Prof. Sander Bohte is a part-time full professor of Computational Neuroscience at the University of Amsterdam (Swammerdam Institute of Life Sciences) and an honorary full professor of Bio-inspired Neural Networks at the University of Groningen. He serves as a Scientific Staff Member and Group Leader in the Machine Learning department at CWI, Amsterdam. His work bridges computational neuroscience and machine learning with a focus on continuous-time information processing. Key research interests include: Spiking Neural Networks with predictive coding and multi-compartment models Biologically Plausible Learning in recurrent and deep architectures Working Memory modeling via reinforcement learning Neuromorphic Computing for real-time systems and GPU acceleration His recent publications highlight trends in neural adaptation , predictive coding , and SNN hardware-software co-design . Awards include the Veni Innovational Research Grant (2004) and ERCIM grant (2013) . He actively supervises MSc theses and leads grants like the NWO KIC project 'Selfhealing Neuromorphic Systems' (2024).
Prof. Geert-Jan P.M. Houben is a Professor at Delft University of Technology's Web Information Systems Department within the Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on AI ethics, machine learning, data integration, and decision support systems. He has published over 165 works and supervised 24 students. Notable contributions include frameworks for meaningful human control in AI systems and methodologies for bias mitigation in data-driven systems. Editorial roles: Editor for CEUR-WS, Springer, and other publishers since 2012 Awards: Royal Honours from TU Delft (2025) Research emphasizes ethical AI, data engineering, and scalable systems. Recent work addresses AI governance, bias in analytics, and interactive data discovery in modern data ecosystems. He actively contributes to conferences like ACM Web Science and VLDB.
Dr. Cor Verdouw is a Senior Researcher in Information Technology and Data Science, focusing on innovation in agri-food systems. He is affiliated with Wageningen University & Research (WUR), though the specific school/department isn't explicitly stated. Research Interests : Digital Twin architectures for horticulture and pharmaceutical production Blockchain applications in agri-food supply chains IoT systems for smart farming Transparency and traceability frameworks Knowledge engineering in food industry contexts Organizational design for digital innovation ecosystems Selected Article Trends : His 2023-2025 work spans digital twins (cannabis/pharma production), blockchain (food supply chains), and IoT (smart agriculture). Key themes include system integration, circular economy models, and data-driven decision-making. Projects : Leads EU-funded STREAMING project (2025-) and LVVN projects on AI in agri-food systems (2025-2028), data-driven fisheries (2024-2025), and information management (2016-2025).
Tiziano De Matteis is an Assistant Professor in the @Large Research group at Vrije Universiteit Amsterdam's Faculty of Science, Department of Computer Systems. He also holds an affiliation with the Network Institute. His research focuses on overcoming post-Moore architecture challenges through parallel and distributed computing, high-performance systems, energy efficiency, and FPGA applications. Previously, he was a PostDoc at ETH Zurich's SPCL Group and earned his MSc/PhD from the University of Pisa. Education PhD in Computer Science, University of Pisa MSc in Computer Science, University of Pisa Research Interests Post-Moore architectures for distributed ecosystems Energy-aware parallel computing High-level abstractions for parallel software development FPGA-based hardware acceleration Data stream processing and distributed systems Recent Research Trends Recent work emphasizes: Data center risk analysis and sustainability Optimizing microservices and distributed scheduling LLM model offloading to NVMe storage Python-based data-centric programming productivity GPU interconnect performance in supercomputing Grants & Projects Participates in the EU-funded 'Extreme and Sustainable Graph Processing' project (2023-2025), exploring scalable graph algorithms and energy-efficient computing systems. Teaching Accelerator-Centric Computing Ecosystems Computer Organization Distributed Systems Systems Seminar
Prof. Peter Groot Koerkamp is a Professor and Managing Chairholder at the Agricultural Biosystems Engineering group at Wageningen University. His expertise spans agricultural engineering, environmental systems, and sustainable livestock production. He holds an MSc (1990) and PhD (1998) from Wageningen University, with a focus on ammonia emissions from poultry systems. He has held roles as a researcher, project manager, and senior scientist across multiple institutes before joining Wageningen University in 2005. His research emphasizes sustainable agricultural systems, including manure/nutrient management, gaseous emissions, and animal welfare. He leads projects on methane monitoring in dairy cows, climate control in livestock housing, and bioenergy from segregated excreta. He supervises over 50 PhD students and collaborates on initiatives like the National Growth Fund’s regenerative agriculture projects. Notable projects include Synergia (optimizing dairy systems) and collaborations with the European Society of Agricultural Engineers (EurAgEng). His work integrates environmental engineering with technological innovation, addressing challenges like particulate matter reduction in poultry houses and precision livestock farming.
Hanna Surma is an Assistant Professor in Media Studies at the Department of Media and Culture Studies, Faculty of Humanities, Utrecht University. Her work bridges television theory, reality television analysis, and data-driven creative practices in media production. Research focuses on governmentality studies, media and subjectivation, and the transformation of television production through viewer data. Co-coordinates the MA Film and Television Cultures program and teaches courses on media in transition and research methods. Recent publications include a 2022 chapter on data-driven script development for streaming services. She organizes academic workshops like Let's Talk Screenwriting and investigates the intersection of algorithms, AI, and screenwriting practices in Europe. Her scientific award includes earning a Ph.D. summa cum laude from Ruhr-Universität Bochum.
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.
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
Dr. Susanne Baumgartner is an Associate Professor in the Faculty of Social and Behavioural Sciences at the University of Amsterdam, specializing in Youth & Media Entertainment. Her research examines the effects of digital media on attention, well-being, and development. Her work investigates: Media multitasking and attention processes Smartphone usage patterns and effects on sleep Digital stress and well-being interventions Adolescent development in digital environments Longitudinal effects of media use Dr. Baumgartner's current research employs mobile tracking, experience sampling, and experimental methods to understand how digital behaviors affect psychological functioning. Her work on notification-disabling interventions and grayscale smartphone displays examines practical approaches to digital well-being. She contributes to the Centre for Research on Children Adolescents and the Media and the Digital Communication Methods Lab, developing innovative approaches to studying digital behaviors in everyday life contexts.
Dominique Wirz is an Assistant Professor in the Communication Science department at the University of Amsterdam's Faculty of Social and Behavioural Sciences. Her research examines media effects, political communication, and entertainment experiences, with a focus on social media platforms and emotional responses to media content. Her work investigates hate speech perception, infotainment strategies on Instagram/TikTok, psychologically rich entertainment, and binge-watching behaviors. Current projects explore the intersection of negative emotions and populist communication effectiveness. Recent publications analyze how news organizations adapt content for social media, psychological dimensions of entertainment experiences, and motivational factors in media consumption patterns.
Walter G.J. van der Meer is a full Professor at the Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology. He is a leading researcher in membrane technologies, water treatment, and sustainable water systems, with over 100 research outputs and an h-index of 36. His work focuses on reverse osmosis, filtration, PFAS removal, and micropollutant management in potable and wastewater systems. His research interests lie at the intersection of environmental and chemical engineering, particularly in advancing membrane-based separation processes. Key areas include reverse osmosis modeling, adsorption mechanisms, brine management, and the removal of emerging contaminants such as perfluoroalkyl substances (PFAS). His work integrates experimental studies with theoretical modeling to improve efficiency and sustainability in water treatment. Recent publications (2020–2025) reveal a strong trend toward sustainable water solutions, with emphasis on PFAS remediation, bio-based adsorbents, energy-efficient desalination, and wastewater reuse. His research combines material science, process engineering, and environmental chemistry, contributing to both fundamental understanding and practical applications in water infrastructure. Walter van der Meer has secured significant research impact through extensive collaboration, particularly within the Dutch 4TU federation. He has co-supervised at least 10 student projects and contributes to open science through public data repositories. His work is frequently published in high-impact journals such as Desalination , Water Research , and ACS ES&T Water .
Maris Ozols is an Assistant Professor at the University of Amsterdam and a researcher at QuSoft, affiliated with the Algorithms and Complexity department at Centrum Wiskunde & Informatica (CWI). His primary research focuses on quantum algorithms and quantum information theory, with significant contributions to quantum complexity, quantum cryptography, and quantum state discrimination. His research interests span quantum algorithms, quantum information theory, quantum cryptography, quantum complexity, and theoretical computer science. Ozols has developed fundamental techniques in quantum query complexity, quantum state discrimination, and quantum cryptographic security models. His work often bridges theoretical computer science with quantum information physics, demonstrating practical implications for quantum computing architectures. His publication record shows consistent output in top venues including Communications in Mathematical Physics, Leibniz International Proceedings in Informatics, and Quantum journal. Recent work (2022-2025) focuses on quantum state discrimination, quantum circuit optimization, quantum machine learning, and cryptographic applications of quantum algorithms. His research demonstrates strong theoretical foundations with practical implications for quantum computing development. Leverhulme Early Career Fellow (University of Cambridge) Ozols has secured research funding through multiple Netherlands Organisation for Scientific Research (NWO) grants including the Quantum Software Consortium (QSC) and Quantum Computation with Bounded Space projects. His collaborative work spans international institutions including the University of Waterloo (where he earned his PhD), University of Cambridge, IBM Research, and various European quantum computing groups. He maintains active research groups in quantum algorithms at both QuSoft and CWI, with recent focus on quantum machine learning applications and quantum cryptographic protocols.
Pablo Cesar is Professor holding the Human-Centered Multimedia Systems Chair at Delft University of Technology and leads the Distributed and Interactive Systems (DIS) group at Centrum Wiskunde & Informatica (CWI), the Netherlands' national research institute for mathematics and computer science. His work bridges academic research at TU Delft with applied innovation at CWI. Education: Doctorate (2005) from Helsinki University of Technology, Department of Computer Science and Engineering Master's (2002) from Universidad Politécnica de Madrid His research pioneers human-centered multimedia systems focusing on modeling and controlling distributed media collections across time and space, integrating real-time media streams and sensor data to enable novel remote collaboration scenarios. This work directly addresses challenges in distributed human interaction through system-level innovations in media orchestration. Scientific Awards: 2020 Netherlands Prize for ICT Research (recognized for scientific impact and communication) ACM Distinguished Member (top 10% of ACM members) IEEE Senior Member (top 10% of IEEE members) No specific student advising details or grant funding information appears in the source text, though his leadership of the DIS research group implies supervisory responsibilities. His inaugural lecture 'Human-Centered Multimedia: Making Remote Togetherness Possible' (May 20, 2022) featured a dedicated two-day symposium highlighting his research vision. The DIS group at CWI serves as his primary research laboratory, driving innovation in distributed interactive systems with applications spanning remote collaboration, telepresence, and context-aware media environments.
Rob Basten is an Associate Professor in the Department of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology (TU/e). He has been with TU/e since October 2014, initially as an Assistant Professor before being promoted. His work focuses on operations management and behavioral operations management, with particular expertise in maintenance, spare parts supply, and after-sales services for high-tech equipment. Basten's research is highly applied, often conducted in collaboration with industry partners such as ASML, NXP, Canon Production Printing, Marel Poultry, and the Ministry of Defence. Dr. Basten's educational background includes: Master's in Industrial Engineering and Management (2004) from University of Twente Master's in Computer Science (2005) from University of Twente PhD in Operations Management (2010) from University of Twente Rob Basten's research centers on improving after-sales services for high-tech equipment, with a focus on incorporating new technologies such as 3D printing and IoT. His work spans both analytical and empirical approaches, with increasing emphasis on behavioral operations management as human decision-makers interact with AI-based decision support systems. Basten investigates how to design and control after-sales service supply chains when spare parts can be 3D printed, and how to optimize maintenance policies for complex systems. His interdisciplinary research bridges operations management, maintenance engineering, and human behavior. Analysis of Basten's recent publications reveals a strong focus on the application of new technologies in after-sales services. Key trends include the integration of additive manufacturing in spare parts supply chains, condition-based and predictive maintenance driven by Industry 4.0 technologies, and the behavioral aspects of human-AI collaboration in maintenance decision-making. His work frequently addresses challenges in high-tech manufacturing contexts, particularly semiconductor equipment, with a growing emphasis on Industry 5.0 concepts that integrate human-centered approaches with advanced technologies. Dr. Basten has received recognition for his work, including: Finalist for the 2020 Daniel H. Wagner Prize for Excellence in Operations Research Practice ISIR Best Student Paper Award 2018 Rob Basten actively supervises numerous PhD students and has led several major research projects. He currently supervises nine PhD students including Maryam Azani, Ragnar Eggertsson, Bibi de Jong, Zhao Kang, Niccolò Maccarini, Aran Nasiri, Bas van Oudenhoven, İpek Tanıl, and Alireza Yazdani. He has successfully guided six PhD students to completion. Basten has been project leader and work package leader in significant research initiatives including ProSeLoNext (funded by NWO with industry co-funding), PrimaVera, SINTAS, and OCPROM projects. His research is consistently supported by both public funding agencies and industry partnerships, reflecting the practical relevance of his work. Basten is an active member of the Operations, Planning, Accounting & Control group at TU/e and contributes to the EAISI High Tech Systems initiative. He has organized key academic events including the first two editions of the Maintenance Research Day and the Behavioral Operations Conference 2019. His work connects academic research with industry practice through ongoing collaborations with leading high-tech companies, creating a dynamic research environment focused on solving real-world challenges in maintenance and service logistics.
Dr. V. Menkovski serves as an Associate Professor in Data Mining at Eindhoven University of Technology's Department of Mathematics and Computer Science. He also holds associate professor positions with EAISI Health and EAISI High Tech Systems, and is an ICMS Affiliated member. His work spans multiple domains of artificial intelligence and computational physics, with significant contributions to fusion energy research. Mathematics and Computer Science, Data Mining (Primary Appointment) EAISI Health (Associate Professor) EAISI High Tech Systems (Associate Professor) ICMS (Affiliated Member) Menkovski's research focuses on Graph Neural Networks, Machine Learning, Deep Learning, and their applications in diverse fields from plasma physics to metamaterials. His work demonstrates strong interdisciplinary connections, particularly between computer science and fusion energy research. He has developed novel approaches for crowd simulation, tokamak plasma monitoring, and metamaterials homogenization using advanced neural architectures. His fingerprint reveals expertise in Quality-of-Experience, Autoencoders, Neural Networks, Annotation, Graph Neural Networks, Video Streaming, Adversarial Machine Learning, and Anomaly Detection. Analysis of his recent publications (2023-2025) shows a clear trend toward applying Graph Neural Networks to complex physical systems, particularly in fusion energy research and materials science. His work increasingly integrates symmetry principles with neural architectures, as seen in his research on equivariant networks for metamaterials and symmetry-informed networks for zeolite analysis. There's also significant focus on practical applications in fake news detection, anomaly detection, and plasma state monitoring. Best Paper Award ICPM 2021 (with Sommers and Fahland) Best Paper Award of LoG 2022 (with multiple co-authors including Huang, Chen, Fang, Zhao, Yin, Pei, Mocanu, Wang, Pechenizkiy, and Liu) Menkovski teaches several advanced courses including Deep Learning, Advanced Topics in Artificial Intelligence, and Sociophysics 2, which runs through August 2025. His supervised work portfolio includes 79 projects, indicating substantial mentorship activity. He has received significant media attention for his research, including coverage by 11 news outlets, blog posts, and mentions on social media platforms. His work on 'Supervised Learning of Process Discovery Techniques Using Graph Neural Networks' was particularly noted in media coverage. His research involves collaboration with multiple institutions and teams, particularly in fusion energy research (Eurofusion Tokamak Exploitation Team, ASDEX-Upgrade team, EUROfusion MST1 Team). He works closely with researchers across disciplines, including physicists working on tokamak plasma and materials scientists studying metamaterials and zeolites.
