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Giovanni SilenoView profile

Lecturer

• Artificial Intelligence
• Logic Programming
• Knowledge Representation
• +5 more

Giovanni Sileno is an academic specializing in Artificial Intelligence, Logic Programming, and Knowledge Representation, with teaching appointments at the University of Amsterdam, EPITA Paris, and Université Pierre et Marie Curie. His primary affiliation is with the Informatics Institute at the University of Amsterdam where he serves as a Lecturer. His research interests span multiple domains within computer science, focusing particularly on formal methods for normative systems, agent-based programming, and logic-based knowledge representation. His work bridges theoretical computer science with practical applications in policy modeling, forensic science, and business information systems. Dr. Sileno has developed several notable software projects including AgentScriptCC for single-threaded intentional agents, DCPLschema for normative policy specifications, and libraries for normative primitives in Answer Set Programming. His GitHub activity shows consistent contributions from 2012 through 2025, indicating active engagement in both research and development. His publications reflect a strong focus on formal methods applied to practical problems, with particular emphasis on normative systems, agent architectures, and logic programming applications. The research trajectory shows evolution from foundational topics in logic and numeral systems toward increasingly sophisticated applications in policy modeling and agent-based systems. As an educator, he has taught across multiple institutions and disciplines, covering topics from basic programming paradigms to advanced knowledge representation and formal modeling techniques. His teaching spans undergraduate to master's level courses in information studies, cognitive science, data science, and forensic science.

• Statistical Cybermetrics
• Operational Research
• Mathematical Modeling
• +5 more

Pardeep Sud is a Senior Lecturer in Mathematics and Statistics at the University of Wolverhampton's School of Mathematics and Computer Science , part of the Faculty of Science and Engineering. He holds a BSc (Hons) in Physics from Imperial College London and a Postgraduate Certificate in Learning and Teaching in Higher Education from the University of Wolverhampton. His research focuses on statistical cybermetrics, citation analysis, and bibliometric methodologies, contributing to journals like Quantitative Science Studies and Journal of Informetrics . Prior to academia, he worked in actuarial science (pension modeling) and natural gas industry R&D (fluid flow modeling). He teaches undergraduate/postgraduate modules including Operational Research, Calculus, and Financial Mathematics. Professional activities include participation in the 2020 Wolverhampton Impact Day, focusing on research impact and policy engagement. His work explores interdisciplinary topics such as gender disparities in academia, international collaboration impacts, and altmetrics evaluation. A key member of the Statistical Cybermetrics Research Group, his contributions bridge quantitative research methods with real-world applications in policy and industry. Teaching responsibilities span core mathematical techniques, advanced calculus, and financial modeling. Research output emphasizes data-driven analysis of scholarly communication, citation patterns, and digital scholarship trends. Over 18 peer-reviewed publications since 2011 reflect his expertise in bibliometric analysis, webometrics, and collaborative research dynamics.

• Natural Language Processing
• Information Extraction
• Social Media Analysis
• +4 more

Dr. Mark Greenwood is a Research Associate at the School of Computer Science, University of Sheffield. His work focuses on Natural Language Processing (NLP), Information Extraction, and Semantic Technologies. He has contributed to projects like the GATE framework, developing tools for text processing and social media analysis. His research addresses real-time semantic annotation, online abuse detection in political discourse, and healthcare data mining. Notable collaborations include work with the National Archives and medical institutions to improve text analysis in clinical records. Greenwood has authored over 30 peer-reviewed publications, with recent focuses on social media analytics and genomic data integration. His expertise spans dependency parsing, temporal expression extraction, and cross-media knowledge systems. Education details are not explicitly stated, but his academic trajectory includes significant contributions to computational linguistics and NLP since the early 2000s. Research interests are anchored in practical NLP applications such as question-answering systems, genic interaction extraction, and media influence quantification. He has led teams on EU-funded projects and advised on large-scale semantic search initiatives like Khresmoi. Current work emphasizes partisanship analysis in digital spaces and automated text pattern recognition.

Michael GruningerView profile

Professor

• Ontologies
• Semantic Integration
• Process Modelling
• +6 more

Michael Gruninger is a Professor in the Department of Mechanical and Industrial Engineering at the University of Toronto, serving as Associate Chair of Undergraduate Studies. He holds a PhD and MSc in Computer Science from the University of Toronto and a BSc in Computer Science from the University of Alberta. His research focuses on semantic integration, process modeling, and mathematical logic applications in manufacturing and enterprise engineering. He contributed to the ISO 18629 standard for Process Specification Language. Research interests include ontologies, semantic web technologies, knowledge representation, and formal methods. He leads the Semantic Technologies Laboratory, advancing theories in mereotopology, spatiotemporal ontologies, and ontology engineering. Recent work emphasizes automated spatial reasoning in robotics and standards-based ontology development. Publications span ontology validation, mereological foundations, and applied semantic technologies. His work bridges theoretical computer science with practical enterprise systems and smart city applications. No awards are explicitly listed, though his contributions to ISO standards reflect industry impact. Advising and grants: No specific students/grants detailed here. His lab focuses on semantic technologies with applications in manufacturing and urban systems. Collaborations include NIST and the Industrial Ontologies Foundry.

Xuanjun GongView profile

Assistant Professor

• Media Selection
• Computational Modeling
• Communication Networks
• +4 more

Xuanjun (Jason) Gong is an Assistant Professor in the Department of Communication and Journalism at Texas A&M University. His research program bridges computational modeling, media psychology, and neuroscience to investigate sequential media selection, information diffusion, and neural correlates of media engagement. He employs diverse methodologies including fMRI, behavioral experiments, and large-scale social media analysis. Gong's research focuses on the computational mechanisms of media choice, exploring how curiosity, mood, and cognitive processes shape decisions in dynamic media environments. His work formalizes theories like mood management and flow states using drift-diffusion models and network neuroscience approaches. Key themes include prediction of media behaviors, cross-platform information spread, and neurocognitive foundations of audience engagement. His publications demonstrate consistent emphasis on developing integrative frameworks that combine communication theory with computational rigor. Recent work advances understanding of time-dependent media selection, neural dynamics during flow experiences, and social media discourse patterns during exogenous events. Gong's research has been recognized with a Graduate Student Award (CNS21). Awards & Honors: Graduate Student Award, CNS21 (2021)

Dean RehbergerView profile

Associate Professor

• Digital History
• US History
• Public History
• +6 more

Dean Rehberger is an Associate Professor in the Department of History at Michigan State University (MSU) and Director of MATRIX, MSU's digital humanities center. His research focuses on developing digital technologies for research and teaching, including semantic web, big data, digital libraries, and geospatial knowledge graphs. He leads interdisciplinary projects such as the KnowWhereGraph and the Enslaved.org hub knowledge graph, emphasizing ethical sustainability and disaster risk management frameworks. Education details are not explicitly stated in the provided text. Research interests include digital history, public history, and the application of computational methods to cultural heritage and emergency management. His work bridges geospatial analysis, ontology engineering, and humanities applications through projects like HIP Ontology and the S2 Discrete Global Grid System. Recent articles highlight advancements in knowledge graph architectures, geospatial data integration, and ethical frameworks for digital projects. Notable projects include the $1.5M Mellon Grant for the Enslaved.org database and collaboration with GLAM institutions (galleries, libraries, archives, museums). Grants: Mellon Foundation Grant (2023) for slave trade database Labs/Teams: MATRIX (digital humanities center at MSU)

Dieter De WitteView profile

Professor

• Artificial Intelligence
• Data Science
• FAIR Data
• +3 more

Prof. Dieter De Witte serves as a Professor at Ghent University, dedicating 50% of his time to the Internet Technology and Data Science Lab (IDLab) while simultaneously contributing 50% to the Royal Museums of Fine Arts Belgium (RMFAB) in Brussels through a FED-tWIN mandate from Belspo. At RMFAB, he spearheads the strategic overhaul of digital infrastructure toward FAIR-compliant and data-driven systems, while at Ghent University he collaborates with Prof. Steven Verstockt on applied AI projects across heritage, mental healthcare, and education domains. His academic foundation includes a Master's in Engineering Physics from Ghent University (2008) followed by doctoral research on Big Data technologies and FAIR data for life sciences. Prior to returning to academia in 2021, he gained industry experience as an AI consultant and team lead at Telenet and Ordina. De Witte's research centers on AI-driven transformation of cultural heritage through FAIR data publication , collection enrichment , and intuitive querying interfaces . His technical expertise spans multimodal algorithms, image segmentation, pose estimation, large language models (LLMs), and semantic technologies including SPARQL and IIIF. Current projects focus on human-in-the-loop AI systems that combine diverse AI building blocks for practical heritage applications. Analysis of his 15 most recent publications reveals a clear trajectory from early bioinformatics work (2007-2018) on genomic motif discovery and life sciences data infrastructure toward contemporary cultural heritage applications (2023-2024). Recent outputs demonstrate innovative fusion of pose estimation, linked data frameworks, and multimodal AI for museum contexts, highlighting increasing specialization in AI enrichment of digital collections while maintaining core expertise in FAIR data principles. His FED-tWIN grant enables critical knowledge transfer between academic research and cultural heritage institutions, supporting development of next-generation digital infrastructure at RMFAB. Current projects involve creating AI tools for intuitive collection exploration and systematic enrichment of heritage assets through advanced computational methods. De Witte operates within Ghent University's Internet Technology and Data Science Lab (IDLab), participating in interdisciplinary teams developing applied AI solutions. His work bridges technical innovation with practical implementation in cultural institutions, focusing on sustainable, interoperable systems that enhance public access to digital heritage collections through cutting-edge AI interaction paradigms.

Annette ten TeijeView profile

Professor

• Artificial Intelligence
• Knowledge Representation
• Medical Knowledge Representation
• +7 more

Annette ten Teije is a Full Professor at Vrije Universiteit Amsterdam (VU Amsterdam) with appointments in the Faculty of Science, Artificial Intelligence department, the Network Institute, and the Knowledge Representation and Reasoning research group. Her academic career spans several decades with a strong focus on the intersection of artificial intelligence and healthcare applications. Professor ten Teije's research interests center around Knowledge Representation, particularly in medical contexts. Her work bridges multiple domains including Semantic Web technologies, Ontology development, Neuro-Symbolic AI systems, and Clinical Decision Support. She has made significant contributions to the formalization of clinical guidelines, handling multimorbidity in healthcare systems, and developing design patterns for hybrid AI systems. Her research integrates machine learning with symbolic reasoning to create explainable and reliable AI systems for healthcare applications. Analysis of Professor ten Teije's recent publications reveals a strong trajectory toward neuro-symbolic AI approaches that combine the strengths of neural networks and symbolic reasoning. Her work increasingly focuses on explainability in medical AI systems, with numerous publications on feature selection, interaction detection, and narrative-based understanding. She has developed frameworks for shared understanding in multi-agent systems and created design patterns specifically for medical decision-making contexts. Her research consistently bridges theoretical AI advances with practical healthcare applications. Professor ten Teije has supervised 5 PhD theses as indicated in her academic profile and teaches courses including "AI in Health" and "Machine Learning and Reasoning for Health" for the 2024-2025 academic year. Her academic contributions extend to editorial work, including serving as editor for conference proceedings and special issues on Knowledge Representation for Healthcare Processes.

Patrick LambrixView profile

Professor

• Semantic Web
• Databases
• Knowledge Engineering
• +3 more

Patrick Lambrix is a Professor and Head of the Division for Database and Information Techniques at Linköping University's Department of Computer and Information Science (IDA). He holds a MSc in Mathematics and Computer Science from KU Leuven and a PhD from Linköping University. His expertise spans semantic web technologies, ontology engineering, and sports analytics. He leads the Database and Web Information Systems group and co-founded the Sports Analytics research group at IDA. Research interests include knowledge engineering applications in materials science and life sciences, ontology alignment, and performance metrics in sports. He coordinates the Master in Computer Science program and serves as Director of Studies. Notable contributions include award-winning systems in ontology debugging and completion, and organizing the Linköping Hockey Analytics Conference (LINHAC). Education: MSc (Mathematics & Computer Science, KU Leuven, 1988/1990), PhD (Linköping University, 1996) Leadership Roles: Head of ADIT Division, Director of Studies, WASP program participant Research Groups: Database & Web Info Systems, Semantic Web, Sports Analytics Publications focus on ontology engineering advancements and sports analytics innovations, with recent work on hockey performance metrics and ontology alignment methodologies. His teams collaborate with industry and sports organizations to apply semantic technologies in real-world scenarios.

Marina De VosView profile

Senior Lecturer

• Answer Set Programming
• Normative Multi-Agent Systems
• Knowledge Representation
• +3 more

Dr. Marina De Vos is a Senior Lecturer in the Department of Computer Science at the University of Bath. She leads research in knowledge representation and normative systems, with affiliations to multiple research centers including the Centre for Mathematical Biology and UKRI CDT in Accountable AI. Education: Postgraduate Certificate in Learning and Teaching in Higher Education, University of Bath (2010) Doctor of Science in Computer Science, Vrije Universiteit Brussel (2002) Master of Computing, Vrije Universiteit Brussel (1998) Research focuses on declarative programming paradigms, particularly Answer Set Programming (ASP) and normative multi-agent systems. Her work enables intuitive problem description in knowledge representation languages, with applications spanning structural engineering, music composition, legal reasoning, and policy modeling. Additional research interests include inductive machine learning, explainable AI, hybrid AI systems, and game theory. Recent publications demonstrate strong focus on norm synthesis in multi-agent systems, contextual reasoning, and governance frameworks for autonomous systems. This reflects a consistent pattern of applying formal computational methods to social and ethical dimensions of AI. Professional activities include keynote presentations at international conferences, workshop chair roles for major AI events, and external PhD examination responsibilities across European institutions.

• Spatial Data Infrastructure (SDI)
• GIS Applications
• Machine Learning in Geoinformatics
• +3 more

Iwona Kaczmarek is a Professor in the Department of Systems Research at the Wrocław University of Science and Technology's Faculty of Spatial Management and Landscape Architecture. Her work focuses on advancing spatial data infrastructure (SDI), geoinformatics, and machine learning applications in urban and regional planning. She leads interdisciplinary research integrating GIS, natural language processing (NLP), and graph neural networks to address challenges in spatial planning, policy analysis, and socio-economic modeling. Research interests include: Spatial data integration and interoperability Machine learning for geospatial analysis Semantic web technologies for open data Urban sustainability and transit-oriented development Policy text mining and stakeholder analysis Recent work emphasizes: 1. Combining media sentiment analysis with economic indicators during crises (2025) 2. GeoAI-driven quality of life predictions using graph neural networks (2023) 3. Knowledge graph extraction from spatial development plans (2023) 4. Harmonizing local planning documents via NLP (2022) Collaborations include projects like the EYE initiative analyzing remote sensing for economic trends and SDI implementations in academic contexts. Over 75 documented outputs highlight her contributions to geospatial innovation and policy-relevant research.

Uli SattlerView profile

Professor

• Description Logics
• Automated Reasoning
• Ontology Engineering
• +4 more

Uli Sattler is a Professor in the Computer Science Department at the University of Manchester, specializing in logics for knowledge representation and automated deduction. He holds a PhD from RWTH Aachen (1998) and a habilitation from TU Dresden (2003). His research focuses on Description Logics, ontology engineering, and their applications in fields like molecular biology. He contributes to ontology languages such as OWL and develops practical inference algorithms. Research Interests: Description Logics, automated reasoning, ontology engineering, and their integration with AI systems. His work addresses challenges like module extraction, entailment explanation, and decision procedures using automata and tableau techniques. Awards: Best Paper Award (2008) ISWC Best Student Paper Award (2011) SWSA Ten-Year Award (2018) Advising & Grants: Supervised 17 students and participated in initiatives like Gender Advancement through Transforming Institutions. Active in organizing conferences and peer-review panels. Labs/Teams: Part of the Information Management Group and the Data Science Institute at the University of Manchester.

Sabrina AzziView profile

Lecturer

• Digital Health
• Artificial Intelligence
• Health Informatics
• +6 more

Sabrina Azzi is a Lecturer in Digital Business at the University of the West of Scotland’s School of Business & Creative Industries. She holds a PhD in Computer Science from the University of Quebec in Outaouais, specializing in Health Informatics and Ontology Engineering, with a research fellowship at the Canadian House of Commons. Her expertise spans telehealth evaluation, digital health for seniors, user experience design, ontology engineering, and AI-driven decision systems. She contributes to UN Sustainable Development Goals related to health and innovation. External roles include PhD examiner at Glasgow Caledonian University (2025), University College Birmingham (2025–2029), and Heriot-Watt University (2023). She leads the Desinformation Applications Lab-Canada and serves as an examiner at Université du Québec en Outaouais since 2024. Research interests focus on smart homes for assisted living, parliamentary analytics, and digital health policy. Notable awards include the 2024 Innovative Teaching Award and 2018 Two Cities, Curious Minds recognition. Her work bridges technology and societal challenges, particularly in aging populations and healthcare system modernization. Collaborations span Canada, Algeria, and the UK, addressing global health and digital transformation challenges.

Josiah WangView profile

Teaching Professor

• Artificial Intelligence
• Computer Vision
• Natural Language Processing
• +3 more

Josiah Wang is a Senior Teaching Fellow in the Department of Computing at Imperial College London, where he also serves as the degree coordinator and admissions tutor for the MSc Computing program. He teaches Python Programming and Introductory Machine Learning courses. Previously, he held postdoctoral research positions at Imperial College London and the University of Sheffield, focusing on Artificial Intelligence with specializations in Computer Vision and Natural Language Processing. He earned his PhD in Computer Science from the University of Leeds (2013), advised by Katja Markert and Mark Everingham. His research explored cross-modal learning, including pioneering work on learning object recognition from textual descriptions and multimodal machine translation. Notable contributions include the MultiSubs dataset and methods for phrase localization without paired training examples. Education: PhD in Computer Science, University of Leeds (2013) MSc in Computing, University of Leeds (2007), supervised by David Hogg Research Interests: Josiah's work bridges AI and human cognition, emphasizing systems that learn from limited data and integrate multiple modalities. His contributions span visually descriptive language annotation, image captioning, and multimodal translation. Though no longer active in active research, his legacy includes foundational papers on object recognition from text (BMVC 2009) and unsupervised phrase localization (ICCV 2019). Grants & Projects: MultiMT and MMVC projects (Imperial College London) CHIST-ERA ViSen project (University of Sheffield) Labs & Teams: Collaborated extensively with interdisciplinary teams at Imperial, Sheffield, Lyon, and Barcelona, including work on multimodal machine translation and visual grounding systems.

Albert Qiaochu JiangView profile

Research Fellow

• Machine Learning
• Theorem Proving
• Artificial Intelligence
• +3 more

Albert Qiaochu Jiang serves as a Visiting Research Fellow at the Department of Computer Science and Technology, University of Cambridge. His research integrates machine learning with formal theorem proving, focusing on neural theorem provers and mathematical reasoning systems. He leads the reasoning team at Mistral AI while maintaining academic supervision at Cambridge. His research interests center on machine learning for theorem proving , with specific expertise in neural-symbolic integration, autoformalization, and large language models for mathematical reasoning. His work bridges artificial intelligence with formal verification, developing systems that enhance automated reasoning capabilities through neural networks. Current projects involve improving premise selection for theorem provers, multilingual mathematical formalization, and creating efficient architectures for mathematical language models. Analysis of his recent publications reveals a strong focus on advancing neural theorem proving through innovative architectures like Target-Based Automated Conjecturing and Magistral. His research trajectory shows increasing sophistication in integrating language models with formal verification systems, with significant contributions to datasets like Numinamath and frameworks like Llemma. Key trends include optimizing compute efficiency in proof generation, enhancing multilingual mathematical reasoning, and developing interactive human-AI collaboration systems for formal mathematics. While no scientific awards are currently documented in available sources, his research output demonstrates significant impact in the intersection of AI and formal methods. As leader of Mistral AI's reasoning team, Jiang directs research on neural theorem proving systems while contributing to academic supervision at Cambridge. His work involves substantial industrial-academic collaboration, leveraging resources from both institutional contexts to advance mathematical AI. Current projects focus on creating practical systems for mathematical automation with real-world verification applications. His research operates at the intersection of academia and industry through Mistral AI's reasoning team, where he develops neural theorem proving systems with practical applications in formal verification. This dual affiliation enables rapid translation of theoretical advances into deployable tools for mathematical automation.