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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.

• Digital Archaeology
• Computational Archaeology
• Machine Learning in Archaeology
• +13 more

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.

• Computational Linguistics
• Dialogue Systems
• Cognitive Science
• +3 more

Paul Boersma is a Professor of Phonetic Sciences at the University of Amsterdam within the Faculty of Humanities. His research explores how phonetic, phonological, and morphological phenomena emerge through computational modeling using artificial neural networks and Optimality Theory, with a focus on multi-level constraint interactions and distributional learning. University of Amsterdam Faculty of Humanities Phonetic Sciences Key research areas include: Computational Modeling : Simulations of phonological category emergence from phonetic data Optimality Theory : Gradual Learning Algorithm applications BiPhon Framework : Parallel bidirectional phonology/phonetics models Statistical Learning : Cross-situational and distributional learning mechanisms Recent publications emphasize: 2025: Inclusive speech recognition systems using Whisper model 2025: F0 ratio analysis for creaky voice diagnostics 2024: Prosodic clitics in child speech and checked tones in Shanghai Chinese 2023: Distributional learning in developmental language disorder contexts 2022: Substance-free phonological features and ghost segment phenomena He has also contributed extensively to the Praat software for phonetic analysis, with continuous updates since 1993.

• Phonetics
• Phonology
• Computational Linguistics
• +7 more

Nathan van de Wouw is a Full Professor at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e), affiliated with ICMS, EAISI Mobility, EAISI High Tech Systems, EAISI Foundational, and EIRES. He also holds an adjunct Full Professor position at the University of Minnesota and a part-time Full Professorship at Delft University of Technology. His research focuses on dynamics and control of mechanical systems, including mechatronics, robotics, smart manufacturing, energy systems, and networked control. He has supervised over 150 students and led numerous projects funded by industry partners like ASML, Philips, and Shell. Education: M.Sc. (with Honors) in Mechanical Engineering, TU/e (1994) Ph.D. in Mechanical Engineering, TU/e (1999) Research Interests: Nonlinear systems and control Model reduction and complexity analysis Data-driven and networked control strategies Applications in high-tech systems, autonomous vehicles, and energy systems Awards: IEEE Control Systems Technology Award (2015) for variable-gain control in motion systems Grants & Projects: Lead projects on mechatronic design, lithography systems, and thermodynamic optimization Collaborations with TNO, ASML, and industrial partners Labs & Teams: Member of TU/e’s Dynamics and Control group Affiliated with EAISI (Eindhoven AI Systems Institute)

• Dynamics and Control
• Mechatronics and Robotics
• Smart Manufacturing
• +6 more

Ralf Peeters is a Full Professor in Mathematics of Knowledge Engineering at Maastricht University's Faculty of Science and Engineering , Department of Advanced Computing Sciences. He serves as Vice-Dean of Research and Director of the STEM Graduate School, while leading the university's team at the inter-university research school DISC and co-chairing the Mathematics Centre Maastricht. Education: PhD in Mathematics (Free University, Amsterdam, 1994) Technical Mathematics (Delft University of Technology, 1988) Research Interests span applied mathematics, systems and control theory, signal/image processing, artificial intelligence, and biomedical engineering applications. His work bridges mathematical techniques with real-world challenges in healthcare and industrial systems. Recent Publications highlight advancements in deep learning for cardiac signal reconstruction, tensor-based signal decomposition, and recurrence plot analysis. These works integrate machine learning with clinical diagnostics, particularly in electrocardiographic imaging and arrhythmia characterization. Key Collaborations: Mathematics Centre Maastricht Dutch Mathematics Platform Dutch Institute of Systems and Control Leadership Roles: Vice-Dean of Research (FSE), Director of STEM Graduate School, Head of DISC-affiliated team, and Co-Chair of Mathematics Centre Maastricht. He has supervised over 25 PhD projects, emphasizing applied research across health and industrial domains.

• Signal and Image Processing
• Systems and Control Theory
• Mathematical Modelling
• +7 more

Maarten de Rijke is a Professor at the University of Amsterdam's Informatics Institute, leading the Information Retrieval Lab (IRLab). He specializes in information retrieval, machine learning, and recommendation systems, focusing on neural ranking models, fairness, and conversational search. His work bridges theory and practice, addressing challenges in reproducibility, robustness, and ethical AI. He supervises numerous PhD students and postdocs, including recent defenses by Barrie Kersbergen, Antonis Krasakis, and Vera Provatorova. His lab collaborates internationally, organizing events like SIGIR workshops and the Search Engines Amsterdam (SEA) meetup. Key awards include the Best Reproducibility Paper Award (2025) and Best Paper at WSDM 2021. Research interests span generative retrieval, adversarial robustness, and fairness in ranking. Notable projects include the FULTR dataset, FairDiverse toolkit, and studies on empathetic conversational systems. He actively promotes open science through reproducible methodologies and community-driven benchmarks.

• Information Retrieval
• Machine Learning
• Neural Ranking Models
• +4 more

Dr. Stevan Rudinac is a Researcher at the University of Amsterdam's Faculty of Economics and Business , Section Business Analytics . His work focuses on interactive learning systems and multimodal data analysis, particularly in urban contexts and multimedia modeling. Education: PhD in Multimedia and Information Retrieval from Delft University of Technology (2013). Research Interests: Stevan specializes in multimedia modeling , hypergraph learning , and interactive video search . He develops frameworks for scalable analysis of social networks, urban imagery, and large multimodal datasets, bridging machine learning with practical applications in city planning and financial social media. Recent Trends: His 2024-2025 publications highlight large language model optimization , diffusion model evaluation , and dynamic graph embedding for meme stocks. Collaborative projects include the CASTLE 2024 dataset and Exquisitor , a system for 100 million image exploration. Labs & Teams: He contributes to the Business Analytics group at UvA, collaborating with Prof. Marcel Worring and Dr. Björn Þór Jónsson. He co-organized the UrbanMM'21 workshop and participates in ACM Multimedia and MMM conferences.

• Multimedia Modeling
• Machine Learning
• Computer Vision
• +7 more

Prof. Alexandre Bonvin is a Professor of Computational Structural Biology and Scientific Director of the Bijvoet Center for Biomolecular Research at Utrecht University. He leads the Computational Structural Biology group, focusing on developing integrative computational methods to study biomolecular interactions. His work emphasizes predicting protein complex structures using experimental data and computational models, with applications in drug discovery and structural biology. Bonvin pioneered the HADDOCK software suite, a widely used platform for modeling biomolecular complexes. Education: Studied Chemistry at the University of Lausanne, obtained a PhD from Utrecht University (1993), followed by postdocs at Yale University and ETH Zurich. Joined Utrecht University in 1998, becoming a full professor in 2009. He has held leadership roles, including Director of Chemical Education and Vice Head of the Chemistry Department. Research Themes: Biomolecular interactions, computational modeling, NMR spectroscopy, and grid computing. He coordinates EU projects like WeNMR and BioExcel, advancing e-infrastructure for structural biology. His lab’s contributions include software tools like HADDOCK, DisVis, and PowerFit, serving thousands of researchers globally. Awards: 2006 VICI grant from NWO, HPC Innovation Award (2020). Over 275 peer-reviewed publications, including high-impact work on teixobactin mechanism and antibody modeling. Active in training through EMBO courses and mentoring postdocs/students.

• Computational Structural Biology
• Biomolecular Interactions
• NMR Spectroscopy
• +5 more

J.A. La Poutré is a Full Professor at Delft University of Technology and a Scientific Staff Member at CWI (Centrum Wiskunde & Informatica) in the Intelligent and Autonomous Systems group. He holds part-time positions and is involved in research on multi-agent systems and computational intelligence for smart energy systems, with a focus on market-based optimization and fairness in network congestion management. PhD in computer science (Utrecht University, 1991) MSc in mathematics (TU Eindhoven, 1986) His research spans algorithm design, game theory, and reinforcement learning applied to energy systems, particularly smart grids and market mechanisms. Recent publications address cybersecurity threats in power networks, auction-based energy trading, and AI integration in media sectors. The 2019–2025 publications highlight his work in combining game theory with smart grid optimization , covering topics such as topology attacks , demand-side bidding , and fair congestion management . Papers frequently involve reinforcement learning , metaheuristics , and market mechanisms . Scientific Awards : Best paper award at GECCO-2015 KNAW fellow (Utrecht University, 1991–1997) He has led funded projects like Computational Capacity Planning in Electricity Networks (STW program) and co-chairs the Commit2Data Energy division. La Poutré also serves as Vice President of ERCIM (European Research Consortium for Informatics and Mathematics).

• Multi-Agent Systems
• Computational Intelligence
• Smart Energy Systems
• +4 more

Karl Aberer is a full Professor for Distributed Information Systems at École polytechnique fédérale de Lausanne (EPFL) since 2000. From 2005 to 2012, he led the Swiss National Research Center for Mobile Information and Communication Systems (NCCR-MICS). Currently serving as Vice-President of EPFL responsible for information systems, he contributes to academic leadership while maintaining an active research profile. His research spans Distributed Systems Data Mining Machine Learning Social Computing Web Science Graph Neural Networks with recent work focusing on multimodal learning, federated unlearning, and social media analysis. He serves on the editorial boards of the VLDB Journal ACM Transactions on Autonomous and Adaptive Systems World Wide Web Journal and contributes to PeerJ Computer Science.

• Distributed Systems
• Data Mining
• Machine Learning
• +5 more

Dr. Carla Ruis is an Associate Professor in the Faculty of Social and Behavioural Sciences at Utrecht University . She serves as a researcher in Experimental Psychology and works as a Clinical Neuropsychologist in the Department of Neurology and Neurosurgery at University Medical Center Utrecht . Her academic contributions span neuropsychology, cognitive neuroscience, and clinical interventions. University: Utrecht University Faculty: Social and Behavioural Sciences Department: Psychology Academic Rank: Associate Professor Research Interests : Dr. Ruis focuses on cognitive functioning in neurological conditions, including: Neuropsychological assessment during awake brain surgery Relationships between cognition and survival in glioma patients Impact of affective touch on chronic pain Memory and executive function in diabetes and dementia Her recent publications (2024) explore intraoperative cognitive monitoring, left-right orientation in brain tumors, and pain modulation through touch in Parkinson's disease. She has contributed to 56+ peer-reviewed works. Advising and Leadership : Dr. Ruis acts as a PhD examination committee chair and year group trainer for clinical neuropsychology programs. She serves on editorial boards and accreditation committees.

• Neuropsychology
• Cognitive Functioning
• Awake Brain Surgery
• +4 more

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.

• Natural Language Processing
• Information Retrieval
• Text Mining
• +5 more

Chuan Meng is a researcher transitioning to a postdoctoral position at the University of Edinburgh's Natural Language Processing Group in September 2025. He obtained his PhD in Artificial Intelligence from the University of Amsterdam (UvA) in June 2025, supervised by Maarten de Rijke and Mohammad Aliannejadi. His research focuses on Information Retrieval and Natural Language Processing with Large Language Models , particularly in conversational agents, model/data-efficient neural ranking, and automatic evaluation techniques like query performance prediction. Education: PhD in Artificial Intelligence, University of Amsterdam (2025) MS in Computer Science and Technology, Shandong University (2021) BS in Electronic Commerce, Shandong Normal University (2018) His research interests span conversational search optimization, proactive agent development, knowledge-grounded dialogue systems, and query performance prediction using LLM-generated judgments. Recent work includes UniConv (unified retrieval/response generation), SOLID (intent-aware dialog generation), and QPP++ 2025 workshop organization. Key scientific contributions include 440+ Google Scholar citations (H-index: 13) and publications in premier venues like SIGIR , ACL , EMNLP , and TOIS . He has served as program committee member for SIGIR 2025/2024, ACL 2023, and other top conferences. As teaching assistant , he contributed to Information Retrieval and Natural Language Processing courses at UvA and Shandong University. His administrative roles include IRLab LinkedIn manager, webmaster for IRLab website, and seminar chair at UvA.

• Information Retrieval
• Natural Language Processing
• Conversational Search
• +6 more

Lennart Hoogerheide is an Associate Professor at the Department of Econometrics, School of Business and Economics, Vrije Universiteit Amsterdam, and a Research Fellow at the Tinbergen Institute. He earned his PhD from Erasmus University Rotterdam in 2006. His research focuses on Bayesian econometrics, Monte Carlo integration methods, and computational statistics, with applications in financial econometrics and instrumental variable models. Hoogerheide has developed influential R packages like MitISEM and AdMit for Bayesian analysis and importance sampling. He has received awards including the 'Best Senior Lecturer' (2015-2016) and 'Lecturer of the Year' (2009-2010). His work explores complex posterior distributions, non-elliptical contours, and robust inference techniques in econometric models. His recent research includes time-varying factor models, Bayesian mode inference for discrete distributions, and adaptive mixture methods. He has been awarded the VENI grant (NWO, 2009) and is recognized for contributions to computational Bayesian methods in economics and finance.

• Econometrics
• Bayesian Analysis
• Monte Carlo Methods
• +6 more

Catherine Sibert is a faculty member at the University of Groningen, affiliated with the Faculty of Science and Engineering in the Department of Artificial Intelligence. She holds the academic rank of Assistant Professor and is actively engaged in interdisciplinary research bridging cognitive science, neuroscience, and artificial intelligence. Her research interests include cognitive architecture, decision-making, neuroimaging, causal modeling, and brain activity, with a strong emphasis on data-driven approaches. She applies methods such as Granger causality and dynamic causal modeling to analyze multitask neuroimaging data and understand the neural underpinnings of cognition. The recent publications of Catherine Sibert demonstrate a consistent focus on modeling cognitive processes, particularly in decision-making and motor control, using computational and neuroimaging techniques. Her work often explores individual differences and the interaction between episodic and procedural memory systems. She collaborates with researchers such as Andrea Stocco and Hake H.S., contributing to high-impact journals like Topics in Cognitive Science and Frontiers in Neuroscience . Her research output is peer-reviewed, open access, and widely shared in academic networks. Decision Making Cognitive Architecture Neuroimaging Resting-State Networks Speech Motor Control Data-driven Modeling Catherine Sibert has no listed scientific awards in the provided text. She does not appear to have formal advisees listed, and there is no mention of grants or teaching responsibilities. However, her active publication record suggests ongoing research and collaboration within cognitive neuroscience and AI. She is associated with a research lab or team focused on cognitive modeling and neuroimaging, likely collaborating within the Artificial Intelligence department on projects involving brain-computer interfaces, cognitive systems, and computational modeling of human behavior.

• Cognitive Architecture
• Decision Making
• Neuroimaging
• +7 more