جست‌وجوی استاد و پژوهشگر

Samira Si-Said Cherfiمشاهده پروفایل

استاد

• Data Quality
• Conceptual Modeling
• Ontology Engineering
• +۵ مورد دیگر

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.

Ovidiu Șerbanمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Natural Language Processing
• Interactive Systems
• +۶ مورد دیگر

Ovidiu Șerban is a Research Fellow at the Data Science Institute, Imperial College London, leading the Data Observatory group. His work focuses on real-time Natural Language Processing, Data Curation, and Large Scale Visualization Systems. PhD in Computer Science (2013) - Joint from INSA de Rouen Normandy and Babeș-Bolyai University MSc in Artificial Intelligence (2009) - Babeș-Bolyai University BSc in Computer Science (2008) - Babeș-Bolyai University Research interests span Artifical Intelligence, Natural Language Processing, Interactive Systems, Affective Computing, and Deep Learning. Recent publications emphasize knowledge graph completion, temporal graph analysis, and multimodal data processing frameworks. Contributed to development of OVE (Open Visualization Environment) for scalable data rendering Created TKGQA dataset for temporal knowledge graph validation Advanced conflict-aware multilingual knowledge graph techniques Projects include SENTINEL for real-time event detection, Watchme for workplace analytics, Intuitel for e-learning enhancement, and Agentslang for distributed interactive systems. Affiliations include Imperial College London, University of Cambridge, and University of Reading.

• Network Science
• Philosophy of Science
• Social Networks
• +۱ مورد دیگر

Alan Baker is an Associate Professor of Philosophy at Swarthmore College. He held the Andrew W. Mellon Regional Fellowship in the Humanities during 2009–2010 and was affiliated with the Forum on Connections. His research centers on the philosophical foundations of network science, examining how network models prioritize connections over individual entities. This perspective challenges traditional views of identity in social relationships, biochemical systems, and complex networks, emphasizing patterns of links as defining characteristics. Scientific Awards: Andrew W. Mellon Regional Fellowship in the Humanities

• Database Systems
• Natural Language Processing
• Knowledge Graphs
• +۱ مورد دیگر

Nicolas HIOT is a Post-doctoral fellow at the University of Orleans affiliated with the LIFO laboratory (Laboratoire d'Informatique Fondamentale d'Orléans) and the Pamda project. His research bridges database systems, natural language processing, and medical informatics with a focus on text-to-database integration and consistency maintenance. His research interests center on: Database Systems for medical applications with emphasis on consistency and evolution Natural Language Processing for clinical text analysis and relation extraction Knowledge Graph construction from unstructured textual data Medical Informatics applications for healthcare data management Analysis of his 15 most recent publications (2020-2024) reveals a cohesive research trajectory at the intersection of databases and NLP. Key thematic clusters include automated medical database construction from clinical texts, consistency management in evolving RDF/property graph systems, and clinical entity/relation extraction for knowledge graphs. His work consistently addresses real-world challenges in healthcare data integration through tools like DataFix and ArchiTXT, demonstrating strong translational potential. Nicolas HIOT actively contributes to the LIFO research laboratory at the University of Orleans, collaborating extensively with Jacques CHABIN, Mirian HALFELD-FERRARI, and Dominique LAURENT. His technical output includes multiple software systems for database evolution management and clinical text processing, reflecting both theoretical contributions and practical implementations in semantic data management.

Andreas Åvitslandمشاهده پروفایل

دانشیار

• Physical Activity and Health
• Outdoor Education
• Physical Activity in School
• +۳ مورد دیگر

Andreas Åvitsland serves as Associate Professor of Sports Science and Physical Education at the Department of Education and Sports Science within the Faculty of Arts and Education at the University of Stavanger (UiS), a position he has held since 2020 following his PhD candidacy (2017-2020) at the same institution. His academic credentials include a PhD in Educational Sciences from UiS focusing on school-based physical activity interventions and mental health associations; an MSc in Sports Science from WNUAS examining workplace nudging strategies for stair usage; and a BSc in Sports and Physical Education from WNUAS investigating exercise impacts in correctional facilities. PhD: School interventions, physical fitness-mental health links, implementation MSc: Lifestyle choices, office building nudging for stair use BSc: Prison inmate physical activity and staff relationships Åvitsland's research centers on Physical Activity and Health with emphasis on fitness-mental health interplay, behavior change mechanisms, and active transportation. His work extends to Outdoor Education (Nordic friluftsliv) and School-Based Physical Activity Implementation , employing mixed-methods approaches to translate interventions into real-world settings. Key contributions address adolescent health through ecological frameworks and implementation science principles. Analysis of his 2017-2025 publications reveals consistent investigation of physical activity's role in adolescent mental health and academic performance, primarily through the longitudinal School in Motion project. His work demonstrates methodological diversity including cluster randomized trials, decision tree analysis, and process evaluations focused on Norwegian youth populations, with growing emphasis on environmental determinants of school travel behavior. Åvitsland actively contributes to UiS's Sports Science and Physical Education research group, with his work supported by institutional frameworks for public health research. While specific grant details aren't provided, his publications indicate collaboration with national research entities focused on youth health outcomes. His research infrastructure includes departmental resources within UiS's Faculty of Arts and Education, with particular alignment to school-based intervention studies involving Norwegian adolescents across diverse socioeconomic contexts.

• Civil Law
• Commercial Law
• Stock Exchange Law

Associate Professor Sylvia Tsoneva, PhD, is a faculty member in the Department of Law at New Bulgarian University (NBU). She holds a doctoral degree in Law with a dissertation on the legal nature of securities. Her office hours are Wednesday 9:00-11:00 and Friday 13:00-15:00 in Building 412. Her primary research explores civil law , commercial law , and stock exchange law , with a focus on liability frameworks, comparative European systems, and tort law evolution. She contributes to cross-border legal harmonization projects and examines contemporary issues in media accountability, employer liability, and human rights in private law contexts. Tsoneva's scholarly publications emphasize tort law , EU legal integration , and comparative analyses between Bulgarian and English legal systems. Recent works investigate media defamation, child liability, reverse mortgages, and state accountability under EU law, reflecting interdisciplinary engagement with financial, historical, and ethical dimensions. She has participated in key European research initiatives: Common Core of European Private Law (Causation in Tort Law working group, 2013) Common Core of European Private Law (Protection of Immovable Property working group, 2009) Impact of Directive 2002/65/EC (Institut für Finanzdienstleistungen, Hamburg, 2008) Currently, she teaches undergraduate courses: Civil Law (in English) and Corporate Law (in English) .

Debayan Banerjeeمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Explainability
• Machine Learning
• +۵ مورد دیگر

Dr. Debayan Banerjee is a Researcher at the Institute for Business Information Systems (IIS) and part of the Professorship for Business Informatics, especially Artificial Intelligence and Explainability at Leuphana University Lüneburg. His work bridges academic research with practical applications in knowledge management, network science, and AI systems. Institute: Institute for Business Information Systems (IIS) Professorship: Business Informatics, Artificial Intelligence and Explainability Location: Universitätsallee 1, C4.308b, Lüneburg (21335) Email: debayan.banerjee@leuphana.de Research interests focus on knowledge graph integration, hybrid intelligence systems, and explainable AI. His projects include USIN5G, ARDIAS, and INSTANT, emphasizing human-AI collaboration and scholarly data accessibility. Recent publications highlight SPARQL translation automation, hybrid question answering frameworks, and environmental impact analysis of language models. Collaborative work spans DBpedia-Wikidata interoperability, DBLP knowledge graph applications, and graph embeddings for QA systems. Education and advising : Mentored students include Mathias Gross, Fatemeh Ghoochani, and Soham Majumder, focusing on final theses related to AI-driven data extraction and knowledge graph development.

Recep Firat Cekinelمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Multimodal Fact-Checking
• Explainable AI
• +۷ مورد دیگر

Recep Firat Cekinel is a Turkish NLP researcher who recently obtained his Ph.D. in Computer Engineering from Middle East Technical University (METU). He spent 13 months as a visiting predoctoral researcher at the University of Tübingen and is currently a researcher on the EU-funded EXA4MIND project, where he develops NLP pipelines that convert natural language into database queries using large language models. His research focuses on responsible, scalable AI systems and bridges foundational NLP work with real-world applications. Education: Ph.D. in Computer Engineering, Middle East Technical University (METU), Türkiye Visiting Predoctoral Researcher, University of Tübingen, Germany (13 months) Research Interests: Dr. Cekinel’s work spans natural language processing , multimodal fact-checking , explainable AI , and large language models . He is particularly interested in building responsible and scalable AI systems that integrate foundational research with practical deployments, such as natural-language interfaces for high-performance computing environments. Recent Publication Trends: His 2025 publications reveal a concentrated effort on multilingual and multimodal fact-checking , satire-style debiasing , and NL-to-database-query generation . Earlier work explores graph-based event extraction , Turkish irony detection , and cultural-heritage text mining , demonstrating a trajectory from low-resource Turkish NLP toward globally applicable, responsible-AI systems. Contact & Code: Email: rfcekinel@ceng.metu.edu.tr Office: METU Computer Eng. Dept. A-206, 06800 Ankara, Turkey Phone: +90-(312)-210-5593 GitHub: firatcekinel Google Scholar: profile available

Aris Anagnostopoulosمشاهده پروفایل

استاد

• Stochastic Analysis of Dynamic Processes
• Modeling and Mining in Social Networks
• Data Mining and Algorithms for the WWW
• +۳ مورد دیگر

Aris Anagnostopoulos is a Professor at the Department of Computer, Control, and Management Engineering (Dipartimento di Ingegneria Informatica, Automatica, e Gestionale) at Sapienza University of Rome since April 2012. His academic journey includes a Marie-Curie fellowship at Sapienza University and a postdoctoral position at Yahoo! Research in Santa Clara, CA. His educational background includes: Ph.D. in Computer Science, Brown University, Providence, RI Sc.M. in Applied Mathematics, Brown University, Providence, RI Sc.M. in Computer Science, Brown University, Providence, RI Diploma in Computer Engineering and Informatics, University of Patras, Patras, Greece Professor Anagnostopoulos's research focuses on the design and analysis of algorithms with applications in data mining and data science. His work spans stochastic analysis of dynamic processes, social network modeling and mining, WWW algorithms, randomized and approximation algorithms, information retrieval, and information security. His research has evolved to address contemporary challenges in federated learning, knowledge graphs, and ethical AI considerations in recommendation systems. His recent publications demonstrate a strong trend toward addressing real-world applications of data science and machine learning, particularly in healthcare, social media analysis, and privacy-preserving techniques. His work shows increasing interdisciplinary collaboration, especially with medical researchers, while maintaining strong theoretical foundations in algorithm design. Among his notable scientific awards are: Google Focused Research Award (1 of 6 PIs), 1M USD Junior Fellow, School for Advanced Studies, Sapienza University of Rome Personal research grant, Swedish Research Foundation, 200K euro, 2011 (declined) Best Poster Award, 4th International Conference on Web Search and Data Mining (WSDM 2011) Marie Curie International Incoming Fellowship, 160K euro, 2010 Paris Kanellakis Fellowship, Brown University Runner Up, Best Paper Award, 14th International World Wide Web Conference 2005 (WWW 2005) Professor Anagnostopoulos serves as the academic responsible for mobility (RAM) for the Data Science master's program and has developed comprehensive teaching materials for data science education. He teaches courses including Social Networks and Online Markets, Algorithmic Methods of Data Mining, Data Mining, and Algorithm Design. His teaching approach emphasizes both theoretical foundations and practical applications, with extensive use of AWS and Python-based tools to prepare students for industry certification.

• Data Science
• Big Data
• Natural Language Query Processing
• +۴ مورد دیگر

Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.

Andreas Harthمشاهده پروفایل

استاد

• Decentralized information systems
• World Wide Web technologies
• Blockchain environments
• +۷ مورد دیگر

Prof. Dr. Andreas Harth holds the Chair of Business Information Systems, especially Technical Information Systems, at Friedrich-Alexander University Erlangen-Nuremberg (FAU), where he has been a faculty member since 2018. He also serves as a department head at the Fraunhofer IIS-SCS in Nuremberg. His academic work spans both theoretical research and practical applications in decentralized information systems, with strong connections to industry through numerous collaborative projects. Harth completed an apprenticeship as a banker before studying computer science. He earned his doctorate from the Digital Enterprise Research Institute at the National University of Ireland, Galway, and completed his habilitation at the Karlsruhe Institute of Technology. His academic journey included teaching and research stays at the universities of Heidelberg, Innsbruck, Stanford, and Southern California, providing him with a global perspective on information systems research. His research focuses on developing methods and technologies for decentralized information systems found in the World Wide Web and blockchain environments, with applications in companies. He investigates data integration using Semantic Web and Linked Data technologies, process modeling languages, and their applications in the Internet of Things, Web of Things, and Industry 4.0 contexts. His work bridges theoretical computer science with practical business applications, particularly in data sovereignty and decentralized architectures. Analysis of his recent publications reveals a strong trend toward Solid protocol applications, knowledge graph technologies, and the integration of large language models with semantic web technologies. His research increasingly focuses on practical implementations in enterprise settings, healthcare data management, and manufacturing systems, demonstrating the real-world applicability of his theoretical work. As a member of FAU's research focus on Digitalization and Innovation, Harth collaborates with strategic partners including the Fraunhofer Institute for Information Systems (IIS) and major German industrial companies. His work contributes significantly to FAU's position as one of Germany's most research-intensive universities, particularly in the fields of business informatics and decentralized systems.

Laura Dietzمشاهده پروفایل

دانشیار

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۶ مورد دیگر

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire, where she leads the TREMA lab. Her academic journey began with a PhD from the Max Planck Institute for Informatics in Saarbruecken, Germany (2011), followed by postdoctoral positions at the University of Massachusetts Amherst (2010-2015) and University of Mannheim (2015-2016). Her educational background includes PhD studies at both the Max Planck Institute for Informatics (2007-2011) under Prof. Gerhard Weikum and Prof. Tobias Scheffer, and earlier research at Humboldt University in Berlin. She has built a distinguished career bridging theoretical computer science with practical applications in information retrieval and machine learning. Dietz's research primarily focuses on the intersection of information retrieval, natural language processing, and knowledge graphs, with a parallel research initiative in watershed data science. She is particularly known for her work on entity-aspect linking, complex answer retrieval, and the vision of automatic Wikipedia construction. Her approach integrates fine-grained knowledge annotations with text understanding to create comprehensive information systems that go beyond traditional 10-blue-links search paradigms. In watershed data science, she applies similar machine learning techniques to environmental data streams, focusing on solute transport analysis during storm events. Her recent publications reveal a strong trend toward fine-grained semantic understanding, particularly in entity-oriented search tasks. She has pioneered methods for entity-aspect linking that significantly improve retrieval accuracy by capturing different contexts in which entities appear. Her work increasingly integrates knowledge graphs with neural architectures, showing sophisticated understanding of how to leverage both structured and unstructured information for better search experiences. Best paper award at JCDL 2018 for work on entity-aspect linking NSF CAREER Award (2019-2023) for "Utilizing Fine-grained Knowledge Annotations in Text Understanding and Retrieval" OSSI Award 2013 from UMass ICB3 for open-source hardware/software Dietz actively mentors PhD and Masters students through the TREMA lab, with current research focusing on entity ranking, topic extraction, conversational search, and watershed forecasting. Her grant portfolio includes the NSF CAREER award and funding from the Northeast Big Data Innovation Hub for forecasting salinity in rivers during storm events. She has also coordinated the TREC Complex Answer Retrieval track (2017-2019), creating important benchmarks for the IR community. The TREMA lab (Text Retrieval, Entity Modeling, and Applications) serves as the hub for Dietz's research activities, bringing together students and collaborators to work on cutting-edge problems in information access. The lab's work spans both theoretical contributions to information retrieval and practical applications in domains ranging from environmental science to scientific publication analysis.

• Musculoskeletal Pathology
• Bone Tumor Genetics
• Soft Tissue Sarcomas
• +۵ مورد دیگر

Professor Adrienne Flanagan is a distinguished academic in Musculoskeletal Pathology at University College London's Cancer Institute within the Department of Pathology. She practices clinical pathology at the Royal National Orthopaedic Hospital and serves as Meeting Secretary of the Pathological Society of Great Britain and Ireland, while also holding trustee positions with Sarcoma UK, The Jean Shanks Foundation, and The Chordoma Foundation (USA). Her educational background includes a Bachelor of Medicine from the Royal College of Surgeons in Ireland (1981) and a PhD from the University of London (1990), completed during her tenure as a Medical Research Council Training Fellow at St George's Hospital Medical School. Flanagan's research program centers on identifying genetic alterations in bone and soft tissue tumors, translating molecular findings into clinical diagnostics. She pioneered the discovery that brachyury expression serves as a universal diagnostic biomarker for chordoma, first reported IDH1/IDH2 mutations in cartilaginous tumors (linking them to Ollier and Maffucci syndromes), and identified recurrent H3F3 mutations in giant cell tumor of bone and chondroblastoma. Her work with the International Cancer Genome Consortium (2010-2015) established genomic landscapes for multiple bone tumor types. Her publications demonstrate consistent leadership in molecular tumor classification, with recent work spanning chromothripsis in osteosarcoma, HOX gene expression in spinal development, and AI-driven mitotic figure detection. Current research focuses on reclassifying tumors through molecular signatures to improve diagnostic accuracy and treatment stratification. Goudie Award for pathological science contributions (2017) William Gerald Award from Memorial Sloan Kettering Cancer Centre Senior NIHR Fellowship (2017) Flanagan maintains close collaboration with international patient advocacy groups and the Wellcome Trust Cancer Institute. Her clinical and research work directly informs diagnostic services through molecular-pathological correlation. She leads the Research Department of Pathology at UCL and contributes to national pathology training system development using digital pathology platforms.

Klim Zaporojetsمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Information Extraction
• Entity Linking
• +۳ مورد دیگر

Klim Zaporojets is a Marie Skłodowska-Curie Postdoctoral Fellow in the Department of Computer Science at Aarhus University, where he conducts research within the Data-Intensive Systems Group. His work bridges theoretical advancements and practical applications in natural language understanding. His research focuses on information extraction systems that connect textual content with structured knowledge bases. His methodology emphasizes leveraging external knowledge sources to enhance information extraction performance, particularly in document-level contexts where entities evolve over time. His work spans temporal relation extraction, entity linking, and biomedical text mining applications. The publication record reveals a strong focus on document-level information extraction with increasing emphasis on temporal aspects and knowledge integration. Recent work explores large language model applications for graph learning and calibration challenges in LLMs, showing evolution from traditional NLP tasks to cutting-edge foundation model research. His publications appear in top-tier venues including ACL, EMNLP, CIKM, and NeurIPS. His scientific recognition includes the prestigious Marie Skłodowska-Curie Postdoctoral Fellowship, supporting his research at Aarhus University. His work has produced several influential datasets including DWIE, TempEL, and BioDEX that have become benchmarks in document-level information extraction. Zaporojets maintains active collaborations with researchers at Ghent University (evidenced by his ugent.be email address) and has contributed to multiple interdisciplinary projects spanning computational linguistics, healthcare informatics, and knowledge representation. His technical contributions include open-source implementations of his research, demonstrating commitment to reproducible science.

Julian Szymańskiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Natural Language Processing
• Blockchain Technology
• +۷ مورد دیگر

Dr. hab. inż. Julian Szymański serves as Associate Professor at the Department of Computer Architecture within the Faculty of Electronics Telecommunications and Informatics at Gdańsk University of Technology. He concurrently holds the position of Deputy Director at the Industrial Doctoral School, demonstrating dual leadership in academic instruction and doctoral program administration. His research spans artificial intelligence applications across diverse domains including sustainable agriculture, healthcare monitoring, and privacy-preserving systems. Key focus areas include natural language processing, blockchain implementation for sensitive data services, and multi-modal signal analysis. His work bridges theoretical computer science with practical applications in agritech, medical diagnostics, and secure service provision. Recent publications reveal a strong trend toward interdisciplinary AI solutions, particularly in sustainability (2025 smart farming chapter), human-AI alignment techniques, and privacy frameworks compliant with GDPR. His respiratory rhythm classification research demonstrates methodological rigor through multi-sensor data fusion. Academic contributions include: ERASMUS+ funded Modena MoltiMondi project as project manager Development of EditPrefs methodology for cost-effective preference dataset creation Gold-standard multi-genre datasets for entity linking evaluation As an educator, he has taught 185 courses since 2018 across artificial intelligence, deep learning, and intelligent information systems, mentoring students through diploma projects. His laboratory work focuses on IoT-based healthcare frameworks and blockchain implementations for privacy-sensitive services, with current projects exploring AI-driven sustainability solutions.