جست‌وجوی استاد و پژوهشگر

Emanuel Sallingerمشاهده پروفایل

استاد

• Information Systems Engineering
• Computational Logic
• Scalable Artificial Intelligence
• +۸ مورد دیگر

Emanuel Sallinger is a Full Professor at TU Wien's Databases and Artificial Intelligence Group and Vice Dean of Academic Affairs for Business Informatics and Data Science. He leads the Knowledge Graph Lab, focusing on scalable knowledge-based systems, reasoning in knowledge graphs, and AI integration. His research spans computational logic, database theory, and blockchain applications. Education: PhD in Computer Science (awarded 'sub auspiciis praesidentis rei publicae'), Master's degrees in Computational Intelligence and Informatics Management, and a Bachelor's in Software and Information Engineering. Research Interests: Knowledge graphs (construction, reasoning, scalability), logic-based systems, AI/ML integration with databases, enterprise architecture modeling, and financial knowledge systems. His work emphasizes practical applications like enterprise modeling, sustainable waste management, and regulatory compliance. Grants & Projects: Lead Vienna Science and Technology Fund (WWTF)-funded Knowledge Graph Lab. Involved in projects like 'Knowledge Graph-driven Tour Management' (sustainability), 'SustainGraph' (waste processing), and 'Enterprise Architecture Knowledge Graphs'. Teaching: Offers courses on Knowledge Graphs, Generative AI, Database Systems, and research methodology. Supervises doctoral and master's students in AI, databases, and knowledge representation. Labs/Teams: Knowledge Graph Lab at TU Wien, collaborating with industry on blockchain-based systems, financial AI, and enterprise architecture frameworks.

Michael J. Cafarellaمشاهده پروفایل

دانشیار

• Databases
• Information Extraction
• Data Integration
• +۶ مورد دیگر

Michael J. Cafarella is an Associate Professor in the Computer Science and Engineering department at the University of Michigan . His research focuses on databases, information extraction, data integration, and data mining, with applications in economics, social media analysis, and combating human trafficking. He leads the Software Systems Lab and Michigan Database Group . Scientific Awards NSF CAREER award Sloan Research Fellowship (2016) 2018 VLDB Ten-Year Best Paper award Research Impact : Cafarella co-founded the Hadoop open-source project and Lattice Data (acquired by Apple). His work on DeepDive and DARPA MEMEX was featured on 60 Minutes and in Scientific American . Funding from The Census Bureau, DARPA, Google, NSF, Yahoo!, General Electric, and Dow.

Tova Miloمشاهده پروفایل

استاد

• Databases
• Big Data Management
• Machine Learning for Data Management
• +۷ مورد دیگر

Professor Tova Milo is the Chair for Information Management at the School of Computer Science, Tel Aviv University, leading the prominent Databases Lab (DB Group). Her research spans databases, big data management, crowd-based data sourcing, and business process querying, with significant contributions to data integration and semi-structured data systems. Her research interests focus on innovative approaches to data management challenges through machine learning integration, crowd computing, and business process analysis. Key projects include Business Process Querying (BPQ) for analyzing BPEL specifications, MoDaS for crowd-based data management, and PROX for data provenance summarization. Her work bridges theoretical foundations with practical applications in fraud detection, recommendation systems, and data cleaning. Analysis of her recent publications reveals strong trends in human-in-the-loop data management systems, with growing emphasis on crowd integration for data cleaning and knowledge acquisition. Her research increasingly combines traditional database theory with machine learning techniques for scalable big data processing, while maintaining focus on business process modeling and provenance tracking. ACM PODS Alberto O. Mendelzon Test-of-Time Award (2010) ERC Advanced Investigators grant MoDaS (2011) The Weizmann Prize for Exact Sciences (2017) VLDB Women in Database Research award (2017) IEEE TCDE Impact award (2022) ISF Breakthrough Research Grant (2022) Doctorate Honoris Causa, University of Zurich (2023) ACM Fellow Member of Academia Europaea Professor Milo has advised over 30 graduate students including PhD candidates like Yael Amsterdamer and Ohad Greenshpan, and numerous MSc students working on projects including MoDaS, BPQ, and EDOS. Her research has been supported by major grants including the ERC Advanced Investigators grant and ISF Breakthrough Research Grant. She actively collaborates with industry partners including IBM and Microsoft, particularly in business process management standards. She directs Tel Aviv University's Databases Lab, which maintains the DB Group with multiple research streams including business process querying (BPQ), crowd-based data management (MoDaS), and self-adaptive data dissemination (EDOS/COLT). The lab operates from the Schreiber Building (M-20) and maintains strong international collaborations, particularly with European institutions through the ERC-funded MoDaS project.

David Chaves-Fragaمشاهده پروفایل

استادیار

• Knowledge Graphs
• Data Integration
• Semantic Web
• +۴ مورد دیگر

David Chaves-Fraga is an Assistant Professor at Universidade de Santiago de Compostela (Spain), affiliated with CiTIUS (Center for Intelligent Technologies) and a research collaborator at KU Leuven's DTAI group. His expertise lies in Knowledge Graph Construction (KGC), focusing on declarative mapping rules, data integration, and semantic web technologies. He completed his PhD at Universidad Politécnica de Madrid in 2021, researching Knowledge Graph Construction from heterogeneous data sources. Education PhD in Artificial Intelligence, Universidad Politécnica de Madrid (2016–2021) Master in Artificial Intelligence, Universidad Politécnica de Madrid (2015–2016) Bachelor in Computer Science, Universidade de Santiago de Compostela (2011–2015) Research Interests Dr. Chaves-Fraga specializes in optimizing data integration systems using declarative rules (e.g., RML), scalable KG materialization, and benchmarking tools like KROWN. He emphasizes reproducibility and sustainability in KG creation, advocating for community-driven standards. His work bridges theory and practice, addressing challenges in real-world KG adoption. Contributions He co-chairs the W3C Knowledge Graph Construction Community Group, organizes workshops like KGC and Sem4Tra, and coordinates initiatives like Open Summer of Code. His tools (e.g., SDM-RDFizer, RMLdoc) are widely used in the semantic web community. Key themes include RDF-star generation, SHACL constraint extraction, and ontology-mapping interoperability.

Ognjen Savkovicمشاهده پروفایل

استادیار

• Knowledge Graphs
• Semantic Web
• Database Management
• +۷ مورد دیگر

Ognjen Savkovic is an Assistant Professor (RTD-a) at the KRDB Research Center for Knowledge and Data, Faculty of Engineering, Free University of Bozen-Bolzano, Italy. He is based at the NOI Techpark in Bolzano and is actively involved in research on Knowledge Graphs, Semantic Web, and data quality. His work bridges formal logic and machine learning to enhance data management systems. His research interests include Knowledge Graphs, Semantic Web, Database Management, Data Quality, Artificial Intelligence, and Logic Reasoning. He focuses on schema validation (e.g., SHACL), property graph schemas (PG-Schema), and integrating machine learning with declarative languages like Datalog for industrial applications such as welding quality monitoring and cloud resource configuration. His recent publications show a strong trend in semantic technologies for industrial applications, particularly in collaboration with Bosch and Siemens. His work spans theoretical foundations of schema languages and practical implementations in smart manufacturing and cloud systems. He has published in top venues including WWW, ISWC, CIKM, and SIGMOD. Scientific Awards: No specific awards mentioned. Ognjen Savkovic advises students and collaborates on research projects, though specific students are not listed. His research involves significant grant-funded collaborations, particularly in EU-level industrial AI and semantic technology initiatives. He has contributed to projects involving Siemens and Bosch, focusing on semantic diagnostics and scalable data science solutions. He is a key member of the KRDB Research Center, contributing to a vibrant research team working on knowledge representation, reasoning, and industrial applications of semantic technologies.

• Artificial Intelligence
• Intelligent Systems
• Multi-Agent Systems
• +۱۱ مورد دیگر

Anne Håkansson is a Professor of Computer Science at KTH Royal Institute of Technology, specializing in Artificial Intelligence and intelligent systems. She also holds a full professorship at UiT The Arctic University of Norway since 2018, with a guest professorship at NTNU Norwegian University of Science and Technology (2020–2022). Her affiliations include leadership roles such as Director of the Senseable Stockholm Lab and Director of Studies for Bachelor and Master Degree projects at KTH. Education : PhD and Associate Professor (Docent) in Computer Science (Uppsala University, 2004 and 2006) Current Roles : Professor at KTH (2010–present), Full Professor at UiT (2018–present), Guest Professor at NTNU (2020–2022) Her research focuses on intelligent software systems, multi-agent systems, and cyber-physical systems (CPS) for applications in smart cities, explainable AI, and environmental impact assessment. She pioneered visualization techniques for colon cancer detection and developed tools like the EIA-system and T-uck for user-centered knowledge modeling. Recent publications emphasize autonomous CPS design, ontology integration, and context-aware systems, reflecting trends in smart urban technology and trustworthy AI in transportation. She has received the KES Award (2012) and served as Uppsala City Ambassador (2009). Scientific contributions include: Supervising over 90 master’s and bachelor’s theses Advising PhD students such as Esmiralda Moradian and Dan Wu Organizing international conferences like KES-AMSTA 2009 and SEB’12 She leads projects such as DigiCityClimate and Volatile Multiple Smart Systems (VoLM2s), emphasizing sustainability and adaptability in heterogeneous environments.

Adrian Rebmannمشاهده پروفایل

پژوهشگر

• Process Mining
• Event Data Preprocessing
• Event Abstraction
• +۳ مورد دیگر

Adrian Rebmann is a Researcher in the Process Analytics research group at the Data and Web Science Group, School of Business Informatics and Mathematics, University of Mannheim. He is supervised by Prof. Dr. Han van der Aa, focusing on advancing process mining methodologies through semantic event parsing and event abstraction. His work emphasizes preprocessing event data for meaningful process analysis, leveraging natural language processing and machine learning techniques. Research Interests: Process Mining, Event Data Preprocessing, Event Abstraction, Semantic Event Parsing, and Business Process Management. Teaching: Co-lecturer for Advanced Process Mining (since 2021) and Process Management and Analytics courses. Supervised Process Analysis Seminars and Team Projects. Service: PC Member of workshops like NLP4BPM and ZEUS (2021-2024), and reviewer for journals like Information Systems and conferences like ECIS and Modellierung. Labs/Teams: Active contributor to the Data and Web Science Group, focusing on Process Mining innovations and tool development (e.g., M-Viz for inductive miner visualization).

• Semantic Web
• Knowledge Graphs
• Data Integration
• +۳ مورد دیگر

Anastasia Dimou is an Assistant Professor in the Department of Computer Science at KU Leuven's Faculty of Engineering Technology, affiliated with the Declarative Languages and Artificial Intelligence (DTAI) research group at the De Nayer Campus in Sint-Katelijne-Waver. Her research centers on semantic web technologies and knowledge graphs, specializing in RDF mapping frameworks (RML), SHACL validation, and knowledge graph generation for machine learning applications. She develops methods for heterogeneous data integration, stream processing, and ensuring data quality through rule-based validation and inconsistency resolution. Recent publications (2019-2023) demonstrate expertise in RML ontology design, SHACL validation techniques, and systematic reviews of RDF graph generation. Key projects include 'Knowledge Graphs for Machine Learning Data Management' (2021-2023) and 'Knowledge Graphs for Data Integration' (2022-2026), focusing on scalable knowledge graph applications. Dr. Dimou serves on the Council of the Faculty of Engineering Technology, the Computer Science Department Council, and the POC Elektronica-ICT for the Faculty of Industrial Engineering Sciences, contributing to institutional governance and academic strategy. As part of DTAI, she collaborates on declarative language applications in artificial intelligence, with teaching responsibilities spanning Web AI, Data Engineering, and Knowledge Graph courses that bridge theoretical research with practical implementation.

• Databases
• Database Theory
• Complexity Theory
• +۲ مورد دیگر

Christoph Koch is an Associate Professor at Technische Universität Wien specializing in databases and artificial intelligence. His research spans database theory, complexity theory, and logic in computer science, with a focus on XML processing, query optimization, and parallel data science techniques. Lead projects: KnowledgeGraph (2020–2028), HINT (2012–2017), Weblearn (2005–2008) Developed the Lixto visual extraction system and contributed to DLV knowledge representation framework Supervised T. Lukasser's diploma thesis on XPath query processing

Pieter Colpaertمشاهده پروفایل

استادیار

• Semantic Web
• Intelligent Transportation Systems
• Knowledge Graphs
• +۶ مورد دیگر

Pieter Colpaert is an Assistant Professor at Ghent University's Faculty of Engineering and Architecture, affiliated with the Department of Electronics and Information Systems and the IMEC research unit Internet Technology and Data Science Lab. His work focuses on Semantic Web technologies, Linked Data, and data interoperability with a specialization in intelligent transportation systems and decentralized architectures. Research areas: Semantic Web, Linked Data, Decentralized Data Governance, Intelligent Transportation Systems Projects: Materializable Linked Data Web APIs (2024-2025, funded by Special Research Fund), Local Digital Twins for Smart Communities (2025-2028, European funding) Students supervised: Wout Slabbinck, Harm Delva, Ruben Dedecker, Andrei Popescu, Arthur Vercruysse, Bryan-Elliott Tam, Ieben Smessaert, Els de Vleeschauwer, Shehabeldeen Mohamed Abdelfatah, Min Oo Sitt Min Oo Labs: Internet Technology and Data Science Lab at Ghent University

• Constrained Clustering
• Explainable AI
• Constraint Programming
• +۴ مورد دیگر

Thi-Bich-Hanh DIEP-DAO serves as a Lecturer with Habilitation à Diriger des Recherches (HDR) at the University of Orleans, where she is affiliated with the LIFO laboratory (Laboratory of Informatics of Orléans). She holds significant administrative roles including THAT Lab correspondent at the Doctoral School and Member of the Board, demonstrating leadership in academic governance and doctoral education. Her research centers on constrained clustering methodologies, with evolving focus areas including explainable AI, constraint programming applications, and knowledge integration in machine learning systems. Early work established foundational approaches for constraint-based clustering using declarative frameworks, while recent publications demonstrate sophisticated extensions to mineral prospectivity mapping, social media analysis, and knowledge graph-enhanced image classification. Her research consistently bridges theoretical constraint programming with practical applications across diverse domains. Analysis of her publication trends reveals increasing emphasis on explainability and knowledge integration since 2020, with notable contributions to incremental clustering systems and graph-based knowledge representation. Her work maintains strong methodological consistency in constraint programming while expanding into new application areas, particularly evident in her 2023-2025 publications on mineral prospectivity mapping and tweet-based event characterization. As an HDR-qualified researcher, Dr. DIEP-DAO actively supervises doctoral students and contributes to academic training through her roles in the Doctoral School. Her extensive collaboration network, particularly with Christel VRAIN, has produced over 60 publications since 2010, reflecting consistent research productivity. She is integral to the LIFO laboratory's data mining and constraint programming research group, where her work on declarative clustering approaches forms a core research pillar. Current projects focus on knowledge graph integration for explainable image classification systems and constraint-based social media analysis frameworks.

• Internet of Things (IoT)
• Edge Computing
• Digital Twins
• +۶ مورد دیگر

Hui Song serves as an Assistant Professor in the Department of Computer Science at Oslo Metropolitan University's Faculty of Technology, Art and Design. Based at Pilestredet 52, Oslo, their work bridges theoretical computer science and practical IoT security applications within the Innovation, Digital Transformation and Sustainability research group. Research spans Internet of Things security , Edge Computing , and Digital Twin architectures with emphasis on trust management and secure software updates. Key interests include community-driven generative AI development, cloud-to-edge continuum security, and sustainable computing frameworks for IoT ecosystems. Recent projects address identity management in constrained devices and resilience engineering for critical infrastructure. Analysis of 2023-2025 publications reveals a consistent focus on security orchestration for IoT/CPS systems through digital twin implementations and intent-based data operations. Emerging trends show increased exploration of ethical generative AI applications and migration strategies from monolithic to microservice architectures in cloud-native environments. Active in both European research collaborations and industry-facing dissemination, Dr. Song contributes to advancing secure, sustainable computing practices through 56+ scientific publications and practical implementation frameworks developed with international partners including Rustem Dautov and Nicolas Ferry.

Louiqa Raschidمشاهده پروفایل

استاد

• Database Systems
• Data Integration
• Data Mining
• +۸ مورد دیگر

Louiqa Raschid is a Professor at the University of Maryland, holding appointments in the Robert H. Smith School of Business, the Institute for Advanced Computer Studies (UMIACS), and the Department of Computer Science. Her research bridges computer science and business information systems, focusing on data integration, management, and mining in non-traditional domains. Education: B.Tech (IIT Madras, 1980), PhD (University of Florida, 1987) Research Interests include: Data integration and mining across life sciences, health sciences, and financial ecosystems Humanitarian IT solutions for disaster management Semantic query optimization and data delivery techniques Grid and wide-area data management Article Trends reflect interdisciplinary efforts combining bioinformatics, database systems, and social behavior modeling. Key themes include ontology-driven data analysis, network-aware query optimization, and disaster relief systems development. Scientific Awards include: ACM Distinguished Member (2016) ACM Fellow (2016) IEEE Fellow (2000) DILS 2008 Best Paper Award Grants from NSF and DARPA have funded her work on biological data integration and financial cyberinfrastructure. She co-directs the Computational Linguistics and Information Processing (CLIP) Lab and collaborates with institutions worldwide.