Javier Alonso-Mora is a Professor in the Department of Mechanical Engineering at Delft University of Technology, specializing in Learning & Autonomous Control. His research focuses on autonomous systems, robotics, motion planning, and transportation logistics, with applications in mobile manipulation, dynamic environments, and urban mobility. He leads key projects such as INTERACT (Intuitive Interaction for Robots among Humans) and ACT (Perceptive Acting Under Uncertainty), exploring human-robot interaction, autonomous vehicles, and healthcare robotics. Notable achievements include an ERC Starting Grant (2022) and a Veni Grant (2017). His work addresses challenges in robot navigation, control systems, and fleet optimization, with contributions to both theoretical advancements and practical implementations. Projects like TRiLOGy focus on sustainable water transportation, while HARMONY advances assistive robotics in healthcare. Alonso-Mora’s research leverages geometric fabrics for motion planning, probabilistic modeling for dynamic environments, and multi-agent coordination. He collaborates internationally and contributes to open-source frameworks for robotics. His recent publications emphasize safety-aware control, instance-aware semantic mapping, and adaptive systems for cluttered environments.
Dr. Yan Feng is an Assistant Professor at Delft University of Technology's Department of Transport and Planning and Director of the Mobility in eXtended Reality (XR) Lab. Her research focuses on using XR technologies (Virtual, Augmented, and Mixed Reality) to study human mobility behavior in urban environments, including walking, cycling, automated vehicles, and public transportation. She leads interdisciplinary research at the intersection of transportation engineering, design, architecture, and human-computer interaction, aiming to advance human-centered and inclusive mobility systems. Her research interests include mobility behavior analysis, XR-based methodologies for studying wayfinding, evacuation scenarios, human-vehicle interaction, safety training, and accessibility. Key contributions involve developing XR tools for urban mobility research and addressing diversity and inclusion in transportation systems. Recent work emphasizes validating VR simulations against real-world environments and analyzing pedestrian behavior in high-risk scenarios. Articles highlight methodologies for crowd management strategies, pedestrian choice modeling, and evacuation dynamics. No scientific awards are explicitly mentioned in the provided texts. Dr. Feng’s activities include organizing conferences (e.g., XRIA 2024), delivering keynote talks on automated driving and cycling simulator studies, and serving on editorial boards like the Delft Young Academy. The Mobility in eXtended Reality Lab is central to her work, fostering collaborations across disciplines to innovate mobility solutions.
Gamze Z. Dane is a tenured Assistant Professor at the Department of Built Environment of Eindhoven University of Technology (TU/e), affiliated with EAISI Mobility and EAISI Health. She leads the Digital City Program (2020-2024) and specializes in decision-support systems, GIS, urban informatics, and data analytics for sustainable urban development. Her research integrates citizens into urban decision-making using digital tools like VR twins and data-driven approaches. Education: PhD in Urban Planning, MSc in Geographical Information Systems (GIS) and Decision Making. Research Interests: Focuses on human-environment interaction, transdisciplinary urban projects, and the impact of digitalization on cities. She develops tools for public participation and uses big data to analyze citizen behavior and urban experiences. Projects: Principal Investigator for EU/national projects involving cities like Eindhoven, Bologna, and Lisbon. Notable projects include UBeX Urban Behavior eXtended reality lab (2024-2026) and ROCK (2017-2020). Awards: Cuperusprijs 2020 (2nd place for student thesis) Drivers of Change Exhibition 2021 ISPRS International Journal Cover Story (2020) Teaching & Innovation: Coordinates courses like Smart Cities and Urban Redevelopment. Developed online teaching materials using VR, drones, and mobile apps. Guest lectures at Istanbul Technical University and visiting scholar at National University of Singapore. Labs & Networks: Leads the UBeX lab exploring immersive technologies for urban analysis. Active in academic networks including Urban Planning journals and international conferences.
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.
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.
National Research Institute for Mathematics and Computer ScienceNetherlands
Dr. Jie Li is a dual-career academic and creative professional with a PhD in Industrial Design Engineering from Delft University of Technology. As an HCI/UX researcher in industry and Adjunct Professor at multiple institutions, she bridges academia and practice through work on Extended Reality (XR) , Human-AI interactions , and user experience evaluation . Her ACM Interactions column 'Bits to Bites' explores interdisciplinary research methodologies. Education: MSc in Industrial Design Engineering, Delft University of Technology PhD in Industrial Design Engineering, Delft University of Technology (2019) Her research spans social VR platforms , AI-augmented cognition , and privacy-preserving emotion detection , with recent publications analyzing LLM-assisted game design , harassment detection in VR , and XR's impact on remote collaboration . She has received Best Demo Awards (2020, 2022) and the ACM Best Paper Award (2018). Notable trends in her work include emerging immersive technologies (XR, 6DoF displays), human-AI collaboration frameworks , and cross-domain applications from medical VR clinics to cultural heritage experiences . Her advocacy for synthetic UX research and asynchronous co-creation tools reflects industry-academia hybrid innovation. Scientific Awards: Best Demo Award (2020, ACM TVX/IMX 2020) Best Demo Award (2022, ACM Multimedia) ACM Best Paper Award (2018, ACM TVX) While maintaining active roles in CHI conference committees and guest lecturing , Jie also operates a Delft-based creative cake design business , demonstrating her commitment to interdisciplinary exploration and 'slash career' balance between technical research and artistic practice.
Maarten van Steen is a Professor active in the fields of Distributed Systems , Artificial Intelligence , and Cybersecurity . With an h-index of 35 and over 5,400 citations, his work focuses on Edge AI , Privacy Preservation , and WiFi-Based Sensing . His research emphasizes non-intrusive authentication, anonymization techniques, and crowd monitoring without compromising individual privacy. Key research areas: Distributed Systems, Privacy Preservation, WiFi Security Recent projects: RoomKey, LocKey, FlowPrint Crowd-monitoring applications: Subway travelers, pedestrian dynamics Van Steen's work combines Machine Learning with Homomorphic Encryption to develop privacy-first solutions. He has contributed to mobile app fingerprinting , WiFi authentication , and blockchain scalability challenges. His 2024–2025 publications reveal trends in contextual security , crowd behavior analysis , and automated threat intelligence . Notable methods include Bloom Filters, automata learning, and WiFi beacon frame analysis. Dutch Cyber Security Best Research Paper Award 2024 Runner-up (shared prize) Van Steen supervises research teams and collaborates on datasets like Code for Threat Intelligence Processing and DeepCASE . His work spans 20+ years , with 208 total research outputs and significant contributions to decentralized systems, network traffic analysis, and urban mobility.
O. Cats is a Professor of Passenger Transport Systems and Head of the Department of Transport & Planning at Delft University of Technology (TU Delft). They also serve as a Guest Professor at KTH Royal Institute of Technology. Their research focuses on multi-modal passenger transport networks, combining simulation, operations research, behavioral sciences, and complex network theory to address challenges in public transport, shared mobility, and long-distance travel dynamics. Key interests include network robustness, service operations, and passenger demand modeling. Dr. Cats leads the Smart Public Transport Lab at TU Delft, collaborating closely with transport authorities and operators. They hold editorial roles at journals like the Journal of Public Transportation and have received prestigious grants, including the ERC Starting Grant for the CriticalMaaS project. Awards include recognition for work on crowding valuation in urban transport systems. Recent publications emphasize optimization of electric vehicle infrastructure, sustainability in travel behavior, and dynamics of ridesourcing markets. Their work bridges theoretical research with practical applications, aiming to enhance transport efficiency and sustainability globally.
Tom van Woensel is a Full Professor of Freight Transport and Logistics at Eindhoven University of Technology (Netherlands), affiliated with the School of Industrial Engineering and Innovation Sciences and the Department of Operations Planning Accounting & Control. He also holds roles as Academic Director of the Global Supply Chain Management program at Antwerp Management School and Director of the European Supply Chain Forum. His research focuses on freight transport, logistics systems, and operations research methodologies, with contributions to over 150 peer-reviewed publications in journals like Transportation Science and European Journal of Operational Research . Education: BSc/MSc in Applied Economic Sciences (Econometrics), University of Antwerp (Belgium) PhD: Queueing Theoretical Approaches for Traffic Flow Networks, University of Antwerp Research Interests: Optimization of transport and logistics networks using integer programming, metaheuristics, and reinforcement learning Urban freight systems, last-mile delivery, and sustainable logistics Supply chain resilience, collaboration in logistics networks, and industry-academia partnerships Applications of AI in solving stochastic transportation problems Awards and Recognition: Outstanding Professor in Supply Chain & Logistics (2021) European Journal of Operational Research Best Review Paper (2016) INFORMS Senior Member (2024) Collaborations and Projects: Leads initiatives like SYNERCIZE (zero-emission construction logistics) and Circulaire stromen (circular logistics) Editorial roles at Transportation Science , OR Spectrum , and Urban Science Active in industry partnerships through the European Supply Chain Forum (75+ multinationals) Labs/Teams: EAISI Mobility (Eindhoven AI Systems Institute) CIRRELT (Montreal, Canada) collaborating member
Dr. Charlotte Gerritsen is an Associate Professor in Artificial Intelligence at the Faculty of Science, Vrije Universiteit Amsterdam (VU), and an affiliated member of the Network Institute. Her research focuses on agent-based modeling of social and emotional dynamics, emotion contagion in virtual environments, and applications of AI in crowd management, mental health, and criminology. She has published extensively on topics such as sentiment analysis, gamification, and the ethical implications of AI in public safety. Her work integrates computational methods with social science theories, addressing challenges like real-time violence detection, avatar-mediated emotion recognition, and serious gaming for resilience training. She has supervised 5 PhD theses and teaches courses on AI and Law, Artificial Intelligence, and Socially Aware Computing. Her research project, 'Integrating sentiment analysis in real-time crowd management,' highlights her commitment to bridging technology with societal needs. Education: PhD in Artificial Intelligence (implied by title 'dr.') Affiliations: Faculty of Science, Network Institute Key Research Areas: Emotion Contagion, Agent-Based Modeling, Crowd Behavior, Gamification Her articles analyze topics like emotion contagion in crowds, AI-driven crowd control, and virtual training systems. While no specific awards are listed, her prolific publication record underscores her academic impact. She advises students on criminological and computational social science projects and collaborates internationally in criminology, healthcare, and AI ethics.
Wouter van Toll is a Lecturer at the Academy for AI, Games & Media, specializing in crowd simulation and real-time systems. His research focuses on path planning, crowd behavior modeling, and fluid dynamics in agent-based simulations. He has contributed to advancing algorithms for microscopic crowd simulation and integrating techniques like Smoothed Particle Hydrodynamics (SPH) to handle extreme crowd densities. Key research interests include sketch-based interaction design for steering behaviors, navigation mesh optimization, and topological strategies for agent coordination. His work bridges computational methods with creative applications in game development and artificial intelligence. Received Best Paper Award Honorable Mention (2022) for his work on sketch-based steering behaviors in crowd simulation. Active collaborations in Europe and North America, particularly in crowd simulation software development. Publications span algorithmic advancements in crowd simulation, navigation systems, and interdisciplinary applications combining physics-based methods with agent-based models. Current research emphasizes real-time simulation efficiency and human-centered design tools for behavior specification.
Alessandro Corbetta is an Assistant Professor in the Department of Applied Physics and Science Education at Eindhoven University of Technology (TU/e). He leads the 'AI for Traffic and Complex Flows' group, focusing on pedestrian dynamics, machine learning in fluid mechanics, and active flowing matter. His work integrates empirical data, statistical physics, and computational methods to model crowd behavior and optimize pedestrian environments. Education & Academic Background: MSc (cum laude) in Mathematical Engineering, Polytechnic University of Turin (2011) PhD in Applied Mathematics, TU/e (2016) PhD in Structural Engineering, Polytechnic University of Turin (2016) Research Interests: His research spans pedestrian dynamics, machine learning for fluid mechanics, turbulence modeling, and high-performance computing. Key areas include real-world crowd tracking, AI-driven crowd management, and statistical mechanics applied to big data analytics. Awards & Grants: 2021 Ig Nobel Prize in Physics for studying pedestrian collision avoidance 2018 VENI Grant (NWO) for 'Understanding and Controlling Human Crowd Flows' Teaching & Academic Contributions: Responsible for courses like 'Machine Learning in Science' and 'Machine Learning for Fluid Mechanics' Editor-in-Chief of Collective Dynamics Labs & Collaborations: Collaborates with municipalities, museums, and festivals to deploy real-time crowd management systems. Active in TU/e's Intelligent Lighting Institute and Fluids and Flows research groups.
Marileen Dogterom is a Professor of Bionanoscience at the Kavli Institute of Nanoscience, Delft University of Technology, and holds a dual appointment as a Medical Delta Professor at the Leiden Institute of Physics, Leiden University. Her research focuses on the quantitative biophysics of the cytoskeleton, particularly microtubule dynamics and their role in cellular organization and division. Her work integrates in vitro reconstitution , theoretical modeling , and live-cell experiments to dissect the physical mechanisms underlying cytoskeletal processes. She leads the national 'Building a Synthetic Cell' (BaSyC) initiative, aiming to construct a minimal synthetic cell, reflecting her pioneering role in bottom-up synthetic biology. Her lab investigates cytoskeletal crosstalk, DNA segregation systems, microtubule-kinetochore coupling, and force generation using advanced techniques like optical tweezers and liquid-phase electron microscopy. The most recent publications highlight a strong trend in mechanistic biophysics and synthetic cell engineering , with a focus on protein complexes (Ndc80, Ska), microtubule end dynamics, actin-microtubule coordination, and the development of minimal in vitro systems to model cellular processes like polarity and coacervate formation. Spinoza Prize (2018) : One of the highest scientific awards in the Netherlands. Member of the KNAW board (2017) : Elected to the Royal Netherlands Academy of Arts and Sciences. She actively supervises PhD and master’s students, fostering the next generation of scientists in biophysics and synthetic biology. Her lab collaborates widely, including with groups at TU Delft, Leiden University, and international institutions like BIOCEV in Prague. The lab is deeply involved in cutting-edge projects such as building light-controllable DNA segregation systems and visualizing microtubule dynamics with liquid-phase EM. Her research is conducted at the intersection of physics, biology, and engineering, primarily within the Marileen Dogterom Lab at TU Delft, and through collaborative efforts with the Koenderink group (TU Delft) , the Schneider lab (Leiden University) , and the broader European Synthetic Cell initiative .
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.
Caspar A.S. Pouw is a Research Fellow in the Department of Applied Physics and Science Education at Eindhoven University of Technology (TU/e). He holds a dual role as a Postdoc researcher and Data Scientist at ProRail. His work focuses on advancing human crowd flow monitoring, modeling, and nudging technologies, particularly within the HTCrowd project. Pouw’s research integrates fluid dynamics principles to analyze pedestrian behavior in crowded environments, aiming to enhance safety and efficiency in urban spaces. Educated at TU/e, he earned his Master’s in Applied Physics (specializing in Fluids and Flows) and a Bachelor’s in Combustion Science. He has taught courses on sociophysics, covering crowd dynamics analysis, modeling, and nudging strategies. His contributions align with UN Sustainable Development Goals related to safe cities and resilient infrastructure. Recent research emphasizes data-driven modeling of pedestrian dynamics, psychological influences on train boarding efficiency, and real-time monitoring systems. His work bridges physics, computer science, and urban planning, with applications in transportation and public safety. Collaborations include ProRail and interdisciplinary teams at TU/e. Pouw’s datasets and software tools, such as those for pedestrian trajectory analysis, are openly available. His media coverage highlights innovations in crowd management post-COVID-19. Future work involves expanding predictive models for crowd behavior and optimizing transport infrastructure design.
