtes is an Extra-ordinary Professor of Text Mining at the Department of Knowledge Engineering, Faculty of Science and Engineering, University of Maastricht. He is also a Senior Research Fellow at the Dutch School for Information and Knowledge Systems (SIKS), a Board Member at IPRally, and a Venture Partner at ENDEIT Capital. M.Sc. in Computer Science from Delft University of Technology Ph.D. in Computational Linguistics from University of Amsterdam His research expertise spans Natural Language Processing, Machine Learning, and Artificial Intelligence, with applications in legal, medical, business, and regulatory domains. He focuses on text mining, machine translation, question-answering systems, and information extraction from unstructured data. Recent publications (2024–2025) emphasize context-aware machine translation, misinformation detection in recommendation systems, healthcare data analysis, and food science applications. Key themes include integrating deep learning architectures (e.g., Transformers), optimizing search and translation efficiency, and leveraging hybrid human-machine approaches. He has collaborated widely in industry and academia, notably deploying e-discovery software for institutions like the UN War Crimes Tribunals and FBI-ENRON. His career history includes leadership roles at ZyLAB (1987–2021) and prior service in the Royal Dutch Navy.

Abel Jansmaمشاهده پروفایل

پژوهشگر ارشد

• Complexity
• Information Theory
• Statistical Physics
• +۷ مورد دیگر

Abel Jansma is a Research Fellow at the Dutch Institute for Emergent Phenomena (DIEP), affiliated with the University of Amsterdam's Institute of Physics (IoP) and Institute of Logic, Language, and Computation (ILLC). His work bridges physics, mathematics, and biology to study emergence, higher-order interactions, and complexity. He holds a unique interdisciplinary background, transitioning from art school to scientific research while maintaining creative projects like origami design and AI culinary applications. Research interests include quantitative mereology, information theory, and causal modelling, with a focus on decomposing systems into parts and interactions. Notable contributions involve applying Möbius inversion techniques to study higher-order effects in complex systems. He explores topics like entropy decomposition, coarse-graining, and interdisciplinary frameworks linking category theory with physics. Additional projects span art-science interfaces: creating Penrose tiling visualizations, developing AI cooking assistants (Autolenghi), and collaborating on bio-design projects (mycorrhizal transplantations). His work often emphasizes visual and conceptual clarity, with outputs ranging from mathematical blogs to physical installations. Active on social media platforms including Twitter and GitHub, he engages in open science and public communication.