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Hans van LintView profile

Professor

• Traffic Simulation
• Transportation Engineering
• Data Analytics
• +3 more

Prof. Hans van Lint is a Professor of Traffic Simulation and Computing at Delft University of Technology (TU Delft), where he holds the Anthony van Leeuwenhoek Chair since 2013. He is affiliated with the Department of Transport & Planning within the Faculty of Civil Engineering and Geosciences. His research focuses on the intersection of traffic flow theory, data analytics, and traffic simulation, with applications in estimating and predicting traffic states in networks. He has supervised numerous PhD students and contributed to valorization projects translating research into practical solutions. Van Lint earned his MSc in Civil Engineering in 1997 and returned to TU Delft for his PhD, which he completed in 2004 on 'Freeway Travel Time Prediction.' He has held roles including Assistant Professor (until 2009), Associate Professor, and has served as Director of Education for the MSc Transport, Infrastructure and Logistics program from 2010–2016. His research interests include traffic simulation frameworks, data assimilation techniques, and the development of tools for traffic state estimation. He has authored influential papers on topics such as microscopic traffic modeling, congestion pattern analysis, and macroscopic fundamental diagrams. His work emphasizes bridging theoretical models with real-world applications, enhancing traffic management and infrastructure planning. Van Lint teaches courses like 'Transport & Planning' and 'Interdisciplinary Fundamentals,' reflecting his commitment to both research and education. He actively contributes to TU Delft's labs, including the Traffic Dynamics, Modelling and Control Lab, advancing interdisciplinary approaches to mobility challenges.

Achilleas PsyllidisView profile

Assistant Professor

• Accessibility Measures
• Sustainable Urban Mobility
• Land-use and Transport Interaction
• +8 more

Achilleas Psyllidis is an Assistant Professor of Urban Mobility and Director of the Urban Analytics Lab at TU Delft. He also leads the Social Urban Data Lab at Amsterdam Institute for Advanced Metropolitan Solutions and is affiliated with the LDE Centre for BOLD Cities. His roles include membership in TU Delft's Transport & Mobility Institute, the Mobility Futures Vision Team, and serving on the Executive Board of CUPUM. Education: PhD in Spatial Data Science (TU Delft, Faculty of Architecture and the Built Environment) Master of Science in Spatial Planning (National Technical University of Athens) Engineering Diploma in Architectural Engineering (National Technical University of Athens) Research Interests: Focuses on accessibility, walkability, land-use dynamics, and travel behavior. Develops computational methods for analyzing access equity, spatial segregation, and human mobility. Leads projects on sustainable urban mobility, environmental exposures, and the 15-minute city concept. Awards: CTwalk Map: Best Demo Award (ICT.Open 2024) ROUTE Ontology of Urban Transportation Entities (2015) Grants & Projects: Involved in initiatives like PERISCOPE (Social Resilience Design), Horizon2020 'Equal-Life' (Environmental Health), and SocialGlass (Urban Analytics Dashboard). Active in research collaborations across Europe and Asia. Labs & Teams: Directs Urban Analytics Lab and Social Urban Data Lab, focusing on data-driven urban solutions. Engages in interdisciplinary teams addressing mobility futures, urban health, and sustainable design.

Asier MonevaView profile

Researcher

• Criminology
• Cybercrime
• Environmental Criminology
• +4 more

Dr. Asier Moneva is a Postdoctoral Researcher at the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR) and The Hague University of Applied Sciences , specializing in cybercrime , environmental criminology , and situational crime prevention . His work focuses on offender decision-making in cyberspace, cybercrime victimization patterns, and the application of data science to crime analysis. Education : PhD in Criminology (2020), Master in Crime Analysis and Prevention (cum laude, 2017) from Miguel Hernández University. Current Role : Analyzing cybercrime patterns through environmental criminology frameworks and data science methodologies. Moneva's research examines longitudinal offending patterns in cybercrime, particularly through analyses of web defacement archives ( Zone-H data) and hacker behavior. His studies reveal extreme concentration of cybercrime among chronic offenders, with 2.9% of hackers responsible for 68.5% of defacements. He also investigates repeat victimization dynamics in digital environments and the effectiveness of warning banners as deterrents. Recent publications focus on ransomware payment decisions by SMEs, stolen data markets on Telegram, and the intersection of familial relationships with cybercrime involvement. His work combines quasi-experimental designs , crime scripting , and conjunctive analysis to develop prevention strategies.

• Stochastic Processes
• Queueing Theory
• Probability Theory
• +7 more

Michel Mandjes is a Professor at the University of Amsterdam's Faculty of Science and holds a Visiting Professor position at the Faculty of Economics and Business (FEB). His research focuses on stochastic processes, queueing theory, and probability theory, with applications in risk modeling, network analysis, and operations research. Recent publications highlight his contributions to multivariate Hawkes processes , Lévy-driven systems , and dynamic random graphs , emphasizing large deviations, rare event simulation, and statistical inference. His work bridges theoretical probability with practical challenges in traffic flow, financial risk, and social network modeling. The trends in his research include the development of stochastic models for network stability, appointment scheduling optimization, and inference techniques for non-stationary processes. His methodological innovations often leverage advanced probability theory and queueing frameworks to address real-world problems in transportation, healthcare, and finance.

• Human-Algorithm Interaction
• AI Ethics
• Data-Driven Design
• +3 more

Dr. Dave Murray-Rust is an Associate Professor in Human-Algorithm Interaction Design at TU Delft's Faculty of Industrial Design Engineering. He explores the intersection of humans, data, and AI through design research, focusing on ethical AI systems and sociotechnical interactions. His work bridges computer science, design theory, and digital sociology, addressing challenges like algorithmic fairness and human-AI collaboration. He leads initiatives such as the AI Futures Lab and Data-Centric Design Lab, advancing methods for leveraging behavioral data in design processes. His research emphasizes experiential AI frameworks, metaphors for designers, and the legibility of AI systems. He has been honored with awards including Best alt.HRI 2024 and a CHI 2023 Best Paper Award for contributions to fairness perceptions in algorithmic decision-making. Murray-Rust teaches courses like the Speculative Design Studio and collaborates on projects like DCODE (Designing the Future of AI) and the BrightSky Project. His work extends to public engagement through installations like GeoPact and explorations of blockchain's societal impact. He holds an Honorary Fellowship at the University of Edinburgh.

Monika KufferView profile

Professor

• Urban Remote Sensing
• Urban Planning
• Deprived Areas
• +6 more

Monika Kuffer is a Full Professor at the University of Twente's Faculty of Geo-Information Science and Earth Observation (ITC), holding additional roles as Associate Professor in the Department of Urban and Regional Planning and Geo-Information Management. She leads research in urban remote sensing, deprived area monitoring, and sustainable urban development. Her work integrates spatial statistics, machine learning, and citizen science to inform inclusive city planning. Education: PhD in Human Geography & GIS from the University of Twente MSc in Human Geography (TU Munich) MSc in Geographic Information Science (University of London) Research Interests: Urban Remote Sensing Slum and Deprivation Mapping Climate Adaptation Strategies Citizen Science for Urban Inequalities Earth Observation Policy Support Articles Trends: Recent work emphasizes multi-city climate adaptation analyses, thermal inequality assessments in African slums, and scalable deprivation modelling frameworks like IDEAMAPS. Projects like ONEKANA and NightWatch highlight fusion of EO data with community-driven methods. Awards: 2022 EO4all Prize for innovative Earth Observation applications Advising & Grants: Supervised 3 PhD/MSc projects. Active in global initiatives like the EU's Knowledge Centre on EO and the UN's SDG frameworks. Leads datasets on deprivation (e.g., IDeAMapSudan). Labs/Teams: Core member of ITC's Urban Remote Sensing and GeoAI teams. Collaborates with the Digital Society Institute for interdisciplinary urban research.

Jaap ZevenbergenView profile

Professor

• Land Administration
• Geo-Information Management
• Property Rights
• +5 more

Jaap Zevenbergen is a Full Professor at the University of Twente's Faculty of ITC, specializing in Land Administration and Geo-Information Management. He holds a PhD from Delft University of Technology (2002) and degrees in Geodetic Engineering (Delft) and Law (Leiden University). His research focuses on international land governance, digital transformation of property institutions, and pro-poor land tools, with projects in Africa, Southeast Asia, and Eastern Europe. He has contributed to UN Habitat initiatives and World Bank programs, emphasizing sustainable development goals (SDGs 1, 11, 13). Key roles: Theme leader at TU Delft's OTB Institute (2003–2010), Portfolio Manager for MSc Land Administration at ITC. Teaching: MSc programs in Land Administration, GIMA, and International Land Management. Research interests include: Land Administration Domain Model (LADM) implementation, legal-technical integration in geo-ICT systems, and post-disaster/post-conflict land governance. He has authored/co-authored over 340 publications and edited books like Real Property Transactions . Notable achievement: Co-winner of the 2018 FIG-Survey Review Prize for land policy analysis. Current projects explore 3D cadastral systems, UAV applications in land registration, and ethical geospatial practices. He advises on land reforms in Greece and Egypt and collaborates with institutions like UN Habitat and the World Bank.

Erik QuaeghebeurView profile

Assistant Professor

• Probability Theory
• Statistics
• Uncertainty in Artificial Intelligence
• +4 more

Erik Quaeghebeur is an Assistant Professor at Eindhoven University of Technology's School of Mathematics and Computer Science, focusing on uncertainty modeling in artificial intelligence. His work spans probabilistic circuits, imprecise probability theory, and wind energy applications. PhD in Applied Mathematics (Ghent University, 2002-2009) Master's in Applied Mathematics (Université catholique de Louvain, 2001-2002) Master's in Physics Engineering (Ghent University, 1998-2001) Research interests include probabilistic modeling under uncertainty, with applications in AI and wind energy systems. His recent work explores tensor factorizations, equivariant graph neural networks, and scalable probabilistic circuits. Scientific contributions include 60 research outputs and 2 datasets . Awards encompass the ERCIM Alain Bensoussan Fellowship (2013), BOF Postdoc (2010), and B.A.E.F. Francqui Fellowship (2009). He serves on committees for the Society for Imprecise Probability and acts as editorial board member for related conferences. Foundations of Artificial Intelligence course (since 2020) Uncertainty Representations and Reasoning course (since 2021)

Judith VerstegenView profile

Assistant Professor

• Geographic Information Science
• Spatial Modelling
• Agent-Based Modelling
• +4 more

Dr. Judith Verstegen is an Assistant Professor in the Department of Human Geography and Spatial Planning at Utrecht University's Faculty of Geosciences. Her research focuses on geosimulation modeling and spatial optimization, with applications in urban planning, environmental vulnerability assessment, and policy analysis. She leads projects such as HEADS 4 Health (2023-2024), which integrates agent-based models into urban digital twins, and coordinates the GeoSIM research group. Her work emphasizes interdisciplinary collaboration, including projects analyzing linguistic diversity in South America and environmental threats to Amazonian indigenous lands. She is the Program Chair of the MSc Geographical Information Management and Applications (GIMA) program and serves as Editor-in-Chief of the Journal of Spatial Information Science. Notable contributions include methodologies for spatial optimization under uncertainty and agent-based modeling of pedestrian behavior in urban environments. Key research areas include applied data science, complex systems analysis, and the PtS - Transforming Cities initiative. She has advised PhD students on topics ranging from fire prevention optimization to indigenous land vulnerability. Her lab at the University of Münster previously focused on spatial modeling frameworks, and she collaborates internationally with institutions like Leiden University and the PBL Netherlands Environmental Assessment Agency. Recent projects highlight innovation in computational methods, such as Python-based open-source tools for land-use modeling (IMAGE-land) and immersive video experiments for behavioral studies. Her work bridges theoretical modeling with practical policy applications, addressing challenges in sustainable urban development and environmental conservation.

Arno SiebesView profile

Professor

• Data Mining
• Pattern Mining
• Minimum Description Length Principle
• +2 more

Arno Siebes is Professor of Algorithmic Data Analysis in the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His research focuses on data mining methodologies, particularly pattern mining and Minimum Description Length (MDL) principles. Key research areas include: Developing efficient algorithms for pattern discovery Applying MDL to data characterization Creating interpretable models for complex datasets Addressing challenges in data science education Recent publications demonstrate applications in diverse domains including mobility analysis, genomic screening, and pandemic response. His work combines theoretical foundations with practical implementations for knowledge discovery.

• High-dimensional probability theory
• Data Science
• Machine Learning
• +5 more

Sjoerd Dirksen is a Professor of Mathematics for Data Sciences at Utrecht University since May 2025, having previously served as an Associate Professor for Applied Mathematics (2019-2025) and Junior Professor at RWTH Aachen University (2014-2019). He is affiliated with the Mathematical Institute within the Faculty of Science at Utrecht University, where his office is located in the Hans Freudenthal Building. His research interests focus on high-dimensional probability theory and its applications in data science, machine learning, and signal processing. Specifically, he investigates randomized data dimension reduction methods using structured random matrices, theory for deep learning including random neural networks, high-dimensional covariance estimation for wireless communication systems, and statistical postprocessing of weather forecasts in collaboration with the Royal Netherlands Meteorological Institute (KNMI). Previously, he worked on compressed sensing, sharp estimates for stochastic processes in Banach spaces, and noncommutative analysis. Analysis of his recent publications (2018-2024) reveals a strong focus on quantization effects in high-dimensional data processing, particularly one-bit compressed sensing and covariance estimation under coarse quantization. His work bridges theoretical mathematics with practical applications in signal processing, wireless communications, and meteorological forecasting, demonstrating a consistent trajectory from foundational mathematical research to applied data science problems. Dirksen's academic career shows progression from postdoctoral work at the Hausdorff Center for Mathematics in Bonn to independent research positions. His publication record demonstrates significant contributions to the mathematics of data science, with papers appearing in top journals across mathematics, statistics, and signal processing. His research combines deep theoretical insights with practical applications, particularly in the areas of dimensionality reduction and high-dimensional statistics.

• Hydrogeology
• Groundwater
• Stochastic Modelling
• +16 more

Dr. Alraune Zech is an Assistant Professor for Computational Environmental Hydrogeology at Utrecht University, Faculty of Geosciences, Department of Earth Sciences, Hydrogeology group. Her research focuses on groundwater flow and transport processes in heterogeneous environments, with emphasis on practical applications in contaminated aquifers, PFAS remediation, and construction-related groundwater issues. She is affiliated with the Helmholtz-Centre for Environmental Research and serves as convener for EGU sessions on contaminant transport. Dr. Zech's educational background includes: PhD in Computational Hydrosystems from Friedrich-Schiller-University Jena and Helmholtz-Centre for Environmental Research (2010-2013) Prediploma Degree in Business Mathematics from University Leipzig (2011) Diploma in Mathematics with minor in Chemistry from University Leipzig (2003-2009) Her research interests span hydrogeology, groundwater modeling, and environmental remediation. She specializes in stochastic and computational modeling of subsurface processes, with focus on contaminated aquifers, PFAS removal strategies, biodegradation, bioremediation, and heat transport in the subsurface. Her work bridges theoretical approaches with practical applications in construction engineering and environmental protection. Analysis of Dr. Zech's recent publications (2021-2025) reveals strong focus on advancing hydrogeological modeling techniques, particularly in aquifer heterogeneity characterization, machine learning applications in hydrogeology, and practical remediation strategies. Her work spans fundamental research on pore-scale processes to field-scale applications, with increasing integration of artificial intelligence methods for solving complex groundwater problems. Dr. Zech leads several significant research projects: Living Lab PFAS Remediation (2024-2028): Developing strategies for PFAS contamination SilPit (2024-2028): Studying erosion of silicate grouting in construction pits MIBIREM (2022-2027): Creating innovative technological toolbox for bioremediation She actively supervises multiple PhD students including Alexandra Hockin, Kim Bartsch, Mahammad Valibeknejad, Sona Aseyednezad, Hannah Gebhardt, and Martijn van Leer. Her research is supported by funding from the Ministry of Infrastructure and Water Management, NWO, and EU Horizon programs.

Norman KerleView profile

Professor

• Disaster Risk Management
• Remote Sensing
• Geoinformatics
• +6 more

Norman Kerle is a Professor at the Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente, holding the chair of Geoinformatics for Disaster Risk Management within the Earth Systems Analysis department. He earned Masters degrees in geography from the University of Hamburg and Ohio State University, and a PhD in volcano remote sensing from the University of Cambridge (2002). His research spans volcanology, landslide detection, and quantitative geomorphology, with a focus on object-oriented remote sensing methods for disaster risk management. He leads the ITC Object-Based Image Analysis research group and coordinates EU-funded projects like RECONASS and INACHUS , emphasizing UAV-based structural damage mapping. Recent work includes post-disaster recovery assessment using remote sensing and macro-economic modeling. His scientific contributions include over 221 research outputs (peer-reviewed articles, book chapters, conference papers) and datasets such as Evaluating Resilience-Centered Development Interventions with Remote Sensing (2020). He has received the 2011 Lloyd's Science of Risk Prize (Natural Hazards) and served as Associate Editor for journals like Remote Sensing and Natural Hazards and Earth Systems Sciences . Prof. Kerle’s professional affiliations include the European Geosciences Union (EGU), American Geophysical Union (AGU), International Society for Photogrammetry and Remote Sensing (ISPRS), and Remote Sensing and Photogrammetry Society (RSPS). He has examined PhD theses and reviewed proposals for Horizon 2020, STEREO, and UNESCO.

Mehdi DastaniView profile

Professor

• Artificial Intelligence
• Multi-Agent Systems
• Formal Methods in AI
• +5 more

Prof. Mehdi Dastani is a Professor and chair of the Intelligent Systems group within the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. He leads the Master's program in Artificial Intelligence and focuses on formal and computational models in AI, particularly multi-agent systems. His research integrates insights from philosophy, psychology, and law to develop autonomous agents that reason about social and cognitive concepts like norms, emotions, and responsibility. Dastani has held academic roles at Utrecht University since 2001, including postdoctoral research and faculty positions. Education: M.Sc. Computer Science (University of Amsterdam, 1991), M.Sc. Philosophy (University of Amsterdam, 1992), Ph.D. in Humanities (University of Amsterdam, 1998). His work spans theoretical and applied projects, including grants for initiatives like Golden Agents (simulating Golden Age creative industries) and traffic control systems using virtual organizations. He is actively involved in academic committees, editorial boards, and organizing international conferences like AAMAS and PRIMA. Research Interests: Multi-Agent Programming, Normative Systems, Autonomous Agents, Cognitive Robotics, and Human-Centered AI. His projects address challenges like norm enforcement, decision-making in complex systems, and ethical AI integration with societal needs. Advising & Grants: Supervised numerous PhD students (e.g., Birna van Riemsdijk, Bas Testerink) and secured grants for projects such as 'Controllable AI: Human-Centered Approach'. His work includes collaborations on urban governance, autonomous driving, and AI tools for literacy support in children. Labs & Teams: Leads the Intelligent Systems group, contributing to agent-based simulations, ethical AI frameworks, and interdisciplinary collaborations with social scientists and urban planners.

Evert van NieuwenburgView profile

Assistant Professor

• Quantum Physics
• Machine Learning
• Condensed Matter Physics
• +6 more

Dr. Evert van Nieuwenburg is an Assistant Professor at Leiden University, affiliated with both the Leiden Institute of Advanced Computer Science (LIACS) and the Leiden Institute of Physics (LION). His research bridges the fields of Quantum Physics , Machine Learning , and Condensed Matter Physics , with a focus on quantum algorithms, reinforcement learning, and quantum game development (e.g., Quantum TiqTaqToe ). He actively contributes to the Applied Quantum Algorithms (aQa) initiative and leads the QuantumPlayed subgroup for quantum games and education. Research Interests: AI-driven quantum experiment control, quantum machine learning, variational quantum circuits, and quantum games for education and intuition-building. Publications: 15+ peer-reviewed works spanning quantum error correction, phase transitions, reinforcement learning in quantum systems, and quantum dot array simulations. Community Engagement: Developer of educational quantum games, open science advocate, and active participant in interdisciplinary initiatives. Selected Trends: His work demonstrates AI's transformative role in quantum physics, from decoding error-correcting codes with graph neural networks to merging reinforcement learning with quantum control systems. Labs & Initiatives: Affiliated with the Applied Quantum Algorithms (aQa) initiative and co-founder of QuantumPlayed , where quantum mechanics meets game theory to engage diverse audiences.