Didier Meuwly is a Full Professor of Forensic Biometrics at the University of Twente (since 2013) and Principal Scientist at the Netherlands Forensic Institute (NFI). His work focuses on automating and validating probabilistic evaluation of forensic evidence, particularly biometric traces. He has contributed to international standards via ISO Technical Committee 272 and served as Associate Editor for Forensic Science International . PhD in Forensic Speaker Recognition (University of Lausanne, 2000) Research spans forensic biometrics, likelihood ratios, AI validation, and gait/body analysis from surveillance footage. Recent work addresses ISO standards (21043), forensic AI explainability, and multimodal evidence evaluation. His publications emphasize empirical validation and statistical rigor. Key awards include: ENFSI Distinguished Forensic Scientist Award (2022) University of Lausanne Law Faculty Prize (2002) Active in global forensic networks, he chairs the ENFSI R&D Committee and collaborates across disciplines on digital evidence, biometric security, and forensic methodology.
Nicola Calabretta is a Full Professor in Electro-Optical Communication Systems and Senior Research Fellow at Eindhoven University of Technology (TU/e). His work focuses on smart optical networks, high-speed electronics, FPGA implementations for scheduling algorithms, and photonic integrated circuits. He holds a PhD from TU/e (2004) and previously conducted research at DTU Fotonik and the Sant'Anna School of Advanced Studies. His expertise spans optical signal processing, multi-level modulation formats, and applications in data center and metro networks. Key research areas include optical switching architectures (e.g., SOA-based switches), WDM systems, and low-latency interconnect networks. He has led projects like ADAPTOR (resource optimization), SmartTWO (future telecom technologies), and 5G-MOBIX (cross-border mobility). His courses include 'Optical Fibre Communication Technology' and 'Optical Interconnection Networks.' Collaborations involve institutions globally, with recent work emphasizing photonic integration for neural networks, ultra-fast switching, and edge computing. His contributions align with UN SDGs through sustainable telecom infrastructure advancements.
Rémy Jacquemond is a Postdoctoral Researcher at Eindhoven University of Technology's Department of Chemical Engineering and Chemistry, working within both the Membrane Materials and Processes Group and Electrochemical Materials and Systems Group. His research focuses on developing advanced materials for next-generation redox flow batteries, with particular emphasis on membrane technology and electrode engineering. His academic background includes: BSc in General Chemical Sciences from Institut Universitaire de Technologie (IUT), Montpellier, France MSc in Chemical Engineering with materials science specialization from Ecole Nationale Superieure de Chimie de Montpellier (ENSCM), France Second MSc in Nanoscience, Materials and Processes from Universitat Rovira I Virgili (URV), Tarragona, Spain PhD in Chemical Engineering and Chemistry from Eindhoven University of Technology (2023) His research centers on solving critical challenges in redox flow battery technology, particularly the development of ion exchange membranes stable in organic solvents and engineered porous electrodes. He pioneers the application of neutron radiography for in-situ diagnostics of battery operation and employs non-solvent induced phase separation techniques for precise electrode microstructure control. His work directly contributes to UN Sustainable Development Goals related to clean energy and responsible consumption. Analysis of his publication record reveals a strong trajectory in advanced battery diagnostics and materials engineering, with increasing focus on neutron-based visualization techniques and microstructure-controlled electrode fabrication. His 2024 Nature Communications paper on concentration distribution mapping represents a methodological breakthrough, while his consistent development of phase separation techniques for electrode engineering demonstrates systematic innovation in manufacturing approaches. He has received significant recognition for his contributions: Energy Technology Division Graduate Student Award sponsored by Bio-Logic for work on porous carbon electrodes As an active postdoctoral researcher, Jacquemond contributes to research supervision and project leadership within his groups. His current work focuses on membrane diagnostics and novel porous materials development, with emphasis on improving battery efficiency, longevity, and compatibility with organic electrolytes. He maintains strong industry and academic collaborations, evidenced by multiple co-authored publications with international research teams. He operates within Eindhoven University of Technology's cutting-edge research infrastructure, utilizing specialized facilities for membrane synthesis, electrode fabrication, and advanced characterization including neutron imaging capabilities through partnerships with major research facilities. His work bridges fundamental materials science with practical energy storage applications.
Dr. Stevan Rudinac is a Researcher at the University of Amsterdam's Faculty of Economics and Business , Section Business Analytics . His work focuses on interactive learning systems and multimodal data analysis, particularly in urban contexts and multimedia modeling. Education: PhD in Multimedia and Information Retrieval from Delft University of Technology (2013). Research Interests: Stevan specializes in multimedia modeling , hypergraph learning , and interactive video search . He develops frameworks for scalable analysis of social networks, urban imagery, and large multimodal datasets, bridging machine learning with practical applications in city planning and financial social media. Recent Trends: His 2024-2025 publications highlight large language model optimization , diffusion model evaluation , and dynamic graph embedding for meme stocks. Collaborative projects include the CASTLE 2024 dataset and Exquisitor , a system for 100 million image exploration. Labs & Teams: He contributes to the Business Analytics group at UvA, collaborating with Prof. Marcel Worring and Dr. Björn Þór Jónsson. He co-organized the UrbanMM'21 workshop and participates in ACM Multimedia and MMM conferences.
Peter Desain is a Professor and Principal Investigator at the Donders Institute for Brain, Cognition and Behaviour, Radboud University. His work focuses on developing advanced brain-computer interfaces (BCI) leveraging evoked potentials, particularly through code-modulated visual and auditory stimuli. He pioneers methods like noise-tagging and Bayesian dynamic stopping to enhance BCI efficiency and accessibility. His research spans neurotechnology, electrophysiological modeling, and clinical applications such as objective EEG audiometry and ALS communication aids. Recent studies emphasize gaze-independent systems, semantic decoding, and minimizing BCI calibration requirements. Key contributions include optimizing c-VEP code-books, real-time fMRI neurofeedback for memory contexts, and literature reviews on BCI design trends. Experimental pilot studies explore auditory attention and high-frequency SSVEP dynamics. No scientific awards are explicitly mentioned. His work integrates multidisciplinary approaches, bridging neuroscience, machine learning, and engineering to advance human-computer interaction and clinical tools.
Dr. Carlo Cavicchia is an Assistant Professor of Statistics at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. He holds a PhD in Methodological Statistics from La Sapienza University of Rome and has held roles such as Research Fellow at UnitelmaSapienza University and Consultant for NGOs in Zanzibar. His research focuses on latent variable models, composite indicators, and unsupervised classification, with applications in environmental policy, sports analytics, and teacher job satisfaction. Cavicchia teaches statistics and data science courses at undergraduate and graduate levels and actively contributes to academic communities through journal reviewing, conference organizing, and editorial roles. Education: PhD in Methodological Statistics (La Sapienza University of Rome, 2020) MSc in Statistics and Decision Sciences (La Sapienza University of Rome, 2016) BSc in Statistics (La Sapienza University of Rome, 2013) Dutch University Teaching Qualification (BKO, 2022) Research Interests: Cavicchia’s work emphasizes hierarchical models, non-parametric statistics, and data science applications. He develops methodologies for composite indicators, including ultrametric Gaussian mixture models and disjoint principal component analysis. His research bridges theoretical advancements with real-world problems, such as waste management in Italian municipalities and ranking European football teams using composite metrics. Grants & Awards: 2024: IFCS Chikio Hayashi Award 2023: ESE Starter Grant (€300,000) 2017: Research Grant for Junior Researchers (€1,270) 2016: PhD Scholarship, La Sapienza University Academic Engagement: Cavicchia serves as IASC Data Analysis Competition Officer (2023–2025), co-edits the ISI Magazine , and organizes conferences like DSSV 2020 and DSSV-ECDA 2021. He is an elected member of the International Statistical Institute and contributes to SVQS’s Sustainability initiatives. Labs & Teams: He co-organizes the Econometrics internal seminars at Erasmus University and collaborates with researchers at University of Naples Federico II on hierarchical models and convex clustering.
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.
Dirk Thierens is an Associate Professor in the Department of Computer Science at Utrecht University's Faculty of Science, specializing in Intelligent Systems within AI & Data Science. His academic career spans over 25 years, with continuous publications from 1996 through 2025, demonstrating sustained research activity and leadership in his field. He maintains an active research program with numerous collaborations, most notably with Peter A.N. Bosman, indicating a long-standing productive research partnership. Thierens' research focuses on evolutionary computation, particularly model-based evolutionary algorithms, genetic algorithms, and optimization techniques. His work has evolved from foundational genetic algorithm research in the late 1990s and early 2000s to more specialized model-based approaches in recent years, including significant contributions to Gene-pool Optimal Mixing Evolutionary Algorithms (GOMEA). His expertise spans single-objective and multi-objective optimization, permutation problems, mixed-integer problems, and real-valued optimization. In recent years, his research has expanded into applications in machine learning, particularly semi-supervised learning and neural network optimization. His publication record shows a consistent output of high-quality research, with numerous papers in top conferences like GECCO and journals in evolutionary computation. His most recent work (2023-2025) demonstrates continued innovation in synthetic data generation, neural network combination techniques, and parameterless evolutionary algorithms. The breadth of his work spans theoretical algorithm development, benchmarking methodologies, and practical applications in healthcare and other domains. While no specific scientific awards are mentioned in the available information, his extensive publication record, tutorial contributions at major conferences, and sustained research productivity over multiple decades indicate recognition within the evolutionary computation community. His tutorial work at GECCO conferences suggests he is considered an authority on model-based evolutionary algorithms. Thierens maintains an active research laboratory focused on evolutionary algorithms and their applications, with recent work exploring the intersection of evolutionary computation and deep learning. His research continues to advance both theoretical understanding and practical applications of optimization techniques in complex problem domains.
Roel Loonen is an Associate Professor at the Unit Building Physics and Services within Eindhoven University of Technology's Department of the Built Environment. Since October 2024, he has joined the EIRES Management Team, leading the Energy Transition in the Built Environment focus area alongside Lenneke Kuijer. His work emphasizes integrating energy efficiency with occupant comfort through advanced building simulation techniques. Academic Affiliation: Eindhoven University of Technology Management Role: EIRES Management Team Research Focus: Adaptive facades, building-integrated renewable energy, and occupant behavior modeling Loonen’s research explores modeling and simulation strategies for buildings that reconcile high indoor quality with minimal environmental impact. Key projects include collaborations with eyrise on solar shading glass and TNO on the ZIEZO project, which experiments with insulated glazing units that combine solar shading with bifacial photovoltaics at the SolarBEAT facility. His work spans computational methods for urban irradiation, occupant-facade interaction frameworks, and multi-domain thermal comfort modeling. The ZIEZO project exemplifies his approach to energy-efficient design by redirecting reflected light to enhance photovoltaic output while maintaining daylight comfort. His publications address challenges in adaptive façade systems, PV integration, and sensitivity analysis for heating demand. Loonen advocates for interdisciplinary collaboration to bridge technical innovation with societal implementation needs. Loonen’s educational contributions include co-teaching the interdepartmental master course Sustainable Energy Technology and developing interactive simulation-based teaching tools. He emphasizes connecting energy-related research to practical implementation through stakeholder engagement with municipalities, building companies, and grid operators. Key facilities involved in his research include SolarBEAT (for testing solar technologies) and TU/e’s simulation laboratories. His work addresses urban energy transitions by combining building physics with grid interactions, occupant behavior, and scalable PV system optimization.
Twan Basten is a Full Professor in the Electronic Systems group at Eindhoven University of Technology (TU/e). He leads research on embedded and cyber-physical systems, focusing on model-driven design, computational models, and system dependability. He holds an MSc (1993) and PhD (1998) in Computing Science from TU/e, advancing from Assistant to Full Professor by 2009, and became the Electronic Systems group chair in 2013. His research spans international projects (FP5-7, H2020, ECSEL) and Dutch initiatives (STW, NWO, RVO), with over 200 publications and seven best paper awards. He has co-supervised 21 PhD students and actively participates in program committees and conferences. His work contributes to UN Sustainable Development Goals through innovations in smart systems. Education: MSc in Computing Science, TU/e (1993) PhD in Computing Science, TU/e (1998) Research Interests: Explores design methodologies for embedded systems, including scenario-based design, real-time scheduling, and performance analysis. Specializes in model-driven engineering and computational models to ensure system dependability. Active in projects like TRANSACT (real-time systems) and SAM-FMS (flexible manufacturing). Key Contributions: Co-author of 1 book and over 200 scientific publications Recipient of seven best paper awards Co-supervised 21 PhD degrees Senior member of IEEE and lifetime member of ACM Labs & Teams: Leads the Model-Based Design Lab and contributes to EAISI High Tech Systems initiatives. Collaborates on tools like TRACE4CPS for execution trace analysis and CReTS for vehicle platooning simulation.
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
Jordan Boyle is an Assistant Professor in the Department of Sustainable Design Engineering at the Faculty of Industrial Design Engineering, Delft University of Technology (TU Delft). He is affiliated with the Materializing Futures Section, where he conducts research in robotics, bio-inspired systems, swarm intelligence, and human-robot interaction. Academic Background: PhD in Computer Science, University of Leeds – focused on neuro-mechanical control of locomotion in C. elegans . MSc and BSc (Hons) in Electrical Engineering, University of Cape Town. Prior academic experience at the University of Leeds as Research Fellow, Lecturer, and Associate Professor over 12 years. Research Interests: Dr. Boyle’s research centers on robotics with a strong emphasis on bio-inspired design. His work spans swarm intelligence , multi-robot systems , human-robot interaction , and robotic fabrication . He also specializes in designing experimental apparatus for pre-clinical and engineering applications. His interdisciplinary approach integrates mechanical design, control systems, and AI for real-world deployment in construction, medicine, and infrastructure. Publication Trends: His recent publications (2022–2024) demonstrate a clear trajectory toward bio-inspired autonomous systems applied in construction and medical imaging. Key themes include swarm robotics for construction, MRI trajectory correction with robotic components, and locomotion mechanisms inspired by biological systems. These works reflect strong interdisciplinary collaboration, particularly with biomedical and mechanical engineering teams. Teaching: Product Engineering (2024, 2025) Advanced Product Engineering (2024, 2025) Scientific Affiliations and Activities: Visiting Researcher, School of Mechanical Engineering, University of Leeds (2022–2026) Advising and Grants: While no formal students or specific grants are listed in the provided text, Dr. Boyle has supervised research projects and collaborated across disciplines, particularly in medical and civil engineering applications. His role in designing experimental apparatus indicates active involvement in grant-funded interdisciplinary research. Labs and Research Groups: He is part of the Materializing Futures Section within Sustainable Design Engineering, which likely operates in conjunction with TU Delft’s broader design and robotics labs, though specific lab names are not mentioned.
Boelie Elzen is an External employee at Wageningen University, affiliated with the OT Team Agriculture & Society and the Agricultural Biosystems Engineering department. Holding a dr.ir. degree, he functions as a Researcher with expertise in agricultural innovation systems and sustainability transitions. His research focuses on: Innovation Systems in Agriculture Environmental Impact Assessment Sustainability Transitions Agricultural Policy Mixed Cropping Systems Agroecology Recent publications (2022-2025) demonstrate interdisciplinary work spanning computer science history and agricultural innovation, with emphasis on mixed cropping, AKIS frameworks, and EU agroecology strategies that bridge social sciences and agronomy. Elzen has led significant projects including: EU-TU-18037 PLAID (2017-2019) Internationale workshop Systeeminnovaties (2010) Toolkit M&E voor projectleiders (2010) Leren van praktijkinitiatieven (2009) These initiatives address system innovations in agriculture through monitoring tools and practical learning frameworks. He actively contributes to the OT Team Agriculture & Society and Agricultural Biosystems Engineering, driving research on sustainable agricultural transitions.
Dr. Sabbir Ahmed is a Researcher at the Faculty of Science , Utrecht University , specializing in Pharmacology . His research focuses on kidney disease biomarkers, uremic toxins, and cross-disciplinary applications in autism spectrum disorder. BSc in Pharmacy from East West University MSc in Toxicology from Karolinska Institute Research expertise includes: Kidney disease biomarker development High-Performance Liquid Chromatography (HPLC) Pathological analysis of animal models Molecular biology of uremic toxins Drug-induced toxicity testing His publications span Life Sciences , Pharmacology , and Toxicology , with recent work on gut microbiota interactions in kidney disease and autism. Contact: s.ahmed@uu.nl .
Jürgen Pfeffer is a Professor of Computational Social Science & Big Data at the Technical University of Munich's School of Social Sciences and Technology, with an additional appointment as Adjunct Professor at Carnegie Mellon University's Institute for Software Research. His interdisciplinary work bridges computer science and social science with a focus on analyzing large-scale socio-technical systems. His research expertise spans computational social science, network analysis, and big data methodologies. Pfeffer's work examines methodological, algorithmic, and theoretical challenges in analyzing dynamic social systems, with current projects focusing on modeling and detecting negative dynamics from social media, particularly online firestorms and hate speech against politically active women. His research combines network science approaches with computational methods to understand complex social phenomena. Pfeffer's publication record demonstrates significant contributions to the field since his 2010 doctorate, with high-impact papers in journals like Science and EPJ Data Science. His work on social media analysis, particularly the influential 2014 Science paper 'Social Media for Large Studies of Behavior' co-authored with Derek Ruths, has shaped methodological approaches in the field. His research shows consistent evolution from foundational network analysis to contemporary applications in political discourse, hate speech detection, and multi-layer network analysis. Hennig, M., Brandes, U., Pfeffer, J., & Mergel, I. (2012). Studying Social Networks. A Guide to Empirical Research Ruths, D., & Pfeffer, J. (2014). Social Media for Large Studies of Behavior Pfeffer, J., Morstatter, F., & Mayer, K. (2018). Tampering with Twitter's Sample API As an advisor and collaborator, Pfeffer has worked extensively with researchers including Raji Ghawi, Mirco Schönfeld, Momin Malik, and Kathleen Carley. His work demonstrates strong connections between theoretical network science and practical applications in social media analysis. His current research continues to address pressing issues in online discourse, with recent work focusing on hate speech classification, lexical change in negative word-of-mouth, and polarization dynamics in social media environments. Pfeffer leads the Pfeffer Lab, which focuses on developing methodological approaches for analyzing complex social systems through computational methods. His work has implications for understanding political legitimacy, social influence, and community dynamics in both online and offline contexts.
