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
Anna Wilbik is a Professor in Data Fusion and Intelligent Interaction at the Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University (The Netherlands). Her research bridges data understanding and human-machine synergy in complex systems, focusing on multi-criteria decision making, explainable AI, and data fusion techniques. PhD in Computer Science (with honors), Systems Research Institute, Polish Academy of Science (2010) Postdoctoral Fellow, University of Missouri (2011) Stanford University TOP500 Innovators Program Alumnus Research Pillars: Intelligent human-machine interaction for joint decision making Data fusion methods for heterogeneous data integration Contextualized multi-criteria decision frameworks Fuzzy logic and linguistic summaries for explainability Federated learning systems Article Trends: Recent work focuses on intuitionistic fuzzy sets for knowledge-intensive processes, federated learning with uncertainty handling, and linguistic summarization techniques for interpretable AI. She actively explores explainability , collaborative business models , and driver behavior analysis through attention-based models. Professional Leadership: Vice-chair of IEEE Fuzzy Systems Technical Committee Organizer of IEEE World Congress on Computational Intelligence (2024)
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.
Prof. Floris de Lange is a Professor at the Donders Institute for Brain, Cognition and Behaviour, Radboud University, and holds a part-time W3-Professorship in Cognitive Computational Neuroscience at the University of Bonn. His research focuses on understanding how top-down factors like goals, attention, expectations, and prior knowledge shape perception, cognition, and decision-making. He uses behavioral and neuroimaging techniques (MEG, fMRI, TMS) to study these processes in healthy and pathological brains. Key research themes include predictive perception, attention, and the neural mechanisms underlying decision-making. His work has been supported by prestigious grants such as the Vici and ERC Consolidator Grants. He teaches courses on Attention and Prediction, Cognitive Control, and Neurophysiology of Cognition and Behaviour. Education: Not explicitly stated in the provided text. Awards: Vici Grant (NWO), ERC Consolidator Grant, Ammodo Science Award, and others. Labs/Teams: Leads the Predictive Perception and Cognition group within the Donders Institute. His research highlights the brain’s predictive nature, demonstrating how expectations modulate sensory processing in early visual cortex and influence decision-making. He collaborates internationally, including an adversarial testing project on theories of consciousness.
Dr. Joris Demmers is an Associate Professor and Head of the Marketing Department at the Faculty of Economics and Business , University of Amsterdam. His research focuses on consumer behavior, privacy in digital environments, environmental sustainability in marketing strategies, and the intersection of technology with consumer engagement. He leads academic initiatives in marketing while exploring interdisciplinary topics like corporate greenwashing detection and data governance. Key Research Themes: Consumer preferences for eco-labels and corporate social responsibility Privacy risks vs. behavioral incentives in financial and social media contexts Visual content analysis and machine learning applications in marketing Data-sharing dynamics and ethical marketing practices Recent Trends in Publications: Recent work emphasizes greenwashing detection (e.g., GreenScreen dataset), psychological drivers of financial self-disclosure, and the role of digital tools in customer journeys. Earlier studies delve into consumer data-sharing motivations and ethical privacy frameworks. Awards and Activities: No awards explicitly mentioned. Active in teaching and ancillary academic activities related to marketing strategy and digital media. Lab/Team Affiliations: No specific labs or teams listed, but collaborates across disciplines, including molecular biology studies on aging mechanisms (possibly through interdisciplinary projects).
Dr. Sjoukje Osinga is an Assistant Professor in the Information Technology group at Wageningen University's Department of Social Sciences. Her research focuses on computational social science, natural language processing (NLP), and big data applications in agriculture. She holds a PhD from Wageningen University on agent-based modelling of knowledge management in the pig sector, with fieldwork in China. She contributed to EU H2020 projects like Cybele (big data in agriculture) and Dragon (knowledge transfer of ABM tools). She is a member of the SiLiCo Centre, specializing in simulating complex systems through agent-based simulations. Education: Artificial Intelligence and Cognitive Science (Groningen and Leuven, 1991) Research interests include agent-based modelling, big data analytics for agriculture, machine learning, and knowledge management. She explores topics like digital twins in health and agriculture, and sentiment analysis in policy-making. Her work bridges technical innovation with societal challenges, such as sustainable farming practices and compliance strategies in regulatory environments. Publications span agent-based models for pork supply chains, machine learning applications in crop forecasting, and digital twin frameworks for agriculture. She actively engages in interdisciplinary projects addressing data integration and policy implications of emerging technologies.
Dr. Franziska Pannach is an Assistant Professor at the Faculty of Arts of the Rijksuniversiteit Groningen (Netherlands), affiliated with the Centre for Language and Cognition (CLCG) . She specializes in Computational Linguistics , Digital Humanities , and Semantic Web Technology , focusing on modeling narratives and mythological texts using NLP and ontologies. Her work emphasizes Open Science and cross-cultural comparisons of folktales and myths. Education : Dr. rer. nat. (PhD) in Applied Computer Science (Digital Humanities/Computational Linguistics), University of Göttingen, Germany (2024) MSc in Applied Computer Science (Digital Humanities), University of Göttingen (2019) BSc in Applied Computer Science (Computational Neuroscience), University of Göttingen (2013) Research Interests include computational approaches to mythological and folkloristic analysis, shallow ontologies for narrative comparison, and semantic web applications in cross-cultural studies. She leads the GOLEM (Graph Ontologies for Literary Evolution Models) project and contributed to the STRATA (Stratification Analyses for Mythic Narrative Materials) project. Teaching : Semantic Web Technology (MSc), Database-driven Web Technology (BSc), Digital Humanities: Tools and Methods (MSc), Analysing Data (MSc) Supervision : PhD students Pritha Majumdar (RUG) and Kristina Schneider (RUG/University of Mainz) Publications focus on narrative ontologies, mythological event annotation, and NLP for under-resourced languages. Her recent work includes the GOLEM Triple Store and shallow ontologies for myth comparison. Networks & Memberships : Member, Digital Humanities Association of Southern Africa (DHASA) since 2019 Co-founder and editor of the Journal of Digital Humanities Association of Southern Africa (JDHASA) Collaborations with institutions in Germany, South Africa, and Denmark
Aaqib Saeed is an Assistant Professor in the Department of Industrial Design at Eindhoven University of Technology. His research focuses on Human-Centric AI, Federated Learning, Self-Supervised Learning, and Audio Understanding, with applications in Personal Health. He holds a PhD (cum laude) from TU/e and an MSc (cum laude) from the University of Twente. Education: PhD in Computer Science (cum laude), TU/e (2021) MSc in Computer Science (cum laude), University of Twente (2018) Research Interests: Development of robust federated learning frameworks for decentralized data Self-supervised learning for audio and physiological signal analysis AI-driven solutions for healthcare monitoring Key Contributions: DeltaMask: Reducing communication overhead in federated fine-tuning FedNS: Mitigating noisy decentralized data in federated learning Labeling Chaos to Learning Harmony: Handling label noise in FL Professional Experience: Visiting Industrial Fellow, University of Cambridge (2023) Research Scientist, Philips Research (2019–2023) Research Internships: Google Research, TNO/EIT Digital Awards: UT Scholarship (MSc) Cum Laude awards for both PhD and MSc Labs/Teams: EAISI Health, EAISI Foundational, Computational Design Systems.
Prof. Peter van der Heijden is a Professor of Statistics for the Social and Behavioural Sciences at Utrecht University's Department of Methodology and Statistics. He also holds a professorship in Social Statistics at the University of Southampton. His roles include chairing the Ethical Review Board and the Committee for Policy on Integrity at Utrecht's Faculty of Social and Behavioural Sciences. He chairs the Advisory Council on Methodology and Quality of Statistics Netherlands and serves on the Executive Board of the European Statistical Advisory Committee (ESAC). Since 2017, he has led Utrecht's Applied Data Science focus area, focusing on human-centered AI and data-driven solutions. His research emphasizes population size estimation, fraud detection, and categorical data analysis, with applications for Dutch ministries and international bodies like the UN. He has pioneered methods for estimating human trafficking victims and optimizing healthcare treatments using multilevel models and neural networks. Key projects include the AI for Health initiative with Utrecht Medical Center and Wageningen University. His work bridges statistical rigor with societal impact, addressing challenges in criminal justice, public health, and policy-making through innovative methodologies. Universities: Utrecht University (Primary), University of Southampton Key Committees: European Statistical Advisory Committee, UN Human Trafficking Monitoring Research Themes: Multiple Systems Estimation, Data Science for Social Issues Research interests span statistical methods for complex societal problems, including: Register linkage and fraud detection Machine learning applications in healthcare Human trafficking prevalence estimation His publications (2019-2023) highlight advancements in multilevel modeling, randomized response techniques, and AI-driven clinical data classification. He has advised on policy frameworks for official statistics and contributed to global initiatives like the UN Sustainable Development Goals (Target 16.2). Grants and collaborations include projects with Dutch ministries, the EU, and international organizations. Current initiatives involve optimizing Hepatitis C treatment networks and improving criminal recidivism prediction models. His leadership in interdisciplinary teams ensures methodological innovation addresses real-world challenges.
Dr Sandro Pezzelle is a researcher affiliated with the Natural Language Processing & Digital Humanities (NLP&DH) group at the Institute for Logic, Language and Computation (ILLC), Faculty of Science, University of Amsterdam. His secondary affiliation lies with the Language & Music Cognition (LMC) research unit. His research focuses on Natural Language Processing , leveraging Artificial Intelligence and Deep Learning for Language to advance computational linguistics and digital humanities. His work intersects Computational Linguistics and Linguistic Data Analysis , emphasizing interdisciplinary applications. Dr Pezzelle maintains an active research profile at the ILLC, a leading interdisciplinary institute connecting humanities, social sciences, and computer science. Detailed information about his publications and projects can be found on his personal homepage .
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.
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.
Sezer Karaoglu is a Lecturer and part-time postdoctoral researcher at the Computer Vision Group, Informatics Institute, University of Amsterdam. He is also the CTO and Co-Founder of 3DUniversum, a technology spin-off of the University of Amsterdam that provides state-of-the-art 2D/3D computer vision solutions. Additionally, he has co-founded other startups including Scanm and 3DHealthScan. Dr. Karaoglu received his PhD from the Computer Vision Group, Informatics Institute, University of Amsterdam, with research funded by the COMMIT project. His educational background includes a double master's degree: an optics, image and vision master's degree from University Jean Monnet in France and a media technology master's degree from Gjovik University College in Norway. He completed his undergraduate studies with honors at Istanbul Technical University in Telecommunication Engineering. His research focuses on Artificial Intelligence and 3D Computer Vision, with specific interests in SLAM, re-localization, 3D reconstruction, 3D object detection and segmentation, synthetic media, generative AI, deep fake creation and detection, and VR/AR technologies. His work has significant applications in healthcare, particularly in using deepfake technology for therapy for victims of sexual violence-related PTSD and moral injury, as documented in a Frontiers in Psychiatry article. Analyzing his recent publications reveals a strong trend toward neural scene reconstruction, intrinsic image decomposition, and the application of diffusion models to computer vision problems. His research increasingly integrates 3D scene understanding with language models, as evidenced by his work on language-to-3D scene generation. The applications span from healthcare (deeptherapy.ai) to media authenticity (deepfake detection) and industrial applications. ICT.OPEN Poster Award (3rd Position), Oct'13 Pascal VOC'12 Classification challenge, 2nd Position, Sep'12 Pascal VOC'12 Detection challenge, 3rd Position, Sep'12 Best project award at Nokia and CIMET project competition Outstanding reviewer at CVPR'21 PROVADA Future Startup Battle winner Best Dutch AI startup by Valuer Dr. Karaoglu has supervised numerous PhD, Master's, and Bachelor's students, demonstrating his commitment to academic mentorship. His research has attracted significant media attention, with features on Dutch national TV programs including NPO, VPRO, RTL, and international outlets like BBC News. He has received research funding through the COMMIT project during his PhD studies and has successfully translated his research into commercial applications through his startups. His work on deepfake technology has been applied in innovative therapeutic contexts through DeepTherapy.ai, showing the real-world impact of his research. Dr. Karaoglu leads research efforts at the Computer Vision Group Amsterdam and through his company 3DUniversum, which has developed applications like weScan, DeepTherapy, and FairFake.ai. His team collaborates with various institutions including the Netherlands Film Academy for grief therapy applications using deepfake technology. The DeepTherapy project represents a particularly impactful application of his work, using deepfake technology to help victims of sexual violence confront perpetrators in therapeutic settings.
Rianne de Heide is an Assistant Professor in the Statistics group (STAT) within the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science. She maintains collaborative arrangements with LUXs Data Science in Leiden, CWI, and VU Mathematics in Amsterdam as a guest researcher while working partly remotely during her family's relocation. Her academic journey includes a previous position as Assistant Professor at Vrije Universiteit Amsterdam. PhD Dissertation: 'Bayesian Learning: Challenges, Limitations and Pragmatics' (2020) MSc Thesis: 'The Safe-Bayesian Lasso' (2016) De Heide's research spans multiple interconnected domains within statistics and machine learning, with particular emphasis on developing mathematically rigorous frameworks that remain accessible to diverse audiences. Her work bridges theoretical foundations with practical applications, focusing on hypothesis testing with e-values, Bayesian learning methodologies, and best-arm identification problems in multi-armed bandit settings. She demonstrates exceptional interdisciplinary range, connecting statistical theory with philosophical inquiry and even theological discussions as evidenced by her publications on biblical authorship verification and mathematical beauty. Analysis of her publication trajectory reveals a clear evolution toward developing anytime-valid statistical methods, particularly through e-values and e-processes for multiple testing scenarios. Her recent work shows increasing focus on foundational questions in statistical inference while maintaining strong connections to practical machine learning applications. The 2024 'Safe Testing' paper in the Journal of the Royal Statistical Society represents a significant contribution that generated a formal discussion meeting. VENI project 'E-values for Multiple Testing' NWO M2 grant of €742,708 with Jelle Goeman (funding 2 PhD students and a scientific programmer) 2025 Bernoulli Society New Researcher Award De Heide actively supervises research through her VENI project and the NWO M2 grant, while also contributing to broader academic service through the 'Kindness and Excellence in Academia' initiative she co-founded. This initiative addresses critical cultural issues in academic environments through opinion pieces, resources, and community building around compassionate academic practices. She has organized specialized events like the E-Day meet-up for e-value researchers at CWI in Amsterdam, demonstrating leadership in her niche research community. Her research activities are centered around the Statistics group at the University of Twente, with significant external collaborations through the E-mailing list for e-value researchers and partnerships with institutions including CWI, VU Amsterdam, and Leiden's LUXs Data Science. The interdisciplinary nature of her work creates connections across mathematics, computer science, philosophy, and even religious studies.
Jean Wagemans is a Professor at the University of Amsterdam's Faculty of Humanities, where he leads research in the Department of Speech Communication, Argumentation Theory and Rhetoric. His work specializes in the interdisciplinary study of argumentative discourse, bridging philosophy, linguistics, and computational analysis. He maintains an active research profile with recent publications exploring AI-generated argumentation, legal/medical discourse, and digital misinformation. Wagemans' research centers on argumentation theory, rhetoric, and debate, with emphasis on practical applications in AI ethics, healthcare communication, and public discourse. His recent investigations include: Developing computational models like Adpositional Argumentation (AdArg) for natural discourse analysis Examining ethical frameworks for AI-generated arguments Creating argument-checking methodologies to combat misinformation Analyzing normative structures in public deliberation His scholarly publications (2022-2024) demonstrate a distinct trajectory toward computational argumentation, with recurring themes of AI ethics, misinformation detection, and applied discourse analysis. Recent works systematically address: The intersection of argumentation theory with AI systems Methodologies for evaluating reasoning in natural language Cross-disciplinary applications in law, medicine, and digital humanities
