Federico Toschi is a Full Professor at Eindhoven University of Technology (TU/e), holding joint appointments in Applied Physics and Mathematics and Computer Science departments. His research focuses on multi-scale transport phenomena, combining statistical physics, fluid dynamics, and computational methods. He leads projects in the 4TU Centre for Multiscale Phenomena and EAISI. Education: PhD in Physics (University of Pisa, 1998) and academic background at Scuola Normale Superiore di Pisa. Interdisciplinary expertise in fluid dynamics turbulence, Lagrangian turbulence, crowd dynamics, and Lattice Boltzmann methods. Recipient of APS Fellow (2015), Euromech Fluid Mechanics Fellow (2012), and Ig Nobel Prize for Physics (2021). Research emphasizes turbulence modeling, pedestrian dynamics, and active matter, with applications in environmental flows and crowd management. His work bridges computational innovations with experimental validations. Recent articles explore kinetic data-driven turbulence modeling, pedestrian flow optimization, and turbulence effects in biological systems. Projects include digital twins for seismicity modeling and rarefied gas dynamics. Teaches fluid mechanics, computational physics, and chaos theory courses. Founded Flow Matters Holding BV, applying research to practical solutions.
Hans Bouwmeester is a Full Professor in Toxicology at Wageningen University & Research, specializing in chemical risk assessment, nanomaterial toxicity, and in vitro modeling. His work focuses on integrating artificial intelligence and physiologically based kinetic (PBK) models to predict toxic effects of contaminants like organophosphate pesticides, microplastics, and mycotoxins. He leads projects exploring the health impacts of foodborne contaminants in inflammatory bowel disease and developing animal-free testing methods. Key collaborations include EU initiatives like the ONTOX project and the GUTTEST program, which utilize advanced in vitro models such as gut-on-a-chip systems. Research interests span toxicokinetics, nanoplastics exposure, and the application of Bayesian networks for nanomaterial hazard ranking. His team addresses translational challenges in linking in vitro data to in vivo outcomes, with a focus on bile acid metabolism and cardiotoxicity prediction. Bouwmeester has supervised over 10 PhD candidates, including studies on microplastic hazard assessment, nanomaterial gastrointestinal fate, and AI-driven data extraction for risk assessment. Notable contributions include datasets on nanoplastics' protein corona effects and transcriptomic analyses of intestinal cell responses. His work is published in journals like Ecotoxicology and Environmental Safety , Toxicology , and Environmental Science & Technology , emphasizing open-access research and interdisciplinary approaches to chemical safety.
Sveta Zinger is a Full Professor in context-informed dynamic image analysis for clinical decision support at Eindhoven University of Technology (TU/e). She holds affiliations with the Biomedical Diagnostics Lab, NeuroPlatform, and EAISI Health. Her research focuses on medical image/video analysis, temporal data analysis, and machine learning for clinical applications. She has led projects funded by ZonMw, NWO, Philips, and others. She is also Co-Editor-in-Chief of Computer Methods and Programs in Biomedicine and serves on the Vidi committee for NWO. Education: MSc (2000) from Dnepropetrovsk State University; PhD (2004) from École Nationale Supérieure des Télécommunications, France. Postdoctoral roles at the French Atomic Agency and University of Groningen. Research Projects: Includes FORSEE (video monitoring for adverse events in healthcare) and NEUROTREND (fMRI biomarkers for depression). Awards: Second place in the CAMELYON17 challenge for metastases detection. Her teaching includes courses on DSP fundamentals, medical image processing, and cognitive neuroscience. She collaborates with clinical and industrial partners to advance biomedical diagnostics and healthcare technology.
Dr. Debraj Roy is a Visiting Professor at the University of Amsterdam (UvA), affiliated with the Faculty of Science, Mathematics and Computer Science and the Informatics Institute. His research focuses on agent-based modeling, socio-economic dynamics, environmental resilience, and blockchain technology. He investigates complex systems such as urban slums, disaster recovery, and climate adaptation using computational methods like remote sensing and machine learning. His work bridges theory and practice, offering insights into policy design for sustainable development and social equity. Key research interests include slum dynamics, poverty traps, and the application of blockchain oracles for decentralized systems. He employs advanced techniques such as global sensitivity analysis and manifold learning to explore multi-scale socio-environmental challenges. His recent articles highlight trends in carbon pricing, flood risk valuation, and multi-agent systems. Earlier work concentrated on urban inequality in cities like Bangalore and Mexico City, leveraging geospatial and statistical tools. No scientific awards or grants are explicitly listed. His advising and team collaborations are unspecified in the provided text.
Kees van Veen is an Associate Professor at the University of Groningen, affiliated with the Faculty of Behavioural and Social Sciences and the Department of Sociology. His expertise spans Corporate Governance , Policy Evaluation , and Sustainable Food Systems , with significant contributions to International Business & Management and Social Psychology during the pandemic. Education : MSc in Sociology (cum laude) from the University of Groningen. Research Focus : Interdisciplinary work bridging organizational behavior, prosociality, and global health behaviors, leveraging machine learning for cross-national pandemic analysis. His recent publications analyze prosocial behavior , conspiracy beliefs , and lockdown psychology through multi-country longitudinal data, emphasizing UN Sustainable Development Goals . He supervises MSc theses in Business Administration and Sociology and collaborates on open datasets like PsyCorona .
Maurice Heemels is a Full Professor at Eindhoven University of Technology (TU/e), leading the Control Systems Technology group. He holds additional professorships in EAISI Mobility, EAISI Foundational, EAISI Health, and EAISI High Tech Systems. His research focuses on hybrid and networked systems, emphasizing resource-aware control, event-triggered strategies, and cyber-physical systems integration. He is an IEEE Fellow and chairs the IFAC Technical Committee on Networked Systems. Academic Background: MSc and PhD in Mathematics (TU/e, 1995 and 1999, both summa cum laude ) Visiting Professorships: ETH Zurich (2001), UC Santa Barbara (2008) Industry Experience: Research & Development at Océ NV Research Interests: Hybrid Systems, Networked Control, Event-Triggered Control Model Predictive Control (MPC) in healthcare and high-tech systems Cyber-Physical Systems for applications like lithography and precision agriculture Key Contributions: Developed Hybrid Integrator-Gain (HIGS) systems and Projection-Based Control methodologies Recipient of a VICI Grant for wireless control systems research Oversaw over €7M in research funding from NWO, EU, and industry Awards & Recognition: Automatica Outstanding Service Award (2014) Best Paper Awards (EBCCSP 2017, etc.) Invited Keynote Speaker at ECC, CDC, and others Grants & Projects: Current Projects: COMEDI (Cost-effective Mechatronics), PROACTHIS (Projection-based Control) Past Projects: Fault Detection in Wafer Scanners, Drone-based Farming Labs & Teams: Active in TU/e’s Cyber-Physical Systems and Systems Engineering research groups, collaborating globally on nonsmooth dynamics and hybrid systems.
Wolf Ketter is a Full Professor of Next Generation Information Systems at the Department of Technology and Operations Management, Rotterdam School of Management, Erasmus University, and Chaired Professor of Information Systems at the University of Cologne. He serves as Director of the Institute of Energy Economics (EWI) in Cologne and leads the Erasmus Centre for Future Energy Business in Rotterdam. He is a leading figure in designing sustainable smart markets using advanced computing and simulation techniques. His research focuses on Information Systems , Machine Learning , Energy Economics , and Sustainable Smart Markets . He pioneered the use of Competitive Benchmarking through simulation platforms like Power TAC to tackle complex sustainability challenges. His work bridges computer science, economics, and business to design intelligent systems for energy, transportation, and resource allocation. The recent articles highlight a strong trend toward real-time decision-making in sustainable systems—such as electric bus operations, shared electric vehicles, traffic signal control via reinforcement learning, and local energy markets. These reflect his focus on AI-driven optimization , smart market design , and urban sustainability . Scientific Awards: INFORMS ISS Design Science Award (2012) Runner-up for Best European IS Research Paper (2013) ERIM Top Article Award (2013) ERIM Impact Award (2014) He has supervised over 10 PhD students and secured significant research impact through grants and collaborative projects. His editorial roles include serving on the boards of Information Systems Research and MIS Quarterly , the top journals in the IS field. He has chaired over 20 international conferences and workshops, advancing global discourse in trading agents and sustainable systems. Wolf Ketter founded and leads the Learning Agents Group at Erasmus University and chairs the annual Erasmus Energy Forum . His labs and teams focus on building simulation environments and AI agents to model and improve real-world sustainable markets, particularly in energy and mobility.
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. Jacco van Ossenbruggen is a Full Professor in Intelligent Information Systems at Vrije Universiteit Amsterdam (VU), affiliated with the Network Institute. He serves on the Management Board of ODISSEI, a national research infrastructure for social sciences and economics. His academic background includes a PhD in Computer Science (2001) from VU’s Faculty of Science, focusing on hypermedia processing. Research Interests: His work centers on cultural AI, FAIR data principles, ontology engineering, and semantic web technologies. Key areas include inclusive cultural heritage metadata, bias mitigation in AI systems, and knowledge discovery via linked data. Recent projects involve leveraging large language models (LLMs) for metadata enrichment and ontology construction. Key Contributions: He leads initiatives like the Cultural AI Lab, exploring AI applications for cultural heritage. His research bridges technical innovations (e.g., semantic integration of restricted-access data) with societal impacts (e.g., ethical AI frameworks for public-sector applications). Developed frameworks for evaluating entity alignment in knowledge graphs Pioneered FAIR-aligned data management plans for scientific communities Designed tools like Alter Heritage for collaborative metadata curation Grants & Projects: Principal Investigator of the ODISSEI Portal project (2020–2024), advancing open data infrastructures. Active in funding initiatives promoting reproducible research and ethical data practices. Labs/Teams: Cultural AI Lab at VU, focusing on AI-driven solutions for cultural heritage preservation and accessibility.
Dr. Koen Haak is an Associate Professor at Tilburg University's Department of Cognitive Science and Artificial Intelligence within the Tilburg School of Humanities and Digital Sciences. His research focuses on vision science, neuroimaging, and AI applications in healthcare. He leads projects like 'Bridging the gap between visual function and functional vision' (NWO Vidi) and 'Neuroimaging biomarkers for predicting vision training success after stroke' (NWO KIC). He collaborates with institutions such as the Donders Institute and the Lifelong Vision Consortium. His work contributes to UN SDGs related to health and innovation. Research interests include analyzing brain imaging data to predict functional vision outcomes, developing machine learning tools for clinical trials, and studying visual cortex plasticity. He has authored 51+ publications, including papers in Nature Neuroscience and Translational Psychiatry . Awards include NWO Veni (2016) and Vidi (2020) fellowships. He teaches courses on computer vision and AI at Tilburg University. Current projects explore predictive analytics for eye treatments, thalamocortical connectivity, and sleep disruption effects in maritime pilots. His lab develops methods like connectopic mapping and deep learning for MRI analysis, with applications in Alzheimer's, autism, and psychiatric disorders.
Anna Dawid-Lekowska is an Assistant Professor at the Leiden Institute of Advanced Computer Science (LIACS) and affiliated with the Leiden Institute of Physics (LION) at Leiden University, Netherlands. She leads a research group within the aQa group, focusing on the intersection of machine learning and quantum physics. Previously, she was a Research Fellow at the Center for Computational Quantum Physics, Flatiron Institute, New York. PhD in Physics and Photonics (joint, cotutelle), University of Warsaw & ICFO, Spain MSc in Quantum Chemistry, University of Warsaw BSc in Biotechnology, University of Warsaw Anna's research centers on interpretable machine learning for scientific discovery, particularly in quantum systems. She investigates how overparametrized models generalize, the role of loss landscape flatness, and double descent phenomena. Her work bridges deep learning with quantum simulations, aiming to detect quantum phase transitions and extract physical insights from trained models. She also explores ultracold molecules and novel quantum phases using simulation platforms. Her recent publications demonstrate a strong trend in applying machine learning to automate and interpret quantum experiments, such as detecting laser cooling schemes and understanding neural network initialization. The work emphasizes interpretability, aiming to make AI a transparent scientific tool rather than a black box. Anna has received significant recognition, including: 2022 FNP START laureate Participant in the 2024 Lindau Nobel Laureate Meeting She is actively mentoring and expanding her group, currently recruiting PhD students and postdoctoral researchers. Her work is supported by institutional affiliations with leading research centers and collaborations across Europe and the US. Anna also engages in science communication and education, having lectured at the Nordita Winter School on Machine Learning and Physics. She is involved in the aQa research group, which focuses on quantum algorithms and AI, fostering interdisciplinary collaboration between computer science and physics. Her lab integrates theoretical modeling, algorithm development, and applications to quantum experiments.
Dongsheng Yang is an Assistant Professor with the Electrical Energy Systems Group at the Department of Electrical Engineering of Eindhoven University of Technology (TU/e). He has been working at TU/e since 2019, focusing on power electronics and renewable energy integration, and previously served as Assistant Professor at Aalborg University's Department of Energy Technology (2018-2019). Dr. Yang received his B.S., M.S., and Ph.D. degrees in electrical engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2008, 2011, and 2016, respectively. His academic journey progressed from postdoctoral researcher at Aalborg University (2016) to faculty positions at both institutions. Dr. Yang's research focuses on the modeling, analysis, control, and design of power electronics dominated power systems , with the goal of safely accommodating high-penetrations of renewable energy sources and energy-efficient end-uses. His work spans several critical areas in modern power systems: Power electronics dominated grid stability and control Renewable energy integration and grid synchronization EV fast-charging infrastructure development Hydrogen production systems Medium-frequency transformer design and modeling AI applications in power electronics Analysis of Dr. Yang's recent publications reveals a strategic research trajectory toward developing advanced control strategies for power converters, improving modeling techniques through AI approaches, and addressing practical implementation challenges for renewable energy systems. His work spans both theoretical developments and practical applications, with increasing emphasis on neural network frameworks for magnetic modeling, safety boundaries for EV charging architectures, and enhanced fault ride-through capabilities for grid-connected systems. This progression demonstrates his commitment to solving real-world engineering challenges in the transition to renewable energy. Dr. Yang has received professional recognition including: Senior Member of IEEE Corresponding Member of CIGRE Working Group C4.56 Topic chair, technical committee member, and reviewer for top-level conferences and journals in power electronics Dr. Yang actively supervises doctoral candidates and postdoctoral researchers, including Xiao Yang (working on AI for power electronics), Saizhao Yang (postdoc), and L.A. Vlaar. He serves as project manager for multiple significant research initiatives totaling over €5 million in funding: REDCON (2023-2028) - Reconfigurable power electronics testbench Flexible Offshore Wind Hydrogen Power Plant Module (2022-2026) Sectorplan-DCES-Y.D.inv.: Reconfigurable power electronics testbench (2021-2029) E2GO-RDC Cost-reduction of EV fast-charging station (2021-2026) CW620863 System impact analysis for large scale renewable hydrogen production (2022-2023) Dr. Yang leads research within the Electrical Energy Systems group at TU/e's High Tech Systems Center, focusing on power conversion technologies. His work connects with multiple research teams across Europe through collaborative projects focused on renewable energy integration, EV infrastructure, and hydrogen production systems. He also teaches the course 'Dynamic control of power conversion in renewable energy systems' and contributes to the UN Sustainable Development Goals related to affordable and clean energy.
Professor David Abbink is a Full Professor of Haptic Human-Robot Interaction at Delft University of Technology, holding a joint appointment between the Department of Cognitive Robotics in the Faculty of Mechanical Engineering and Industrial Design Engineering since November 2023. He founded the Delft Haptics Lab and co-founded the Cognitive Robotics Department in 2017. Abbink leads the transdisciplinary research and innovation centre FRAIM, which was awarded the prestigious NWO Stevin Premie (Dutch Nobel Prize equivalent) in June 2024. Trained as a mechanical engineer specializing in biomechanics, Abbink's research focuses on human behavior adaptations when interacting with autonomous systems. He has published over a hundred scientific articles on human-robot interaction, haptics, shared control, tele-operation, driver assistance systems, and sensorimotor control. His research has been funded by industry partners (Nissan, Boeing, Renault), RVO (Brightsky project 2022-2026), and the Dutch Science Foundation NWO through personal grants (VENI 2010-2014, VIDI 2015-2019). Abbink's recent work centers on worker-robot relations as an academic focus, collaborating with organizations like Erasmus Medical Centre for nursing work, Schiphol and KLM for baggage handling, and KLM Engine Repair Services for maintenance work. He also serves as scientific director for the Centre for Meaningful Human Control, launched in October 2024. His work bridges engineering, social sciences, and practical applications to responsibly shape the future of work with emerging robotic capabilities. NWO Stevin Premie (2024) Best IEEE SMC journal paper on Cybernetics (2019) Top 25 scientific talents according to New Scientist (2015) Best teacher of Faculty 3mE (2013, 2014) Best teacher of Department of BioMechanical Engineering (seven consecutive years) Abbink has supervised over 110 MSc students and 11 PhD students. His educational contributions include developing the Master Programme in Robotics at TU Delft and receiving international recognition for his course 'The Human Controller.' He is also a prominent science communicator, featured on national television, radio, and major Dutch newspapers, and has delivered lectures at venues like The Royal Institution and Lowlands Festival. Despite his academic commitments, Abbink maintains a drummer persona, having recorded four albums and performed over 400 shows across three continents between 1999-2014.
Marie-Colette van Lieshout is a Professor of Spatial Stochastics at the Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, and a Scientific Staff Member in the Stochastics group at Centrum Wiskunde & Informatica (CWI), Amsterdam. She has been active in research since 1997 and is a leading expert in stochastic geometry, spatial statistics, and image analysis. Her educational and professional background includes positions at the University of Warwick and the Free University Amsterdam. She is currently engaged in advanced research on point processes, random fields, and tessellation models, with applications in seismic hazard, fire risk, and machine learning. Her research interests include: Stochastic Geometry Spatial Statistics Image Analysis Point Process Modeling Seismic Risk Assessment Machine Learning for Spatial Data Her recent publications (2023–2025) focus on spatial intensity estimation, marked point processes, and data-driven risk modeling, showing a strong integration of classical spatial statistics with modern computational and machine learning techniques. Key themes include adaptive kernel smoothing, infill asymptotics, and applications in environmental and public safety domains. She has received significant recognition, including: Elected Fellow, International Statistical Institute (ISI) She has been awarded multiple research grants from NWO and other agencies, including the KLEIN grant for fire risk management and the DeepNL grant for seismicity prediction in Groningen. She has supervised or collaborated with researchers such as C. Lu, Z. Baki, and R. Markwitz. She is also active in academic service, serving on editorial boards (e.g., Methodology and Computing in Applied Probability), advisory boards (InHolland University), and councils of learned societies (Bernoulli Society, KWG). She leads and participates in research clusters such as STAR and contributes to outreach and education through courses and public lectures on earthquake modeling and spatial statistics.
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.
