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Prof. Dr. Pinar Yolum Birbil is a leading researcher in Responsible AI at the Faculty of Science , Utrecht University . Her work bridges Artificial Intelligence , Privacy and Data Protection , and Software Agents , focusing on Human-Centered AI and Trustworthy Systems . She is part of the AI & Data Science and Responsible AI research groups. PhD in Computer Science, North Carolina State University (2003) MS in Computer Science, North Carolina State University (2000) BSc in Computer Engineering, Marmara University (1998) Her research explores Privacy Preservation in collaborative systems, Computational Theory of Mind for human-agent coordination, and Norm-Based AI Systems . Recent projects include the Hybrid Intelligence Center and tools like PANOLA for privacy support. She investigates how AI can balance user autonomy , ethical norms , and societal values in applications ranging from urban planning to healthcare. Her scientific publications (2023-2025) span topics like Explainable Privacy , Trust in Hybrid Teams , and AI for Diabetes Management , appearing in venues such as JAIR , AAMAS , and ACM TOIT . She emphasizes collaborative AI , with contributions to multiagent simulations and privacy-preserving mechanisms . Scientific Awards : NC State University Alumni Hall of Fame (2017) Woman Entrepreneur of the Year (Microsoft Turkey & KAGIDER, 2017) Bogazici University Academic Encouragement Award (multiple years) Best Paper Award at ESAW 2009 She supervises a dynamic research group with PhD students working on Hybrid Intelligence , Computational Ethics , and Privacy Modeling . Her projects often involve interdisciplinary collaboration with institutions like TNO and the Transforming Cities Hub .

• Artificial Intelligence
• Privacy and Data Protection
• Trustworthy AI
• +4 more

Qian Tao is an Assistant Professor at the Department of Imaging Physics , Faculty of Applied Sciences , Delft University of Technology . She previously worked at the Division of Image Processing, Department of Radiology, Leiden University Medical Center from 2009 to 2020. Academic Background: BSc in Electrical Engineering (Fudan University), MSc in Biomedical Engineering (Fudan University), PhD in Biometric Authentication (University of Twente) Research Interests: Focus on trustworthy AI methodologies for critical healthcare applications, including medical imaging for patient diagnosis and clinical intervention. Specializes in cardiac MRI analysis, image-guided interventions for cardiac arrhythmias, and AI in Radiology. Publication Trends: Recent work emphasizes motion correction in cardiac MRI, deep learning for image registration, and novel techniques like TRAFF2 mapping. Keywords include Medical Imaging , Machine Learning , Cardiac MRI , and Quantitative Analysis . Contact: Email: Q.Tao@tudelft.nl

• Artificial Intelligence
• Medical Imaging
• Cardiac MRI Analysis
• +3 more

Nezihe Merve Gürel is an Assistant Professor in Computer Science at Delft University of Technology (TU Delft), affiliated with the Pattern Recognition & Bioinformatics Group within the Intelligent Systems Department of the Faculty of Electrical Engineering, Mathematics and Computer Science. Her research focuses on developing robust, reliable, and efficient machine learning methods with enhanced reasoning capabilities, bridging theoretical rigor and practical applications. She emphasizes data-centric approaches to improve ML systems. Education: PhD in Computer Science from ETH Zurich, MSc from EPFL (Switzerland). Research Interests: ML robustness, reliability, reasoning, data-centric ML, federated learning, and explainable AI. Her recent work includes certified robustness for retrieval-augmented models and time-efficient learning algorithms. She has contributed to the Journal of Data-centric Machine Learning Research as an executive editor and served as a reviewer for top ML conferences (NeurIPS, ICML, ICLR). She previously held roles at IBM Research, Stanford University's Human-Centered AI Lab, and Westlake Institute for Advanced Study. Her awards include the Generation Google Scholarship and Cisco Research Funding . Scientific Awards : Generation Google Scholarship (2021) Cisco Research Center University Funding Labs & Teams : She leads research in the Pattern Recognition Laboratory at TU Delft and collaborates with international institutions like Stanford and Westlake Institute for Advanced Study.

• Machine Learning
• Computer Science
• Pattern Recognition
• +4 more

Dr. Peter Lloyd is a Professor of Integrated Design Methodology at Delft University of Technology's Industrial Design Engineering department. He specializes in design process, design ethics, and AI collaboration in design. His recent work explores human-AI interaction in creative processes. He chairs the Design Research Society and is Editor-in-Chief of Design Studies . Lloyd teaches courses like 'Design Processes and...' and 'Exploring Design...'. He leads the Designing Intelligence Lab, focusing on human-AI collaboration in complex design challenges. His research spans design methodology, ethical AI, and interdisciplinary innovation. Key contributions include advancing design thinking frameworks, co-chairing the Design Thinking Research Symposium since 1994, and publishing influential articles on AI-driven design practices. He collaborates with industry on projects like BMW car interiors and aircraft seat design.

• Design Process
• Design Ethics
• Design Theory and Methodology
• +4 more

Sofie Haesaert is an Assistant Professor in the Control Systems group at the Department of Electrical Engineering, Eindhoven University of Technology. Her work focuses on formal verification and control synthesis methods for cyber-physical systems, particularly through stochastic simulation relations and temporal logic specifications. Education: BSc (cum laude) and MSc (cum laude) in Mechanical Engineering and Systems & Control from Delft University of Technology; PhD from Eindhoven University of Technology (2017) Experience: Postdoctoral researcher at Caltech (2017-2018), then returned to TU/e as Assistant Professor Her research interests include: Cyber-physical systems verification Stochastic control methods Temporal logic specification Markov decision processes Formal methods in control engineering Model abstractions and simulation relations Recent publications show strong focus on: Stochastic temporal logic control Robust and risk-aware control Multi-agent system verification Formal synthesis via simulation relations AI integration in control systems Software tools for formal control Scientific achievements: Veni Grant recipient (2020) Co-developer of the SySCoRe toolset for stochastic control synthesis Contributor to formal verification benchmarks through ARCH-COMP reports She contributes to education through courses on: Control principles for engineered systems Control challenges in autonomous racing Supervisory control of cyber-physical systems Haesaert collaborates across disciplines including computer science, applied mathematics, and robotics, with over 750 citations and significant contributions to formal control theory for stochastic systems. Her work bridges theoretical developments with practical applications in autonomous systems and complex control architectures.

• Cyber-Physical Systems
• Control Theory
• Formal Verification
• +3 more

Prof. Dr. Ben Wagner is a Professor of Media, Technology & Society at Inholland University, Director of TU Delft's AI Futures Lab on Rights and Justice, and Professor of Human Rights & Technology at IT:U. His work bridges social sciences, technology, and human rights, focusing on digital governance, AI ethics, and societal impacts of technological change. He holds a PhD from the European University Institute (2013) and has led institutions like the Center for Internet & Human Rights (Viadrina) and the Sustainable Computing Lab (WU Wien). Key initiatives include Inholland's Digital Rights Research Team (DRRT), Sustainable Media Lab (SML), and contributions to the European Cloud for Heritage OpEn Science (ECHOES). His research emphasizes designing accountable tech systems, digital rights frameworks, and sustainable digital infrastructures. Recent work addresses gaps between legal/ethical guidelines and public sector data practices, AI governance across nations, and audit mechanisms for platform transparency. Awards include the 2023 Best Paper Award at HICSS for AI governance research and a 2013 Best Student Paper at Internet Science. Collaborations span academia, governments, and industries to shape equitable tech policies. Active in advisory roles for ENISA, Patterns Journal, and the UKRI Trustworthy Systems Hub.

• Digital Rights
• AI Governance
• Human Rights & Technology
• +3 more

Virginia Dignum serves as Professor of Responsible Artificial Intelligence at Umeå University, Sweden, leading the AI Policy Lab. She is a globally recognized authority advising the Wallenberg Foundations, IEEE, ACM, UNESCO, OECD, EU High-Level Expert Group on AI, and UN High Level Advisory Body on AI. Her research focuses on ethical frameworks for autonomous systems and responsible AI governance. She investigates societal impacts of AI deployment, policy development for trustworthy systems, and cross-border regulatory alignment, emphasizing human-centric approaches in algorithmic decision-making. Dignum co-chairs the IEEE Global Initiative on Ethics of Autonomous Systems and chairs the ACM Technology Policy Council. She founded ALLAI (Dutch AI Alliance) to promote ethical AI practices nationally and serves as an IWM Visiting Fellow in the Digital Humanism Program. She leads the AI Policy Lab at Umeå University and is authoring the forthcoming book The AI Paradox (Princeton University Press, 2025), analyzing tensions between AI innovation and societal values.

• Responsible Artificial Intelligence
• AI Ethics
• AI Policy
• +3 more

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)

• Data Fusion
• Intelligent Interaction
• Fuzzy Logic
• +4 more

Lotte Asveld is an Associate Professor at Delft University of Technology, specializing in the societal aspects of biotechnological innovations. Her work focuses on responsible innovation in biotechnology and synthetic biology, addressing moral, technical, and institutional uncertainties, integrating societal values into innovation trajectories, and analyzing actor responsibilities. Her research spans the bioeconomy, social sustainability, and risk governance. Key themes in her recent publications include: Responsible innovation frameworks and ethical education Inclusive design in biobased value chains Social Life Cycle Assessment (SLCA) for justice in bioeconomy Safe-by-Design (SbD) principles and risk management Digital Sequence Information (DSI) equity and bioethics

• Responsible Innovation
• Synthetic Biology
• Ethics of Technology
• +5 more

Dr. Juan Durán is an Assistant Professor at the Faculty of Technology, Policy and Management at TU Delft, specializing in the philosophy of science and technology. His work focuses on computer simulations, AI ethics, Big Data, and the epistemological challenges in computational science. Education: Bachelor/Master in Computer Science and Philosophy, National University of Córdoba (Argentina) PhD in Cluster of Excellence SimTech, University of Stuttgart (Germany) Research interests: Epistemic opacity and trust in algorithms Ethics of medical AI and machine learning Computational reliabilism as a framework for justification Dark data and scientific data governance Teaching roles include courses on ethics and engineering, IT and values, and philosophy of science. His publications include influential works like Computer Simulations in Science and Engineering (2014) and co-authored papers on AI trustworthiness and medical epistemology.

• Philosophy of Science
• Computer Simulations
• AI Ethics
• +4 more

Maurice van Keulen is an Associate Professor affiliated with the University of Twente's research institutes including Datamanagement & Biometrics, Digital Society Institute, and TechMed Centre. His multidisciplinary work bridges computer science, healthcare, and social systems. Research Focus: Van Keulen specializes in artificial intelligence applications with emphasis on: Data management (quality, integration, probabilistic databases) Explainable AI and interpretable machine learning models Healthcare informatics (cancer prediction, medical imaging, outcome analysis) Natural language processing and social media analytics His recent work explores dynamic sparse training, meta-learning for data imputation, and ethical AI frameworks. Publication Trends: Recent articles (2023-2025) show strong focus on: Interpretable AI methods in healthcare diagnostics Robust machine learning under data corruption Meta-learning approaches for data preprocessing 3D medical imaging and reconstruction techniques Awards: Beste paper award (2018) for work on probabilistic data conditioning Supervision & Activities: Has supervised 14 research projects and serves on executive boards including EDBT (Extending DataBase Technology) and IFIP WG 2.6. Leads research on ethical dimensions of AI systems.

• Artificial Intelligence
• Data Mining
• Process Mining
• +7 more

Dr. Zhiming Zhao is an Associate Professor and Chair of the Multiscale Networked Systems (MNS) research group at the Informatics Institute (IvI), University of Amsterdam (UvA). He serves as the technical manager of the Virtual Lab and Innovation Center (VLIC) of LifeWatch ERIC, a European research infrastructure for ecology and biodiversity science. Zhao holds an IEEE Senior Member designation and is the Managing Editor of the Journal of Cloud Computing . He earned his Ph.D. in Computer Science from UvA in 2004. His research focuses on quality-critical distributed computing, data-intensive workflows, virtual research environments, and digital twins. He leads projects such as LTER-LIFE (Dutch research infrastructure for digital twins) and coordinates UvA contributions to EU initiatives like ENVRI-HUB Next , EVERSE , and BlueCloud-2026 . Zhao’s work spans technical development in EU projects (e.g., ENVRI-FAIR , ARTICONF , CLARIFY ) and leadership roles in international workshops and conferences. His team develops frameworks like NaaVRE (Jupyter-based collaborative environments) and CloudsStorm (dynamic infrastructure planning). Current research emphasizes trustworthy AI in cloud systems, federated learning, and edge-cloud resource optimization. Key achievements include over 150 peer-reviewed publications, supervision of numerous PhD students, and contributions to open science initiatives. His lab actively explores interdisciplinary applications in environmental science, medical imaging, and blockchain-based decentralized systems.

• Distributed Systems
• Cloud Computing
• Data-Intensive Workflow Management
• +5 more

R.L. Lagendijk serves as a Professor within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology, specializing in Cyber Security research. His academic profile demonstrates sustained leadership in privacy-enhancing technologies and cryptographic systems development across diverse application domains. His core research spans Cyber Security, Cryptography, and Privacy-Preserving Computation with specialized expertise in Differential Privacy and Algorithmic Security. Lagendijk pioneers practical implementations for sensitive data protection in supply chain logistics, healthcare diagnostics, and blockchain infrastructure, consistently bridging theoretical cryptography with real-world security challenges through innovative protocol design. Analysis of his publication trajectory since 2020 reveals concentrated advancement in differential privacy applications, particularly for trajectory data obfuscation in supply chains and bin-packing optimization in logistics. His work increasingly integrates blockchain security with AI ethics frameworks, demonstrating evolving focus toward human-centric privacy solutions in emerging technologies. His distinguished career includes recognition through significant professional honors: NAE Fellow (2023) Professor Lagendijk has guided 44 students through academic supervision while actively leading European research initiatives including H2020 IRIS, SPECIES, and SESAME projects. His editorial contributions to IEEE Transactions on Information Forensics and Security underscore his influence in shaping cryptographic standards. As a core member of TU Delft's Cyber Security research group, he drives collaborative innovation in privacy-preserving computation through both theoretical exploration and industry-engaged solutions development, maintaining active participation in national and international cybersecurity discourse.

• Cyber Security
• Cryptography
• Privacy-Preserving Computation
• +4 more

Dr. Asteris Apostolidis is a Senior Research Fellow at the Faculty of Technology, Amsterdam University of Applied Sciences (AUAS), and serves as Lead for Technical Innovation at KLM Royal Dutch Airlines. His expertise spans aviation engineering, artificial intelligence, and sustainable technologies. He co-chairs the SAE G-34/EUROCAE WG-114 committee for AI certification in aviation and has held roles including Associate Professor and Lab Lead at AUAS. Education: PhD in Computational Aerothermodynamics (Cranfield University, 2015) MSc in Aerospace Propulsion (Cranfield University, 2010) Dipl.-Ing. in Mechanical Engineering (Aristotle University of Thessaloniki, 2009) His research focuses on Aviation Maintenance, Sustainable Aircraft Architectures, AI Integration, and Digital Twins . He has pioneered projects with KLM, Airbus, and Rolls-Royce, addressing challenges like gas turbine performance modeling and predictive maintenance. His recent work explores electric aircraft operations for interisland mobility and trustworthy AI-driven prognostics for gas turbines. Awards: Recipient of the Outstanding Contribution in Reviewing (2019), MSc Research Grant (2009), and PhD Research Grant (2010). Collaborations: Active partnerships with TU Delft, Royal Netherlands Aerospace Centre (NLR), and SAE International. Leads initiatives in innovation strategy and sustainability transformation across academia and industry.

• Aviation Maintenance, Repair & Overhaul
• Novel Aircraft & Propulsion Architectures
• Sustainability in Aviation & Aeronautics
• +4 more

Gwenn Englebienne is an Assistant Professor at the Digital Society Institute and Human Media Interaction group of Utrecht University. Their research focuses on Artificial Intelligence, Computer Vision, and Human-AI Interaction, with applications in robotics, health, and social computing. They have contributed to over 80 research outputs since 2007, emphasizing embodied AI, social robotics, and explainable machine learning. Research interests span activity recognition, teleoperation systems, and ethical AI design. Notable work includes developing GNN-based group detection algorithms and evaluating chatbot reliability through automated question-answering frameworks. Their studies often bridge technical innovation with human-centered design, such as measuring embodiment via pupil dilation or addressing asymmetry in video-conferencing interactions. Key collaborations include work on social robotics, telepresence systems, and health monitoring using ambient sensors. Publications span conferences like IDA, CogMI, and LREC-COLING, reflecting interdisciplinary impact. A dataset on robot social positioning behavior is publicly accessible via 4TU.Centre for Research Data. Current work explores semi-supervised domain adaptation, spiking neural networks, and the psychological dimensions of AI trustworthiness. They lead initiatives in the Digital Society Institute to align technological advancements with societal needs.

• Artificial Intelligence
• Computer Vision
• Natural Language Processing
• +5 more