جست‌وجوی استاد و پژوهشگر

J.L. Rebelo Moreiraمشاهده پروفایل

استادیار

• Interoperability
• Ontology Engineering
• Distributed Systems
• +۶ مورد دیگر

J.L. Rebelo Moreira serves as an Assistant Professor at the Semantics, Cybersecurity and Services (SCS) research group within the Department of Computer Science, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente. He joined the institution in 2014 as a PhD researcher and transitioned to his current faculty position in 2020 after completing his doctoral studies. His academic foundation includes a Bachelor of Science in Computer Science and a Master of Science in Informatics, both earned at the Federal University of Rio de Janeiro, Brazil. Prior to his academic career, he accumulated 13 years of industry experience (2001-2014) as a software developer and enterprise architect. Dr. Rebelo Moreira's research centers on solving interoperability challenges in information systems through dual technical and semantic approaches. His work emphasizes distributed systems architecture for technical interoperability and ontology engineering for semantic interoperability. Current projects investigate digital twins and explainable artificial intelligence (XAI) using ontological frameworks and model-based system engineering. He has significantly contributed to implementing FAIR (Findable, Accessible, Interoperable, Reusable) data principles, particularly for reproducibility in machine learning workflows and scientific pipelines. With extensive teaching experience since 2008 across bachelor's, master's, Engineering Doctorate, and PhD levels, he currently leads the master's course on Architecture of Information Systems while contributing to Smart Industry Systems, Service-Oriented Architecture, Model-Driven Engineering, and Enterprise Architecture courses. He actively participates in academic service through conference organization (FOIS, EDOC, RCIS, ISCRAM) and peer review activities. He maintains strong engagement with standardization bodies including the European Telecommunication Standardization Institute (ETSI) for SAREF IoT ontology development, GO-FAIR initiative, and Research Data Alliance (RDA), bridging academic research with industrial applications through collaborative projects.

• Machine Learning
• Health Informatics
• Data Mining
• +۵ مورد دیگر

Dr. Rittika Shamsuddin is an Assistant Professor in the Department of Computer Science at Oklahoma State University. She holds a PhD in Computer Science from The University of Texas at Dallas and a BA in Computer Science and Biology from Mount Holyoke College. Her research focuses on improving interdisciplinary communication between computational fields and healthcare/biology through machine learning and algorithm development. Key areas include healthcare data analysis platforms, explainable AI, and addressing challenges like data scarcity and privacy in medical applications. Education: PhD, Computer Science & Data Science, University of Texas at Dallas (2013–2016) BA, Computer Science & Biology, Mount Holyoke College (2008–2012) Research Interests: Dr. Shamsuddin develops tools and frameworks to bridge computational and medical domains. Her work emphasizes healthcare-specific machine learning platforms, interpretable AI systems, and solutions for data scarcity. She also explores applications in rural healthcare, disease progression modeling, and ethical AI integration in medical decision-making. Recent projects include System-of-Systems ML frameworks for XAI and synthetic data generation for rare medical conditions. Grants & Funding: RET Site: SecurAIty Nexus (2024–2027) RET Site: Big Data & Machine Learning for Educators (2021–2024) Labs & Teams: She leads a research group focused on healthcare AI and collaborates with medical institutions. Her lab website hosts ongoing projects and tools like CEFEs for ECG interpretability and synthetic data generation frameworks.

Theodoros Evgeniouمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Business Analytics
• +۵ مورد دیگر

Role & Affiliation: Theodoros Evgeniou is a Professor of Decision Sciences and Technology Management at INSEAD since 2001, and Director of the INSEAD Executive Education programme 'Transforming your Business with AI'. He holds a dual academic role in Technology and Business, focusing on Machine Learning, AI ethics, and business process optimization. His work bridges academic research and industry application, with affiliations including OECD Network of Experts on AI and the BCG Henderson Institute. Education: Four degrees from MIT: BSc in Computer Science BSc in Mathematics Master of Computer Science PhD in Computer Science Research Interests: Evgeniou’s research spans 30+ years in AI innovation, including business process optimization, AI regulation, and novel machine learning methods. His work appears in top journals like Science , Nature Machine Intelligence , and Lancet Digital Health . Key areas include AI ethics, healthcare applications, and strategic decision-making with data analytics. Articles Trends: Recent publications focus on XAI narratives, UGC strategies in digital content, AI robustness in healthcare, and pandemic impacts on academic resilience. These reflect interdisciplinary approaches to AI’s societal and business implications. Grants & Advising: Served as OECD AI expert, BCG advisor, and co-founder of Tremau (B2B SaaS for ethical AI). Consults widely on ML applications and regulatory frameworks. Labs & Teams: Co-founded Tremau to promote safe digital ecosystems, and collaborates with INSEAD’s AI initiatives. Active in academic-industry partnerships through case studies and executive education programs.

• Artificial Intelligence
• Machine Learning
• Structural Engineering
• +۷ مورد دیگر

Niusha Shafiabady is an Associate Professor at the Peter Faber Business School, part of the Faculty of Law and Business at Australian Catholic University. Her research focuses on interdisciplinary applications of Artificial Intelligence (AI), including Explainable AI (XAI), machine learning, structural health monitoring, medical diagnosis, and educational technology. She has published extensively in journals like Journal of Building Engineering , IEEE Transactions on Artificial Intelligence , and PLoS ONE . Her work spans diverse domains: optimizing smart buildings for energy efficiency, diagnosing diseases via deep learning, and predicting student engagement in higher education. She also explores AI’s societal impacts, including its role in organizational agility and ethical considerations in technology adoption. Recent research highlights include developing novel algorithms for structural monitoring of bridges, hybrid models for respiratory disease diagnosis, and comparative studies of machine learning in gas safety systems. Collaborations span institutions globally, reflecting her commitment to collaborative, applied research.

• Conceptual Modelling
• Enterprise Modeling
• Requirements Engineering
• +۳ مورد دیگر

Monique Snoeck is a full professor and coordinator of the Information Systems Engineering Research Group (LIRIS) at KU Leuven's Faculty of Economics and Business (FEB). She serves on the FEB Council and Campus Council while contributing to DigiSoc and Leuven.AI institutes as a member focused on digital society and AI initiatives. Her research pioneers conceptual modelling, enterprise modeling, requirements engineering, and model-driven engineering, notably through the MERODE methodology and MERLIN prototyping tool. Current work integrates AI with learning analytics for education and develops automatic UI generation from conceptual models, alongside capacity-building projects for Cuba's software industry using model-driven approaches. Recent publications reveal converging trends in LLM-enhanced conceptual modeling education, XAI applications for credit risk analysis, and digital process twins for business scenario simulation. Key themes include model validation techniques, IoT application patterning, and multi-model integration for domain-process alignment. She supervises PhD candidates Tiukhova, E. (credit risk/XAI) and Verbruggen, C. (multi-modelling), leading grants including 'Explanations in AI for Learning Analytics' (2025-2029), 'Personalized Feedback Generation' (2024-2028), and 'Cuba Software Industry Capacity Building' (2024-2029) as promotor. As LIRIS leader, she drives model-driven engineering research while collaborating with Leuven.AI on AI integration and DigiSoc on digital public service co-creation initiatives.

Lissa Melisمشاهده پروفایل

استادیار

• Operations Research
• Urban Transport Logistics
• Mobility-as-a-Service
• +۳ مورد دیگر

Lissa Melis is an Assistant Professor in Operations Research and Operations Management at the Department of QE Operations Research within the School of Business and Economics at Maastricht University. Her research focuses on heuristic algorithms for urban transport systems and the intersection of operations research with explainable AI. She holds a PhD from the University of Antwerp (Belgium), supported by an FWO Fellowship, and completed postdoctoral research at Penn State University in the USA with a President’s Postdoctoral Fellowship. Education: PhD in Applied Economic Sciences (Operations Research) from University of Antwerp FWO Fellowship recipient during PhD Postdoctoral Researcher at Penn State University (Civil and Environmental Engineering) BAEF (Belgian American Educational Foundation) Alumni Research Interests: Design and analysis of (meta)heuristics for transport problems Urban mobility systems and MaaS (Mobility-as-a-Service) Explainable AI (XAI) in decision-making systems Sustainable public transport integration Recent Work Trends: Recent publications explore transparency in AI explanations and optimizing transport networks. Earlier work addresses migration’s societal impacts, suggesting interdisciplinary engagement with sociological and environmental factors. Awards: President’s Postdoctoral Fellowship Program (USA) FWO Fellowship (Belgium) Teaching Roles: Course coordinator for Advanced Operations Research (Master’s) Operations Management for Business Analytics (Bachelor’s) Operations and Supply Chain Management (Business Engineering) Labs/Teams: Involved in Quantitative Economics research groups at Maastricht University focusing on urban systems optimization and AI ethics.

Yiannis Papadopoulosمشاهده پروفایل

استاد

• Safety of AI
• Dependable Artificial Intelligence
• Safety of Machine Learning
• +۶ مورد دیگر

Professor Yiannis Papadopoulos is a leading academic in safety-critical systems and Dependable AI at the University of Hull's School of Computer Science, Faculty of Science and Engineering. His roles include teaching modules on Software Engineering, Digital Disruption, and Safety-Critical Systems. He holds a DPhil from the University of York and has over 25 years of research experience. Education: PhD/DPhil in Computer Science, University of York (2000) MSc in Advanced Manufacturing Technology, Cranfield University (1993) BSc/MSc in Electrical and Computer Engineering, Aristotle University of Thessaloniki (1991) Research Interests: Dependable AI, Safety of Machine Learning, and Autonomous Systems Model-Based Safety Engineering (HiP-HOPS method) Applications in Automotive, Aerospace, and Offshore Energy Generative Art and Philosophy-inspired Research Environments Publications: Recent work focuses on LLM safety, fairness in AI, and offshore wind maintenance. Key contributions include the Andromeda framework for safety assurance and the SafeML method for statistical safety monitoring. His research bridges bio-inspired AI with formal safety analysis, impacting industries globally. Grants & Projects: SESAME (EU H2020): Safety of Multi-Robot Systems (£482k) DEIS H2020: Dependability Engineering (£461k) EPSRC Edge AI Hub: Safety of Edge-AI (£214k) Academic Garden: His DEIS research group fosters a collaborative environment emphasizing ethical research and student well-being. Notable initiatives include Stoic philosophy seminars and interdisciplinary projects like the Timaeus digital art studio.

Köszegi Sabine Theresiaمشاهده پروفایل

استاد

• Human-Robot Interaction
• Artificial Intelligence Ethics
• Organizational Behavior
• +۳ مورد دیگر

Prof. Dr. Köszegi Sabine Theresia is a full professor at TU Wien's Institute of Management Sciences, affiliated with the Research Department of Work Science and Organization. She leads projects on robotic care (Caring Robots) and negotiation dynamics (e-Nego-motion). Her research focuses on human-robot interaction, organizational behavior in digital transformation, and ethical implications of artificial intelligence. Key areas include trust in autonomous systems, platform-mediated labor, and workplace flexibility. Education details not explicitly stated but inferred through academic role. Research interests prominently feature sociotechnical systems, explainable AI, and gender perspectives in technology. She has co-authored over 160 publications since 2000, with recent work emphasizing care robots' societal impact, remote work dynamics, and AI ethics. No awards listed but maintains active research groups like the Network Lab. Advising role details unavailable. Publications span interdisciplinary topics including healthcare robotics, digital labor platforms, and public sector telework policies.

Gilles Audemardمشاهده پروفایل

استاد

• Satisfiability (SAT)
• Constraint Satisfaction Problems
• eXplainable AI (XAI)
• +۳ مورد دیگر

Gilles Audemard is a Professor at Artois University in France, specializing in satisfiability (SAT), constraint satisfaction problems, and eXplainable AI (XAI). His research focuses on developing efficient solvers such as Glucose and CoSoCo , and tools like PyXAI for interpretable machine learning. He leads the XCSP3 format for combinatorial problem representation and the SAT Heritage project to archive SAT solvers. Key achievements include the 2023 Skolem Award for influential work on hybrid SAT solving, a 2021 CAV Award for foundational contributions to SMT, and multiple best paper awards. His solvers have won medals in SAT and XCSP competitions. Audemard collaborates on projects like the French ANR-funded EXPECTATION AI Chair and contributes to open-source tools like pFactory and PyCSP3 . Research spans formal methods, constraint programming, and AI explainability, with a focus on practical applications in combinatorial optimization and machine learning interpretability. His work bridges theoretical advancements with scalable solver implementations, impacting both academic and industrial problem-solving.

• Translation Studies
• Translation Technology
• Natural Language Processing
• +۴ مورد دیگر

Ayla Rigouts Terryn serves as Assistant Professor of Translation Studies and Translation Technology at KU Leuven Kulak within the Centre for Translation Studies. She currently heads the Translation and Technology research group, driving interdisciplinary work at the intersection of linguistics and computational methods. Her academic foundation includes a PhD from Ghent University (2021) focused on machine learning approaches to automatic terminology extraction. This doctoral work established her expertise in computational linguistics and natural language processing. Dr. Rigouts Terryn's research spans critical areas in language technology: Natural Language Processing (NLP) and Computational Linguistics Monolingual and Multilingual Automatic Term Extraction Explainable AI (XAI) for transparent language models Bilingual Lexicon Induction as a probing task for cross-lingual understanding Her work bridges theoretical linguistics with practical applications in translation technology, emphasizing machine learning techniques for terminology management. While no specific publications are detailed in the source text, her research trajectory indicates strong focus on enhancing AI interpretability in multilingual contexts. No scientific awards or honors are documented in the provided materials. Similarly, information regarding graduate student supervision or research grant acquisition remains unspecified in the available documentation. As leader of the Translation and Technology research group, she directs strategic initiatives advancing computational approaches to translation, fostering collaboration between linguists, computer scientists, and translation professionals to develop next-generation language tools.

• Hydrology
• Climate Change Impacts
• Water Resources Management
• +۳ مورد دیگر

Zhenxing Zhang is an Associate Research Scientist at the Illinois State Water Survey and a Research Affiliate in the Civil and Environmental Engineering Department at the University of Illinois Urbana-Champaign. Their work focuses on hydrology, water resources sustainability, and climate change impacts, with a particular emphasis on drought-flood transitions, optimization algorithms for environmental modeling, and the water-energy-carbon nexus. Primary Affiliation: Illinois State Water Survey (Research Scientist) Secondary Affiliation: Civil and Environmental Engineering Department Research Interests span interdisciplinary domains including: Hydrology : Streamflow dynamics, flood risk analysis, low streamflow statistics, and hydrological modeling frameworks. Climate-Environment Interactions : Climate change impacts on water resources, drought-heatwave synergies, and flash drought mechanisms. Computational Methods : Development and application of novel bio-inspired optimization algorithms (e.g., Electric Eel Foraging Optimization, Tianji's Horse Racing Optimization) to solve engineering problems. Publication Trends reveal a focus on integrating machine learning with traditional hydrological models (e.g., deep learning for rainfall prediction), quantifying water quality risks during compound climate events, and creating new optimization techniques with engineering applications. Their work frequently combines multi-criteria analysis with stakeholder engagement for water supply planning in Illinois and China. Key Collaborations involve institutions in Illinois and China, with frequent co-authorship on topics like: Water resource carrying capacity Drought-flood abrupt alternations Membrane bioreactor (MBR) applications Carbon capture water supply risks Zhang contributes to comprehensive hydrologic modeling frameworks that assess long-term water sustainability under changing climatic and demand conditions, particularly in the Illinois River Basin and Yangtze River region .

Jonn Terje Geitungمشاهده پروفایل

استاد

• Medical Imaging
• Radiology
• Artificial Intelligence in Healthcare
• +۳ مورد دیگر

Jonn Terje Geitung is a Professor at the University of Oslo, affiliated with the Division of Medicine and Laboratory Sciences within the Faculty of Medicine. His research focuses on advanced medical imaging techniques, including MRI, CT, and their integration with artificial intelligence (AI). Key areas of interest include neuroimaging, oncologic imaging, and diagnostic accuracy in interventional procedures. He has contributed to studies on brain tumor classification via deep learning, AI-driven metabolite mapping, and low-dose contrast medium strategies in CT pulmonary angiography. Geitung’s work emphasizes clinical trials and translational research, such as optimizing imaging protocols for cardiovascular and musculoskeletal systems. His publications address challenges in diagnostic reproducibility, such as plaque thickness measurement in arteries and spinal fracture detection in multiple myeloma patients. Collaborations span interdisciplinary teams, including radiologists, oncologists, and AI specialists. He has also explored the application of imaging in rare diseases, like breast sarcoidosis, and evaluated safety aspects of imaging modalities in national healthcare contexts.

Joris Hulstijnمشاهده پروفایل

استادیار

• Responsible AI
• Artificial Intelligence and Law
• Cybersecurity
• +۵ مورد دیگر

Dr. Joris Hulstijn is an Assistant Professor at Utrecht University's Faculty of Science, Department of Software Production. His research focuses on Responsible AI, AI and Law, cybersecurity, and regulatory supervision. He teaches in the Information Sciences (BSc) and Business Informatics (MSc) programs, coordinating master's theses in Business Informatics. His work addresses accountability in AI systems, ethical implications of government IT, and IT audit automation. He leads the Software Ecosystem Security lab with colleagues Kate Labunets and Slinger Jansen. Key research themes include Governing the Digital Society and Human-Centered AI. Recent publications (2023-2024) explore computational accountability, explainable AI for illiterate users, automated phishing detection, and ethical decision frameworks in AI. His work bridges technical systems with legal and societal governance, emphasizing transparency and compliance in digital ecosystems.

Christian Bauckhageمشاهده پروفایل

استاد

• Machine Learning
• Pattern Recognition
• Quantum Computing
• +۲ مورد دیگر

Christian Bauckhage is Professor of Computer Science specializing in Pattern Recognition at the University of Bonn and Lead Scientist for Machine Learning at Fraunhofer IAIS. He serves as co-director of The Lamarr Institute for Machine Learning and Artificial Intelligence. With over 20 years of experience in AI, his current research focuses on quantum computing applications in machine learning. His research spans quantum machine learning, explainable AI, financial document analysis, and medical imaging. Recent publications demonstrate strong focus on quantum computing applications, large language models in finance, and explainability techniques. Bauckhage has published over 200 papers on pattern recognition, machine learning, and quantum computing applications. His research group at Fraunhofer IAIS focuses on industrial applications of AI in finance, healthcare, and agriculture.

Yi-Zhe Songمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Professor Yi-Zhe Song is a Professor of Computer Vision and Machine Learning at the University of Surrey's Centre for Vision, Speech and Signal Processing (CVSSP), one of Europe's largest AI research centers. He leads the SketchX Lab, focusing on understanding human sketching to advance computer vision and cognitive science. His roles include Co-Director of the Surrey Institute for People-Centred AI and Programme Lead of Surrey’s MSc in Artificial Intelligence. He holds a PhD (2008) and MSc (2004, Best Dissertation Award) from the University of Cambridge and Bath. Research interests span sketch-based systems, fine-grained visual classification, domain adaptation, and generative models. His lab has produced over 67 publications in top-tier conferences (CVPR, ICCV, ECCV) and journals, including a Best Paper Award at BMVC 2015. He is an Associate Editor for IEEE TPAMI and Frontiers in Computer Science, and has served as Programme Chair for BMVC 2021. Recent trends in his work emphasize explainable AI (SketchXAI), 3D reconstruction from sketches, and practical applications like virtual try-on and fashion-focused V+L models. His MSc AI programme integrates technical, ethical, and business aspects, preparing students for diverse AI roles. Awards: Best Paper Award (BMVC 2015), Senior Member of IEEE, Fellow of the Higher Education Academy. Lab Leadership: 3 academics, 2 postdocs, 14 PhD students in the SketchX Lab. Grants & Partnerships: Collaborations with industry and international funding bodies (e.g., EPSRC, São Paulo Research Foundation). Future Work: Expanding AI’s societal impact through inclusive education, human-centric AI, and democratizing generative AI tools.