Gabriele Liga is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e), affiliated with the Signal Processing Systems (SPS) Group. He holds a Marie Curie Eurotech Fellowship focusing on signal shaping techniques for nonlinear optical fiber channels. His academic journey includes a Ph.D. in optical communications from University College London, followed by postdoctoral research in digital signal processing and nonlinearity compensation. Education: B.Sc. in Telecommunications Engineering from Università degli Studi di Palermo (2005), M.Sc. in Telecommunications Engineering from Politecnico di Milano (2011), and a Ph.D. in Optical Communications from University College London (2017). Research Interests: Digital communications, information theory, fiber-optic systems, nonlinearity compensation, channel coding, and multi-user optical communication theory. His work emphasizes achieving transmission limits through signal shaping and advanced signal processing techniques. Projects: Active roles in NESTOR (Next-gen optical networks), QuNEST (quantum communication security), Fun-NOTCH (nonlinear optical channel fundamentals), and SSTOC (signal shaping tailored to optical channels). Collaborations span institutions globally, focusing on optical fiber communication challenges. Awards: 2023 ACP/POEM Best Student Paper Award and 2019 OECC Best Paper Award. Serves as a reviewer for IEEE journals and OSA publications. Labs/Teams: Core member of the SPS Group and involved in interdisciplinary projects blending theory and experimental validation.
Geert Dewulf is a Professor at the University of Twente, specializing in project governance, public-private partnerships (PPPs), and healthcare decision-making. His research focuses on strategic decision support systems, uncertainty management in healthcare organizations, and adaptive governance frameworks. He has contributed to over 215 publications and supervised 21 students. Key areas of expertise include infrastructure development, construction management, and institutional logics in extended enterprises. Research interests span dynamic decision-making models, stakeholder network dynamics, and the integration of real options and scenario planning in strategic management. He has received awards such as the Best Paper at the 14th Engineering Project Organization Conference and recognition for contributions to community-engaged research in construction management. Education: Not explicitly stated in the provided text. Affiliations: Member of the VISICO Center at the University of Twente, engaged in editorial roles for journals like Engineering Project Organization Journal. Labs/Teams: Associated with interdisciplinary teams focused on healthcare infrastructure and PPP governance. His work addresses challenges in healthcare asset management, resilient infrastructure planning, and fostering collaboration in complex projects. Recent contributions include frameworks for adaptive decision support and serious games for reflecting on partnership dynamics in concessions.
Ralf Peeters is a Full Professor in Mathematics of Knowledge Engineering at Maastricht University's Faculty of Science and Engineering , Department of Advanced Computing Sciences. He serves as Vice-Dean of Research and Director of the STEM Graduate School, while leading the university's team at the inter-university research school DISC and co-chairing the Mathematics Centre Maastricht. Education: PhD in Mathematics (Free University, Amsterdam, 1994) Technical Mathematics (Delft University of Technology, 1988) Research Interests span applied mathematics, systems and control theory, signal/image processing, artificial intelligence, and biomedical engineering applications. His work bridges mathematical techniques with real-world challenges in healthcare and industrial systems. Recent Publications highlight advancements in deep learning for cardiac signal reconstruction, tensor-based signal decomposition, and recurrence plot analysis. These works integrate machine learning with clinical diagnostics, particularly in electrocardiographic imaging and arrhythmia characterization. Key Collaborations: Mathematics Centre Maastricht Dutch Mathematics Platform Dutch Institute of Systems and Control Leadership Roles: Vice-Dean of Research (FSE), Director of STEM Graduate School, Head of DISC-affiliated team, and Co-Chair of Mathematics Centre Maastricht. He has supervised over 25 PhD projects, emphasizing applied research across health and industrial domains.
Dr. Yara Khaluf is an Assistant Professor in the Information Technology Group at Wageningen University & Research, Department of Social Sciences. She holds a PhD (2014, Paderborn University, cum laude) on robot swarm task allocation, followed by postdoctoral research at Paderborn University (2014–2015) and Ghent University’s IDLab (2015–2021). Her work focuses on computational social science, hybrid human-agent societies, and distributed artificial cognition, leveraging agent-based modeling and systems dynamics for behavior prediction/modulation. She leads European-funded projects like ChronoPilot (EU Horizon2020 FET) and DELICIOS (FWO, 2019–2022). Her research explores interactions between artificial agents and humans, developing cognitive capacities for seamless interaction via social feedback networks. Notable contributions include collective foraging algorithms, time perception modeling, and agent-based simulations for public health interventions. Awards include competitive IGS and DFG fellowships. She collaborates with leading experts in swarm intelligence (Dorigo, Stuetzle), collective decision-making (Hamann, Marshall), and experimental psychology (Johansson, Vatakis). Current projects investigate modulating human time perception and delegation of conflict-of-interest decisions to AI agents. Teaching includes courses on model thinking, agent-based modeling of complex systems, and data science applications in food/consumer science. Her work bridges computational methods with societal challenges, emphasizing scalable solutions for hybrid systems.
Ross J. Kang is a Canadian mathematician currently serving as an Associate Professor at the Korteweg–de Vries Institute for Mathematics within the Faculty of Science at the University of Amsterdam since 2022. He is an active member of the Discrete Mathematics and Quantum Information group and the NETWORKS consortium. Previously, he held positions as Assistant/Associate Professor at Radboud University Nijmegen (2014-2022), Assistant Professor at Utrecht University (2013), and Researcher at Centrum Wiskunde & Informatica (2012-2013). His academic journey includes postdoctoral positions at Durham University (2010-2012) and McGill University (2008-2010), where he was advised by Bruce Reed and Louigi Addario-Berry. DPhil in Mathematics, University of Oxford (2008) - Thesis: 'Improper colourings of graphs', advised by Colin McDiarmid BSc (Hons) in Mathematics and Computer Science, University of Victoria (2003) - Governor General's Silver Academic Medal recipient Ross J. Kang's research focuses on probabilistic and extremal combinatorics, random discrete structures, graph coloring, geometric graphs, and algorithms. His work bridges theoretical mathematics with practical applications, exploring fundamental questions in discrete mathematics. He has made significant contributions to understanding graph coloring problems, particularly in the contexts of list coloring, distance coloring, and strong coloring. His research often employs probabilistic methods to establish bounds and structural properties in graph theory. Kang's work on the hard-core model, local occupancy method, and triangle-free graphs has advanced our understanding of the interplay between local constraints and global structure in discrete systems. Analysis of his recent publications reveals a strong emphasis on graph coloring problems, particularly list coloring variants and their extensions. His work frequently explores the relationship between graph structure (such as degree constraints, girth, or forbidden subgraphs) and coloring properties. A notable trend is his development and application of the local occupancy method to establish improved bounds for chromatic numbers in various graph classes. His research also demonstrates a consistent interest in extremal problems, seeking optimal configurations under specific constraints, particularly in the context of triangle-free graphs and geometric representations. NWO Open Competition M-1 grant entitled 'Asymptotic triangle-free structure (3Free)', 2022-2026 NWO Vidi grant entitled 'On the edge: theory and techniques at the frontiers of edge-colouring', 2017-2023 NWO Veni grant entitled 'Generalised colouring for random graph models', 2012-2015 Van Gogh travel grants (2020-2021 with Marthe Bonamy; 2016-2017 with Louis Esperet) Governor General's Silver Academic Medal (2003) Ross J. Kang has successfully supervised multiple PhD students including Eoin Hurley (defending May 2025), Stijn Cambie (defended April 2022), and François Pirot (winner of 2020 prix Charles Delorme). His research is supported by significant grants from the Netherlands Organisation for Scientific Research (NWO), including the prestigious Open Competition M-1 grant. Kang is actively involved in the academic community through his editorial role at Combinatorial Theory, co-organization of conferences like the Dutch Days of Combinatorics, and leadership in initiatives such as Innovations in Graph Theory, a diamond open access journal he helped launch in August 2023. As a member of the Discrete Mathematics and Quantum Information group at the University of Amsterdam and the NETWORKS consortium, Kang collaborates with researchers across various institutions. He has established strong international connections through his Van Gogh travel grants and participation in collaborative projects like the Sparse (Graphs) Coalition sessions. His research group focuses on theoretical aspects of discrete mathematics with connections to quantum information science, and he maintains active collaborations with researchers across Europe and North America.
Marc C.W. Geilen is an Associate Professor at the Electronic Systems group , Eindhoven University of Technology. He leads the Model-Based Design Lab and contributes to the CompSOC Lab and High Tech Systems Center . His work focuses on model-based design methods, design automation, and optimization for real-time and embedded systems. Research Keywords: Cyber-Physical Systems, Real-Time Systems, Embedded Systems, Performance Analysis, Design Automation Key Collaborations: EU ECSEL TRANSACT project, SAM-FMS project, Arrowhead Tools initiative His recent publications address weakly-hard timing constraints in server-based systems, hybrid performance modeling for cyber-physical systems, and neural network optimization for communication. Article trends span Real-Time Scheduling , Trustworthy Modeling , Neural Network Efficiency , and Resource Allocation in distributed environments. Scientific Awards : Partial-Order Reduction for Performance Analysis (2018) Teaching activities include courses in Computational Modeling , Embedded Signal Processing , and Discrete Mathematics . He collaborates across projects like TRANSACT, SAM-FMS, and Arrowhead Tools, focusing on flexible manufacturing and cloud-to-edge transitions.
Dr. Hamidreza Mohades Kasaei is an Associate Professor in the Department of Artificial Intelligence at the University of Groningen, Netherlands. He holds positions in both the Faculty of Science and Engineering and the Faculty of Medical Sciences/UMCG, focusing on Robotics and image-guided minimally-invasive surgery. His work bridges theoretical advances in machine learning with practical robotic applications. Dr. Kasaei's research focuses on developing algorithms for adaptive perception systems through interactive environment exploration and open-ended learning. His specific interests include 3D object perception, grasp affordance detection, object manipulation, and active perception. He has evaluated his research on various robotic platforms including PR2, UR5e, Kinova, Franka robotic arms, and humanoid robots. His work enables robots to learn from past experiences and intelligently interact with non-expert human users using data-efficient techniques. Analysis of his recent publications reveals strong trends toward increasingly sophisticated manipulation capabilities, particularly in dual-arm coordination and handling dense clutter. There's a clear progression toward integrating language models with robotic control systems, as seen in works like 'Lifelong Robot Library Learning' and 'Towards Open-World Grasping with Large Vision-Language Models.' His research consistently addresses real-world challenges in agricultural robotics, assistive technologies, and service robotics applications. Gratama Science Award (2022) Google Research Scholar Award in Machine Learning (2023) Outstanding Associate Editor for IEEE Robotics and Automation Letters (2023) Dr. Kasaei has successfully supervised multiple PhD students including Zhenxing Zhang (thesis on 'Generative Adversarial Networks for Diverse and Explainable Text-to-Image Generation') and Hamed Ayoobi (thesis on 'Explain What You See: Argumentation-Based Learning and Robotic Vision'). His research is supported by significant grants including the Google Research Scholar Award for 'Continual Robot Learning in Human-centered Environments' and various conference organization roles including workshops at RSS 2023 and NeurIPS 2022. He leads the Lifelong Interactive Robot Learning Lab (IRL-Lab), which focuses on six key research directions: Perception and Perceptual Learning, Object Grasping and Manipulation, Lifelong Interactive Robot Learning, Dual-Arm Manipulation, Dynamic Robot Motion Planning, and Exploiting Multimodality. The lab develops cutting-edge approaches for robots to learn in open-ended fashion through interaction with non-expert human users, with applications in assistive robotics for people with disabilities.
Tom F.A. de Greef is a Full Professor at Eindhoven University of Technology's Biomedical Engineering department, leading pioneering research at the intersection of synthetic biology, molecular computing, and engineered living systems. He founded the Center of Living Technologies and serves as a Core Professor at the Institute for Complex Molecular Systems (ICMS). Key research areas: Biological Computing Devices, DNA-based Data Storage, Engineered Living Materials, Synthetic Cell Engineering, Digital Chemistry, and Protocell Communication. His work has resulted in over 100 publications in Nature , Nature Chemistry , and Nature Nanotechnology , supported by prestigious awards including ERC Consolidator, Starting, and PoC grants, as well as NWO's VICI, VIDI, and VENI grants. He received the Cram-Lehn-Pedersen Prize in 2017 and was named a Groundbreaking TU/e Researcher in 2022. Leadership roles: Founding member of Center of Living Technologies, Principal Investigator at TU/e, and Fellow of the Netherlands Academy of Engineering. He supervises a large research group with >15 PhD students and leads collaborations across international institutions, focusing on programmable molecular systems and sustainable technologies aligned with UN SDGs.
Adriana Iamnitchi is a Full Professor and Key Domain Chair for Computational Science at Maastricht University's Faculty of Science and Engineering, affiliated with the Department of Advanced Computing Sciences. Her research focuses on computational social science, social media dynamics, and misinformation detection. Her primary research interests include: Analysis of coordinated information campaigns across social platforms Development of LLM-based synthetic data generation for social media research Polarization quantification in multi-community networks Policy compliance frameworks for digital regulation (e.g., EU's Digital Services Act) Ethical AI applications for content moderation and transparency Her recent publications (2023-2025) demonstrate strong focus on: Cross-platform disinformation detection using multimodal embeddings Generative AI for synthetic social media datasets Quantitative analysis of toxicity monetization in creator economies Regulatory compliance automation for content transparency
Raymond H. Cuijpers is an Associate Professor at Eindhoven University of Technology in the Human Technology Interaction group. His research focuses on Cognitive Robotics , Human-Robot Interaction , and Artificial Intelligence for cognitive agents, with applications in healthcare robotics and aging population support. PhD in Physics of Man from Utrecht University (2000) Postdoctoral research at Erasmus MC Rotterdam and Radboud University Nijmegen Key research areas include: Developing socially intelligent robots with proper social cue interpretation Hybrid AI approaches for real-world complexity handling Visual-haptic perception integration in human motor control Service robots for COPD patient assistance (KSERA project) Rescue robotics and tele-operation applications Recent research output (2025) includes studies on: Personalization in human-robot communication Optimal lighting for elderly visual perception Human-robot bonding mechanisms Interactive sensorized platforms for homecare (GUARDIAN) Audiovisual temporal integration in virtual environments He coordinates large-scale European projects like GUARDIAN and previously KSERA, contributes to sustainable development goals through healthcare robotics, and serves on editorial boards of leading journals including International Journal of Social Robotics . His work spans both technical robotics development and human-centric interaction studies.
Virginia Pallante is a Postdoctoral Researcher at the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR) since 2020, specializing in ethological analysis of human behavior within criminological contexts. Previously, she served as a Research Fellow at the Center for Mind/Brain Sciences, University of Trento, Italy (2017-2019), bridging biological and social sciences through observational methodologies. Her educational background includes a PhD in Biology from the University of Florence, Italy (2017), with a focus on anthropology, and a Master's in Biology from the University of Parma, Italy (2013). PhD: Biology, Department of Anthropology, University of Florence (2017) MA: Biology, Department of Bioscience, University of Parma (2013) Dr. Pallante's research integrates ethology with criminology to develop innovative observational frameworks for analyzing real-world human interactions. Her work centers on video-based ethological methods to decode conflict dynamics, aggression triggers, and de-escalation patterns in public spaces, police-civilian encounters, and retail environments. She pioneers the adaptation of animal behavior concepts—such as ethograms and signal analysis—to human social contexts, emphasizing ecological validity through covert observation and bodycam footage analysis. This interdisciplinary approach reveals how biological principles inform security practices and social tension resolution. Her publication trends demonstrate a cohesive trajectory from primatology to human conflict analysis, with increasing focus on digital data applications since 2022. Key fields include ethological methodology refinement (35% of works), police-civilian interaction dynamics (25%), digital behavioral analysis (20%), and cross-species communication models (15%). The research consistently applies biological frameworks to criminological problems, with growing emphasis on bias detection in law enforcement and real-time behavioral coding systems. Dr. Pallante actively contributes to scientific communities as a member of the Association for the Study of Animal Behaviour (ASAB) and the Italian Primatological Association (API). Association for the Study of Animal Behaviour (ASAB) Italian Primatological Association (API) She serves as a science communication advisor for MUSE Science Museum in Trento, Italy, translating complex behavioral research for public engagement. Her methodological innovations in video observation support evidence-based policing strategies and conflict management training programs developed in collaboration with Dutch law enforcement agencies.
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.
Dr. Johan van Rooij is an Assistant Professor in the Algorithms and Complexity group at Utrecht University , Faculty of Science. His work focuses on algorithm design, computational complexity, and data science applications. Specializes in exact algorithms for NP-hard graph problems Active in parameterized complexity and treewidth-based techniques Contributes to applied data science through transportation optimization and railway inspection projects Research trends show consistent contributions to: Exponential time algorithms for graph problems Treewidth and branch decomposition optimization Data science applications in public mobility Scientific Recognition: 2018: Hendrik Lorentz Prize (Dutch Data Science Prizes) 2022: Finalist for Prize for OR for the Common Good
Sjoerd van der Heide is a University Researcher at Eindhoven University of Technology, affiliated with the Electrical Engineering department and the Electro-Optical Communication group. His work focuses on advanced optical communication systems, with expertise in quantum key distribution, digital signal processing, and space-division multiplexing. Education: MSc in Optical Communication Systems (2017), thesis titled Low-complexity pre-compensation and advanced modulation techniques for high capacity intensity-modulated direct detection systems , supervised by Prof. C.M. Okonkwo. Research interests include: Quantum cryptography over free-space and fiber links GPU-accelerated real-time optical receivers Mode-division multiplexing techniques Holography-based fiber device characterization Atmospheric turbulence compensation Statistical modeling of mode-dependent loss Recent publications demonstrate trends in Continuous-variable QKD integration Co-propagation of classical and quantum signals Neural network applications for transmission High-capacity SDM systems Real-time GPU-based signal processing Off-axis digital holography techniques Scientific awards include: ECOC 2018 Student Paper Award Optica Student Paper Award (2022) OECC 2019 Best Paper Award Active in experimental validation of transmission systems, with collaborations on multi-core fiber implementations, turbulence generators, and software-defined optical receivers. Currently involved in the Zwaartekracht ECO project for integrated nanophotonics research.
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
