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Jeroen J.L. Schepers is an Associate Professor at Eindhoven University of Technology, affiliated with the Department of Industrial Engineering and Innovation Sciences and the Innovation Technology Entrepreneurship & Marketing group. He also holds a position within the EAISI Foundational research institute. His current educational activities include teaching courses on Marketing Perspectives on Product Innovation, Servitization and Customer Experience, and Multivariate Statistics. Dr. Schepers specializes in the digitalization and robotization of frontline services, with particular focus on the psychological responses of both customers and employees to service technologies. His research examines how AI and robotics transform service encounters, exploring questions about customer adoption of service innovations, the impact of technology on service value, and the optimal collaboration between humans and technology in service delivery. His work spans service marketing, human-robot interaction, consumer psychology, and digital service transformation, with particular emphasis on service robotics, customer experience design, and the psychological mechanisms underlying technology adoption in service contexts. His recent publications reveal a strong focus on human-AI collaboration in service contexts, with recurring themes around service robot design, customer perception of technology, and the psychological mechanisms that determine service success or failure. His research demonstrates how service robots impact customer experience, how frontline employees interact with AI systems, and how service encounters can be optimized through intelligent technology integration. The work spans both theoretical development and practical applications across various service industries. Dr. Schepers has received numerous prestigious awards including the Robert Johnston Outstanding Reviewer Award (2024), multiple highly cited papers in the top 1% of Economics & Business, the Insights Scientist of the Year award (2023), and several Industria Education Awards. His research has been recognized with Best Paper Awards from leading service marketing journals and conferences. HybridLabs - €10.4M grant accelerating Dutch innovations in offshore renewables D-SIDE: Decision Support for Intelligent Damage Estimation and Control (€1.1M) 3TU.CEE research project Grant from DAAD for visiting scholarship at University of Mannheim Dr. Schepers leads research within the Innovation Technology Entrepreneurship & Marketing group, focusing on how digital technologies transform service encounters and employee-customer interactions. His work examines both customer-facing service technologies and employee-facing intelligent systems, investigating how these technologies reshape service delivery, customer experience, and employee work practices in the digital age.

• Service Marketing
• Service Robotics
• Human-AI Interaction
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Yingqian Zhang is an Associate Professor in the Information Systems group at the Industrial Engineering and Innovation Sciences department of Eindhoven University of Technology (TU/e). She is affiliated with the Eindhoven Artificial Intelligence Systems Institute (EAISI), specifically with the EAISI High Tech Systems and EAISI Foundational groups. Her research focuses on applying Artificial Intelligence to solve complex decision-making problems across various domains including logistics, transportation, manufacturing, and e-commerce. Dr. Zhang received her PhD in Computer Science from the University of Manchester, UK. Prior to joining TU/e, she served as an Assistant Professor in the Econometrics Institute at Erasmus University Rotterdam and as a postdoc researcher in the Algorithmics group at TU Delft. She was also a visiting professor at the Institute for Advanced Computer Studies at University of Maryland, College Park, USA. Her research expertise lies at the intersection of Artificial Intelligence and optimization, with particular focus on machine learning, deep reinforcement learning, and trustworthy data-driven optimization. Dr. Zhang develops socially aware algorithms that can optimize decisions in data-rich environments. Her work bridges the gap between theoretical AI advancements and practical applications in industrial settings, addressing real-world challenges through innovative algorithmic solutions. She is particularly interested in how AI can support human decision-making while maintaining transparency and trustworthiness. Dr. Zhang's recent publications reveal a strong trend toward applying graph neural networks and reinforcement learning to complex scheduling and optimization problems. Her work demonstrates increasing sophistication in handling stochastic elements in decision-making processes, with applications spanning healthcare diagnostics, logistics, transportation, and manufacturing. She has made significant contributions to the field of neural combinatorial optimization, particularly for job shop scheduling problems and vehicle routing. Dr. Zhang has received several prestigious awards recognizing her contributions to the field: Winner of the MLVRP2023 GECCO competition (2023) Best Paper Award from Omega-International Journal of Management Science (2017) Best Industrial Paper Award (2020) Best Student Paper Award (2019) Best Student Paper Award of ICAART 2022 (2022) As a dedicated mentor, Dr. Zhang supervises numerous PhD students including Mohsen Abbaspour Onari, Abdo Abouelrous, Luca Begnardi, Xia Jiang, Chengpeng Hu, Minshuo Li, Robbert Reijnen, Jesse van Remmerden, Bart von Meijenfeldt, Ya Song, and Igor Smit. Her research is supported by various grants, including the LEO (Learning and Explaining Optimization) project co-funded by Holland High Tech | TKI HSTM via the PPP allowance scheme for public-private partnerships. Dr. Zhang actively contributes to the academic community as the Chair of the Benelux Association for Artificial Intelligence (BNVKI) and as a member of the Technical Board for the European Big Data Value Association (BDVA). She serves as an associate editor for the "Annals of Mathematics and Artificial Intelligence" journal and participates in the technical Program Committee for major AI conferences such as IJCAI, AAAI, AAMAS, and ECAI. She is also on the executive committee of the Data Science meets Optimisation (DSO) working group of EURO to promote collaboration between AI and Operations Research communities.

• Artificial Intelligence
• Machine Learning
• Deep Reinforcement Learning
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Yaoxin Wu is an Assistant Professor at the Eindhoven University of Technology, affiliated with the Department of Industrial Engineering and Innovation Sciences. His research bridges deep learning and combinatorial optimization to solve complex problems in transportation, scheduling, and network design. Education : PhD in Computer Science from Nanyang Technological University (2023). Wu specializes in artificial intelligence and operations research , focusing on graph neural networks, stochastic programming, and multi-objective optimization. His work has significant applications in UAV routing and on-demand delivery systems. His 2025 publications highlight trends in neural combinatorial optimization for stochastic job shop scheduling, ride-hailing, and drone logistics. Key subfields include deep reinforcement learning, preference modeling, and topological graph learning. He has supervised 9 students, including PhD candidates Xia Jiang and Igor Smite, and Master’s students like Venkata Roshan Mannepu and Floor Halkes. Wu's research is funded by projects like LEO (Holland High Tech | TKI HSTM) and SURF Cooperative grants. His educational activities include teaching Fundamentals of Algorithmic Programming and AI-Driven Business Operations , emphasizing data-driven methods for manufacturing processes.

• Deep Learning
• Combinatorial Optimization
• Integer Programming
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Georgy Ishmaev is a Postdoctoral Researcher at TU Delft and a member of the Distributed Systems group within the Faculty EWI . He also contributes to the Delft Blockchain Lab , focusing on the ethical dimensions of decentralized systems. Education: PhD in Technology Ethics from TU Delft, supervised by Prof. Jeroen van den Hoven. His research explores the intersection of blockchain technology and ethical frameworks, particularly addressing decentralized organizations , identity management systems , and the ethics of private data . This work bridges technical challenges with moral philosophy to ensure responsible innovation. The five most recent publications (2020–2021) analyze ethical dilemmas in blockchain applications, IoT data markets, and pandemic response strategies. These align with his expertise in decentralized systems , privacy , and digital ethics .

• Ethics of Technology
• Blockchain Technology
• Decentralized Systems
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Mark Coeckelbergh is a Full Professor of Philosophy of Media and Technology at the University of Vienna. He is a leading scholar in AI ethics, robot ethics, and political philosophy, with affiliations at the Institute of Philosophy of the Czech Academy of Sciences and WASP-HS/Uppsala University. His work bridges technology ethics, democracy, and environmental philosophy. PhD from the University of Birmingham Former President of the Society for Philosophy and Technology (SPT) Member of the European Commission's AI High-Level Expert Group Research Interests include ethical implications of AI, robotic companions, epistemic agency in digital systems, and climate justice. He explores intersections between technology, politics, and human existence. Publications span Robot Ethics , Why AI Undermines Democracy , and Digital Technologies, Temporality , focusing on AI's societal impact, democratic erosion, and environmental ethics. Scientific Awards: Finalist, World Technology Award 2017 Projects include Horizon Europe's ERA Chair, WWTF Democracy grants, and EU COST Action on wearable robots. He leads initiatives like 4TU.Ethics & Technology and advises on AI policy.

• Ethics of AI and Robotics
• Philosophy of Technology
• Political Philosophy
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Anne de Hond is an Assistant Professor in the Data Science Program at UMC Utrecht’s Julius Center, specializing in the responsible development, validation, and implementation of AI algorithms in healthcare. Her work bridges machine learning and clinical decision-making, with applications in mortality prediction, depression risk assessment, and ICU workflow optimization. Her research focuses on enhancing the reliability and fairness of AI models in clinical settings. Key projects include predicting hospital admissions at emergency departments, assessing depression risk in oncology patients, and analyzing the practical implementation of AI software in collaboration with startups. She also explores explainability and validation frameworks for emerging technologies like large language models. Anne’s recent publications span Predictive modeling for hip fracture mortality Frailty assessment in COVID-19 Workflow integration of AI in ICU Comorbidity index limitations Class imbalance in ML models Shortness of breath triage systems She is affiliated with the Julius Center at UMC Utrecht and previously conducted PhD research at Leiden UMC’s CAIRELab, combining a master’s in econometrics with clinical AI applications.

• Artificial Intelligence in Healthcare
• Machine Learning
• Clinical Decision Support Systems
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Koen Vincken is an Associate Professor at the Image Sciences Institute (UMC Utrecht), a research unit of Utrecht University focused on advancing medical imaging technologies. He holds an MSc in Computer Science from Delft University of Technology (1989) and a PhD from Utrecht University (1995) for his thesis "Probabilistic Multiscale Image Segmentation by the Hyperstack". His career spans software development for clinical applications in radiology, neurology, and orthopedics, including the digital reconstruction of ancient skeletal remains like the Dutch Neolithic skull "Trijntje". Vincken's research integrates computer science with medical imaging, emphasizing: Algorithm development for image segmentation and analysis High-precision acquisition methods for brain cancer diagnostics AI-driven solutions for radiology and pathology workflows Quantitative imaging biomarkers for neurodegenerative and orthopedic disorders His recent publications (2020-2024) demonstrate cross-disciplinary work in surgical imaging, neuropathology, and medical education technology, reflecting a focus on translating computational innovations into clinical practice. No scientific awards, grants, or supervised students are mentioned in the source material. He leads projects within the Image Sciences Institute research group, which develops analytical methods for brain cancer, stroke, and image-guided interventions.

• Medical Image Analysis
• Medical Imaging
• Computer Science in Medicine
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Esther van Kleef is an Assistant Professor at the University Medical Centre Utrecht and a technical consultant for the World Health Organisation (WHO) on global surveillance of antimicrobial resistance (AMR). She is affiliated with the Oxford Nuffield Department of Medicine and focuses on interdisciplinary research to enhance public health responses to emerging infectious diseases through data science. Her research integrates mathematical modeling and surveillance data to analyze AMR transmission dynamics and evaluate infection prevention strategies . She works across high-income and low- to middle-income countries, with projects spanning Europe, Africa, and global health contexts. Recent publications highlight her expertise in AMR surveillance , outbreak analytics (including LLM applications), and behavioral interventions for antibiotic use reduction. She specializes in merging conventional and innovative data sources for policy-driven public health solutions.

• Antimicrobial Resistance (AMR)
• Emerging Infectious Diseases
• Infection Prevention and Control
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Anne Zander is an Assistant Professor affiliated with the Digital Society Institute, Mathematics of Operations Research, and TechMed Centre at Karlsruhe Institute of Technology (KIT). Her work bridges mathematical modeling with healthcare systems, focusing on operational efficiency and data-driven decision-making in medical environments. Research Interests: Zander specializes in applying operations research methodologies to healthcare challenges, including ambulatory care logistics, surgical process optimization, and pandemic response planning. Her work incorporates data mining, reinforcement learning, and stochastic programming to improve patient flow, resource allocation, and system resilience. Publications: Recent research spans operating room benchmarking (2024), bed allocation during pandemics (2023), vaccination process optimization (2022), and innovative teaching approaches for operations research (2018). Her work often involves collaborations with healthcare institutions and interdisciplinary teams. Additional Contributions: Zander co-created open datasets for surgical process durations (2022) and participated in media discussions about cross-border healthcare collaboration (2024) and hospital patient retention strategies (2024). Her research aligns with UN Sustainable Development Goals for health and well-being.

• Operations Research
• Healthcare Systems
• Artificial Intelligence
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Aleida Braaksma is a Lecturer at the University of Twente, affiliated with the TechMed Centre and Mathematics of Operations Research department. Her work bridges Artificial Intelligence with Health and Well-being , focusing on optimizing healthcare systems through Operations Research methodologies. Key Affiliations: Digital Society Institute, TechMed Centre, Mathematics of Operations Research department Research Themes: Reinforcement Learning, Data Mining, Process Mining, and Queueing Theory applications in healthcare logistics Her recent publications highlight advancements in medical diagnostic scheduling , bed allocation , and adaptive clinical trial designs . She has pioneered dynamic robust optimization frameworks for time-sensitive pharmaceutical workflows and developed sampling-based methods for Gittins index approximation in stochastic environments. Scientific contributions include: Optimization of rheumatology outpatient clinics via patient classification algorithms Response-adaptive procedures in clinical trials using constrained Markov decision processes Real-time forecasting systems for pandemic-related hospital capacity planning Computerized decision support for nurse-to-patient assignment

• Artificial Intelligence
• Health and Well-being
• Reinforcement Learning
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Faiza Allah Bukhsh is an Associate Professor specializing in Artificial Intelligence, Data Mining, Process Mining, Health Informatics, Cybersecurity, and Ethical AI. Her work bridges technical innovation with societal impact, particularly in healthcare systems analysis, telecommunications resilience, and ethical data governance. Digital Society Institute TechMed Centre Datamanagement & Biometrics Her research focuses on Explainable AI , Process Mining , and Privacy Assurance in healthcare systems, with recent work on AI music perception, sepsis treatment analysis, and privacy-utility trade-offs. Key article trends include: AI in music and creative domains Process Mining for healthcare insights Explainable Machine Learning workflows Privacy-preserving analytics Telcom infrastructure resilience

• Artificial Intelligence
• Data Mining
• Process Mining
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Fabian Akkerman is a researcher at the Digital Society Institute within the Industrial Engineering & Business Information Systems department at the University of Twente . His work bridges theoretical advancements in machine learning with practical applications in logistics, energy sustainability, and transportation systems. Primary Affiliation : University of Twente, Industrial Engineering & Business Information Systems Research Focus : Artificial Intelligence, Reinforcement Learning, Autonomous Systems, and Sustainable Logistics Fabian's research specializes in sequential decision-making problems, particularly in dynamic and stochastic environments. A key area of his contributions lies in applying reinforcement learning and optimization techniques to address challenges in: Vehicle routing with uncertain demand Inventory management and warehouse operations Time slot pricing for delivery services Smart transportation and freight logistics Stochastic modeling for supply chain resilience Industry 4.0 adoption in production systems His work demonstrates a strong emphasis on developing algorithmic frameworks (e.g., DynaPlex) that combine statistical modeling with real-world implementation. Recent publications highlight applications in autonomous vehicles, intelligent transport systems, and circular economy strategies. Scientific Awards : 2024 Transportation Science Meritorious Service Award 2025 2nd Place in ISIR Research Challenge for Production-Inventory Planning at ASML

• Artificial Intelligence
• Machine Learning
• Autonomous Agent Systems
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Dr. Margarita Leib is an Assistant Professor in the Department of Social Psychology at Tilburg University's School of Social and Behavioral Sciences. Her research sits at the intersection of psychology and economics, focusing on decision-making processes in ethical dilemmas and human-AI interactions. Research interests include: Behavioral ethics and dishonesty in social contexts Impacts of AI algorithms on moral decision-making Willful ignorance and information avoidance strategies Prosocial/antisocial behavior in organizational settings Cross-cultural differences in ethical behavior Recent publications (2023-2025) cluster around three themes: AI's influence on ethical decisions (computational ethics), psychological mechanisms of dishonesty (behavioral ethics), and meta-analyses of information avoidance strategies (cognitive psychology). Her work appears in high-impact journals including Computers in Human Behavior and Psychological Bulletin. She is currently involved in the multinational research project 'The obligation to obey the law: exploring National Differences' and leads a Digital Sciences for Society-funded project examining interventions in unethical AI-driven decisions.

• Behavioral Ethics
• Decision Making
• Human-AI Interactions
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Marloes Hagens is a PhD candidate at the Rotterdam School of Management (RSM) , affiliated with the Department of Finance at Erasmus University Rotterdam. Her research explores intersections of finance, behavioral economics, and algorithmic ethics. Research Interests: Behavioral Economics: Focused on how AI-generated advice impacts human honesty and ethical decision-making. Algorithmic Ethics: Investigating the role of artificial intelligence in shaping financial behavior. Human-AI Interaction: Analyzing the influence of algorithmic transparency on trust and compliance. Publications: Her recent work examines the ethical implications of AI in economic contexts, particularly dishonesty dynamics.

• Behavioral Economics
• Finance
• Algorithmic Ethics
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Serdar Yavuzyigitoglu is a Researcher in the Department of Ophthalmology at Erasmus University Medical Center (Erasmus MC), Rotterdam, specializing in uveal melanoma research. His work spans molecular diagnostics, treatment outcomes, and epidemiological studies of this rare ocular malignancy. His primary research focuses on genetic drivers of uveal melanoma (particularly BAP1 and SF3B1 mutations), metastatic progression patterns, and comparative effectiveness of radiation therapies. He investigates how chromosomal aberrations like 8q gain influence prognosis, examines global incidence patterns correlated with pigmentation factors, and analyzes 30-year treatment trends in Dutch patient cohorts. His research bridges molecular biology with clinical outcomes to improve risk stratification and therapeutic decision-making. Analysis of his 15 most recent publications reveals consistent emphasis on metastatic uveal melanoma biology, with growing integration of genomic data into clinical frameworks. His work demonstrates strong epidemiological rigor in population studies while advancing molecular understanding of treatment resistance mechanisms, particularly in liver metastasis contexts. Yavuzyigitoglu actively collaborates with the Rotterdam Ocular Melanoma Study Group (ROMS), contributing to multi-center investigations that have generated significant clinical insights published in high-impact journals including Scientific Reports, Ophthalmology Science, and Cancers.

• Ocular Oncology
• Uveal Melanoma
• Radiation Therapy
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