Hayder Murad is a researcher with expertise in machine learning, sentiment analysis, and hybrid filtering algorithms, particularly applied to intelligent tutoring systems and medical diagnostics. His work bridges computational methods with practical applications in education and healthcare. He earned his PhD from the University of Portsmouth in 2019 with a thesis titled An integrated approach to recommending online video materials through sentiment analysis and hybrid filtering algorithms . His recent publications focus on knowledge graph construction, large language model applications, and open science initiatives. Notable contributions include enhancing systematic literature reviews, improving dataset interoperability, and advancing FAIR data principles across disciplines. Research Interests Machine learning for healthcare diagnostics Hybrid recommendation systems Knowledge graph development Open science infrastructure Intelligent tutoring systems LLM-based research synthesis
Sihem Amer-Yahia is a distinguished Research Professor at the University of Grenoble Alpes (affiliated with Grenoble Informatics Laboratory ), with significant contributions to database systems , data exploration , and fairness in AI . Her work bridges human-computer interaction and machine learning to create systems that enhance data-driven decision-making. Research Pillars : Algorithmic fairness, interactive data mining, recommender systems, and human-AI collaboration Recent Advances : 2023-2025 publications focus on statistically sound hypothesis testing , multi-objective recommendation , and conversational analytics Leadership : Co-organized major conferences (DASFAA 2024) and led DEI initiatives in database communities Her 15 most recent articles (2020-2025) span topics like producer fairness in recommendation , statistical hypothesis frameworks , and AI-powered education systems , with keywords covering database optimization , reinforcement learning , and ethical data mining . She actively contributes to ACM/IEEE journals and VLDB/SIGMOD conferences.
Carlo Curino is a researcher at Microsoft Research , focusing on database systems, cloud computing, and machine learning integration. He has collaborated extensively with institutions including MIT, Microsoft, and the University of Wisconsin-Madison. His research spans Geo-distributed data analytics Automated configuration tuning Tensor-based database systems Data lake optimization Spark performance engineering Recent publications highlight his work on AI-driven systems like MotherNet and Rockhopper , alongside contributions to query processing over compressed data and log-structured tables. Collaborators include prominent figures such as Raghu Ramakrishnan and Jesús Camacho-Rodríguez . Key projects involve LST-Bench (cloud storage benchmarking), AutoComp (data compaction), and PyFroid (commodity workstation analytics). His work bridges database optimization with modern machine learning demands in enterprise environments.
Luca Cagliero is an Associate Professor in the Department of Control and Computer Engineering at Politecnico di Torino (Polytechnic University of Turin), Italy. His research spans multiple domains within computer science, with particular expertise in data mining, machine learning, natural language processing, and multimodal analysis. He has established a prolific research career with over 150 publications spanning from 2009 to the present, demonstrating consistent scholarly productivity. Dr. Cagliero's research interests focus on the intersection of artificial intelligence and practical applications. His work addresses fundamental challenges in data mining, information retrieval, and educational technology, with recent publications showing increasing emphasis on large language models, multimodal analysis, and explainable AI. He has made significant contributions to text summarization techniques, database systems, and applying machine learning to educational contexts. His recent publications (2023-2025) demonstrate a clear research trajectory toward multimodal AI systems, with particular attention to the integration of vision and language processing. His work spans theoretical contributions in machine learning methods as well as practical applications in educational technology, social media analysis, and document understanding. The breadth of his collaborations across different application domains indicates a versatile research profile that bridges theoretical and applied computer science. Dr. Cagliero has mentored numerous researchers who have become his frequent collaborators, including Lorenzo Vaiani, Moreno La Quatra, and Davide Napolitano. His work has appeared in top-tier venues including ACL, IEEE Transactions on Knowledge and Data Engineering, and Expert Systems with Applications, reflecting the high quality and impact of his research contributions.
Nitin Gupta is a researcher with affiliations spanning institutions like Google, Cornell University, and Carnegie Mellon University. His work bridges theoretical and applied computer science, focusing on database systems, data-driven applications, and knowledge graphs. Research interests include: Structured data on the web and semantic publishing Scalability in virtual environments and declarative programming Integration of large language models (LLMs) into scholarly workflows FAIR data principles and research data management Recent publications explore hybrid question answering datasets, LLM applications for literature reviews, and knowledge graph construction for software engineering. His contributions emphasize machine-actionable metadata, open science, and enhancing reproducibility through standardized research artifacts. Collaborations span institutions and disciplines, with coauthorships on topics like: Game development and data coordination Non-invasive brain stimulation analysis Neuro-symbolic approaches for digital libraries Ontology matching and FAIR digital objects
Anish Das Sarma is a researcher affiliated with Google, USA , specializing in uncertain data management, MapReduce algorithms, and knowledge graph systems. He earned a PhD from Stanford University in 2010 under the supervision of Jennifer Widom and Alon Halevy, with a dissertation on "Managing Uncertain Data." His career spans collaborations with leading institutions, focusing on scalable data integration, social choice theory, and machine learning applications in scholarly knowledge organization. PhD in Computer Science, Stanford University (2010) Key collaborations: Stanford, Google Research, NFDI4DataScience His research interests intersect uncertain data modeling , MapReduce optimization , and large language model applications for scientific synthesis. Recent work includes FAIR data frameworks, ontology learning, and clinical entity linking. Article trends highlight his evolution from foundational database systems (2004-2015) to modern applications of LLMs in scholarly communication (2023-2024). Key areas: scalable algorithms, research data management, and ethical AI.
Tanya Braun is a Junior Professor in the Institute of Computer Science at the University of Münster, Department of Mathematics and Computer Science. She leads the Data Science research group, focusing on statistical-relational AI, human-aware AI, and text understanding. Her work bridges formal AI methods with real-world applications in healthcare, digital humanities, and public sector systems. Education: Bachelor's and Master's in Computational Informatics, Hamburg University of Technology Doctorate in Computer Science, University of Lübeck (2020), thesis: 'Rescued from a Sea of Queries - Exact Inference in Probabilistic Relational Models' Her research centers on probabilistic inference in relational domains , with a focus on lifted inference techniques that exploit symmetries to scale reasoning. She investigates human-aware AI , particularly how AI systems can reconcile learned models with human expectations to improve explainability and trust. Her work on text understanding addresses challenges in data-scarce settings such as digital humanities, where traditional large language models fail. She has developed methods for identifying and enriching subjective content descriptions, topic modeling in specialized domains, and feedback-driven model improvement. The 15 most recent publications highlight a strong trajectory in lifted inference, model compression, privacy-preserving AI, and explainability . Her work integrates formal AI foundations with practical concerns in high-stakes domains like healthcare. She frequently publishes in top venues such as AAAI, IJCAI, ECAI, and Artificial Intelligence, often in collaboration with Ralf Möller, Marcel Gehrke, and Jan Speller. Scientific Awards: No specific awards listed in the provided text. Tanya Braun actively advises students and leads the HAPPI project, which focuses on human-AI model reconciliation using lifted probabilistic inference. She has supervised multiple theses and mentored researchers including Jan Speller (PostDoc), Nazlı Nur Karabulut, and Sagad Hamid. She has secured funding from the Ministry of Culture and Science of North Rhine-Westphalia for her research. She is deeply involved in academic service: serving as program co-chair for KI 2025, guest-editing special issues in journals like Künstliche Intelligenz and Annals of Mathematics and Artificial Intelligence , and organizing major conferences including ICCS and KR. Labs and Teams: She leads the Data Science Group at the University of Münster, which conducts research in AI, probabilistic modeling, and data science. The group is actively involved in teaching and mentoring students in advanced AI topics.
Philipp Cimiano is a Professor at the Faculty of Engineering, Bielefeld University, and leads the Semantic Databases Group. He holds additional roles as Coordinator of the Cognitive Interaction Technology Center (CITEC) and Director of the Joint Artificial Intelligence Institute (JAII). His research focuses on the intersection of language, semantics, and knowledge representation, with applications in Explainable AI , Knowledge Graphs , and AI in Medicine . Education: University of Stuttgart, University of South Australia, Karlsruhe Institute of Technology (KIT) Key Research Areas: Knowledge Representation, Ontologies, Explainable AI, Clinical Decision Support His recent work emphasizes dialogue-based XAI , federated learning , and semantic data integration . He has secured funding from the German Research Foundation (DFG) and the European Union for projects like TRR 318 "Constructing Explainability" and Pret-a-LLOD. Scientific Awards Carl Adam Petrie Prize, KIT Faculty of Business and Economics Editorial Roles Co-editor, Journal of Applied Ontology Area Editor, Semantic Web Journal Editorial Board, Journal of Web Semantics Notable Projects TRR 318 (Subprojects B01, C05, INF) 3B: Bots Building Bridges for online deliberation LLM4KMU: Open Source LLMs for SMEs
Prof. Dr.-Ing. Dieter Krause is a faculty member at the Institute of Product Development and Engineering Design at Hamburg University of Technology (TUHH). His academic career spans decades, with a focus on modular product development , lightweight design , and additive manufacturing across industries like aerospace, automotive, and medical technology. Research Interests : Modularization strategies for sustainable product families Integration of AI and data-driven methods in design engineering 3D-printed medical phantoms for interventional training Composite material analysis and tribological interface design Scientific Contributions : 2015 Hamburg Teaching Award for excellence in university instruction DFG Review Board member for Product Development Leadership in international conferences and academic governance
Claus Stadler is a researcher at the Business Information Systems department within the Institute of Computer Science at the University of Leipzig , affiliated with the Agile Knowledge Engineering and Semantic Web (AKSW) group since 2011. His work centers on Semantic Web technologies and data integration challenges. Specializes in RDB-RDF query rewriting/optimization and infrastructure for semantic knowledge bases. Contributor to key projects like LinkedGeoData (geospatial data integration) and Sparqlify (SPARQL-to-SQL rewriter). Research Focus : • Modeling spatial/temporal data for the Semantic Web • Scalable query rewriting using unsatisfiability testing • Update propagation in semantic knowledge graphs
Nadeen Fathallah is a researcher at the University of Stuttgart, affiliated with the Analytic Computing group at KI. Her work spans AI applications for accessibility, computer vision, and knowledge engineering. Research Focus: Web accessibility, ontology learning, and LLM-based solutions for Deaf/Hard of Hearing communities Projects: Key contributor to the IKILeUS project (Integrated AI in Teaching) at the University of Stuttgart Teaching: Has served as teaching assistant and assistant lecturer at German International University, German University in Cairo, and The Knowledge Hub Her research explores: Automated detection/correction of web accessibility violations (e.g., AccessGuru platform) Improving video captions using large language models Accessibility tools for tabular data (EchoTables) Ontology learning pipelines (NeOn-GPT, LLMs4Life) Recent work shows a focus on combining LLMs with domain-specific challenges across multiple fields, particularly emphasizing inclusive design principles. Contact details: Office at Universitätsstraße 32, Stuttgart, Germany (Room: 2.312b). Available via +49 711 685 88130.
Cédric du Mouza is a full Professor within the CEDRIC Laboratory at the Conservatoire National des Arts et Métiers (CNAM) in Paris, France. His research activities lie at the intersection of database systems, data mining, and digital humanities, with a strong emphasis on social network analysis, knowledge base population, and spatial/temporal data management. Education details are not explicitly provided in the source material. His research interests can be summarized as follows: Database Technologies: scalable indexing, distributed repositories, spatial and temporal data structures. Data Mining & Machine Learning: community detection, influence maximization, recommender systems. Digital Humanities: prosopographic databases, historical social networks, credibility assessment of historical sources. Social Media Analytics: Twitter user profiling, early detection of popular accounts, reducing filter bubbles. Across more than two decades, Prof. du Mouza has produced a rich scholarly record that blends theoretical advances with practical applications. Recent publications (2020-2024) highlight a methodological shift toward end-to-end evaluation frameworks for knowledge base population, uncertainty modeling in historical corpora, and real-time influence maximization in advertising ecosystems. These works are published in premier venues such as SIGIR , VLDB Journal , WISE , CIKM , and leading French conferences (EGC, BDA). No specific scientific awards, funded projects, or doctoral student names are mentioned in the supplied text. Prof. du Mouza collaborates extensively within interdisciplinary teams involving historians, computer scientists, and statisticians, reflecting CEDRIC’s integrative research culture. While no dedicated laboratory or team name is provided, his continuous affiliation with CEDRIC/CNAM confirms an active and ongoing role in both research and graduate-level education.
Cédric Lammari is a Researcher at the CEDRIC Laboratory of the Conservatoire National des Arts et Métiers (CNAM) . His work spans Cybersecurity , Healthcare Systems Protection , and Information Systems Engineering . Key research themes include Cyber-Physical Security , Ontology Development , and Data Anonymization . Notable contributions: Security Ontology for Healthcare Systems , Threat Analysis Frameworks , and Reverse Engineering for Generalization Hierarchies . Recent publications focus on cybersecurity remediation , attack scenario formalization , and data privacy frameworks . Collaborative work includes threat propagation modeling and semantic-based security analysis .
Samira Si-Said Cherfi is a Professor at the Centre d'études et de recherche en informatique et communications (CEDRIC) within the Conservatoire National des Arts et Métiers (CNAM) in Paris. With a research career spanning over 25 years, she has established herself as a leading expert in data quality, conceptual modeling, and ontology engineering. Her work bridges theoretical computer science with practical applications in healthcare systems, business process management, and knowledge representation. Her research interests focus on data quality assessment , conceptual modeling methodologies , ontology engineering for complex systems , and cyber-physical security in healthcare infrastructures . She has pioneered approaches for evaluating RDF data completeness, developing quality metrics for conceptual schemas, and creating ontologies for healthcare security. Her work demonstrates how formal modeling techniques can solve real-world problems in information systems. Analysis of her recent publications reveals a strong trend toward cyber-physical security and healthcare information systems , where she applies semantic technologies to address cascading effects in critical infrastructures. Her work consistently connects theoretical foundations in conceptual modeling with practical applications in knowledge graphs and data integration. She has made significant contributions to understanding how OWL semantics can be effectively utilized in RDF-based knowledge graphs. As an active member of the academic community, she has served as guest editor for special journal issues and contributed to major international conferences including RCIS, CAiSE, and EDOC. Her leadership in the field is evident through her editorial roles and collaborative research projects. Professor Si-Said Cherfi leads research within the CEDRIC laboratory, specifically contributing to the 'Complex data, machine learning and representations' and 'Data mining and statistics' research teams. Her work often involves interdisciplinary collaboration with healthcare professionals, security experts, and industry partners to address complex challenges in information systems security and data quality.
Dr. Inka Hähnlein is a Researcher at the Department of Educational Psychology within the Faculty of Philosophy III at Martin Luther University Halle-Wittenberg. Her academic trajectory includes doctoral studies at the University of Passau and ongoing research positions focused on educational psychology and technology-enhanced learning since 2018. Her research explores: Epistemological beliefs and metacognitive processes in learning Knowledge modeling and domain representation methodologies Instructional interventions for teacher education Technology-mediated learning environments She has developed specialized instruments like the StEB inventory for assessing teacher candidates' epistemological frameworks. Hähnlein's publications demonstrate strong emphasis on: Experimental studies in mathematics education Knowledge visualization techniques Psychometric validation of educational assessments Technology-supported teacher development Her work frequently employs quasi-experimental designs and knowledge modeling approaches. As a member of the university's Commission on the Future of Studies and Teaching, she contributes to pedagogical development. She also prepares examination materials for state teaching certifications in psychology, demonstrating teaching-related engagement despite her primary research focus.
