جست‌وجوی استاد و پژوهشگر

Olaf Hartigمشاهده پروفایل

دانشیار

• Database Systems
• Semantic Web
• Data Management
• +۵ مورد دیگر

Olaf Hartig is a Senior Associate Professor at Linköping University's Department of Computer and Information Science (IDA), affiliated with the Database and Information Techniques (ADIT) division. He is also an Amazon Scholar collaborating with the Neptune graph database team. His research focuses on data management, semantic web technologies, graph databases, and distributed data systems. Hartig holds a PhD from Humboldt-Universität zu Berlin and is a Docent at Linköping University. He has received numerous awards, including the SWSA Distinguished Dissertation Award and eight best paper awards, and was selected as a Wallenberg Academy Fellow in 2024. Education: PhD in Computer Science (Humboldt-Universität zu Berlin), Docent (Linköping University). Research interests span query processing for Linked Data, federated systems, RDF and GraphQL semantics, and knowledge graph construction. He leads research groups in Database and Web Information Systems and Semantic Web Technologies at IDA. Key achievements include pioneering traversal-based query execution, developing Triple Pattern Fragments, and contributions to standards like RDF* and SPARQL*. His work has been recognized through grants, patents (e.g., on graph acceleration techniques), and leadership roles in conferences like ISWC and ESWC. Teaching: Course leader for database technology courses (TDDD12, TDDD37) and advanced topics like big data analytics and bioinformatics databases. Active in curriculum design and interdisciplinary education. Labs/Teams: Database and Web Information Systems Group, Semantic Web Research Group, Sports Analytics Group (IDA) Grants: Wallenberg Academy Fellowship, Swedish Research Council funding

Juan F. Sequedaمشاهده پروفایل

پژوهشگر

• Knowledge Graphs
• Semantic Web
• Databases
• +۴ مورد دیگر

Juan F. Sequeda is the Principal Scientist and Head of the AI Lab at data.world, with a PhD in Computer Science from the University of Texas at Austin. He co-founded Capsenta, a spin-off from his research on semantic data virtualization. His work bridges academia and industry through roles in the Property Graph Schema Working Group, LDBC Graph Query Languages task force, and W3C standards editing. Education : PhD in Computer Science (2015) from University of Texas at Austin His research focuses on Knowledge Graphs, Semantic Web, and Ontology-Based Data Integration. He develops technologies for graph data management and semantic query processing, with applications in constitutional data analysis (Constitute.org) and enterprise data virtualization (Ultrawrap, Gra.fo, G-CORE). Recent publications analyze composable graph query languages (G-CORE, 2018) and optimize SPARQL execution on relational data (Ultrawrap, 2013). Awards include NSF Graduate Fellowship (2010-2013), ISWC2014 Best Student Paper, and 2015 Institute for Applied Informatics Best Transfer Project. Scientific Awards : NSF Graduate Research Fellowship (2010-2013) 2nd Place, 2013 Semantic Web Challenge Best Student Research Paper, ISWC2014 2015 Best Transfer and Innovation Project (Institute for Applied Informatics) UT Graduate Diversity Fellowship (2008-2009) National Instruments Scholarship (2007-2008) Intel Foundation Fellowship (2007) He actively contributes to program committees (ISWC, ESWC, WWW) and workshop organization (COLD, AMW2018 General Chair). Contact: juan@data.world (work), juanfederico@gmail.com (personal).

Craig Knoblockمشاهده پروفایل

استاد پژوهشی

• Knowledge Graphs
• Semantic Web
• Data Integration
• +۷ مورد دیگر

Craig Knoblock serves as the Keston Executive Director of the Information Sciences Institute (ISI) at the University of Southern California, Vice Dean of Engineering for the Viterbi School of Engineering, and Research Professor of both Computer Science and Spatial Sciences. He also directs USC's Data Science Program and leads the Center on Knowledge Graphs as Research Director. Dr. Knoblock's research focuses on techniques for describing, acquiring, and exploiting the semantics of data. His extensive work spans source modeling, schema and ontology alignment, entity and record linkage, data cleaning and normalization, extracting data from the Web, and building comprehensive knowledge graphs. With over 300 published works in these areas, his research has significantly advanced the field of semantic data integration. His work demonstrates strong trends toward practical applications of knowledge graphs across diverse domains including cultural heritage, geospatial information systems, human trafficking detection, and sensor networks. The evolution of his publications shows a progression from foundational ontology alignment techniques to sophisticated knowledge graph applications addressing real-world challenges. Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) Fellow of the Association of Computing Machinery (ACM) Past President and Trustee of the International Joint Conference on Artificial Intelligence (IJCAI) Recipient of the 2014 Robert S. Engelmore Award Seven best paper awards for his research contributions As Executive Director of USC's Information Sciences Institute, Dr. Knoblock leads one of the world's premier research centers in computer science and information technology. His leadership extends to directing the Center on Knowledge Graphs and serving as Associate Director of the Informatics Program at USC. His educational background includes a Bachelor of Science from Syracuse University and Master's and Ph.D. degrees in Computer Science from Carnegie Mellon University.

Fei Chiangمشاهده پروفایل

دانشیار

• Data Management
• Data Quality
• Data Privacy
• +۶ مورد دیگر

Dr. Fei Chiang is an Associate Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. Her research focuses on data management , with emphasis on data quality, data privacy, information extraction , and contextual data cleaning . She has collaborated with IBM Global Services and Microsoft Research on improving data quality in enterprise systems. Key research themes include graph databases , temporal data analysis , and privacy-aware data processing Recent publications explore federated learning , SQL understanding in LLMs , and temporal graph constraints Industry collaborations with IBM Toronto Lab and Microsoft Research have led to innovations in data cleaning automation and semantic analysis. Her work bridges database theory with machine learning applications in healthcare inventory optimization and flight reliability prediction.

Felix Naumannمشاهده پروفایل

استاد

• Data Profiling
• Data Integration
• Data Quality
• +۱ مورد دیگر

Prof. Dr. Felix Naumann is Professor of Information Systems at the University of Potsdam's Digital Engineering Fakultät and Hasso Plattner Institute. His research develops foundational methods for managing data quality in complex information ecosystems. Naumann's work spans data profiling algorithms, entity resolution systems, and distributed data processing frameworks. The Metanome project exemplifies his contributions to scalable dependency discovery in large datasets. Recent publications address challenges in raw data repair, schema matching, and quality-aware query processing. His group actively develops open-source tools for data cleaning and constraint discovery. Leadership: Speaker of Research School 'Data Science and Engineering' Editor-in-Chief of JDIQ VLDB Endowment Trustee (2018-2023) Honors: Distinguished ACM Member (2021) Multiple best paper and reviewer awards IBM Faculty Awards

Fabian Panseمشاهده پروفایل

پژوهشگر

• Database Systems
• Data Quality
• Probabilistic Data Management
• +۳ مورد دیگر

Dr. Fabian Panse is a Researcher at the Database and Information Systems (DBIS) group within the Department of Informatics at the University of Hamburg. His work focuses on database systems, data quality, and probabilistic data management, with significant contributions to polyglot persistence, duplicate detection, and data simulation frameworks like SmartOpenHamburg and HADeS. Research Assistant since 2009 PhD in Computer Science Research interests center on polyglot persistence , probabilistic databases , duplicate detection , and data pollution techniques . His publications span conferences like VLDB, ICDE, and workshops on database fundamentals. He has supervised over 20 theses including Master's and Bachelor's projects on topics ranging from data synthesis to smart city applications . Key collaborations include Prof. Norbert Ritter and Dr. Wolfram Wingerath.

Mark McMahonمشاهده پروفایل

استاد

• Educational Technology
• Game-Based Learning
• Digital Literacy
• +۴ مورد دیگر

Professor Mark McMahon is a Professor of Games and Interactive Design and Director, International and Strategic, at the School of Arts and Humanities, Edith Cowan University (ECU). He holds a PhD from ECU (2005), a Master of Education (1998), and a Bachelor of Arts from The University of Western Australia (1991). His work bridges educational technology, game-based learning, and digital literacy, with a focus on serious games, problem-based learning frameworks, and cognitive assessment tools. Education: Doctor of Philosophy, Edith Cowan University, 2005 Master of Education, Edith Cowan University, 1998 Bachelor of Arts, The University of Western Australia, 1991 Research Interests: Professor McMahon’s research spans game-based learning, digital literacy, and simulation-based training. He develops tools like the eFun App to measure executive functions in students and explores immersive 3D environments for spatial cognition. His work emphasizes applying technology to enhance learning outcomes and self-regulation, with a focus on problem-based pedagogy and serious games design. Advising & Grants: Current supervisions include projects on schema complexity in problem-solving and creative media education. Completed supervisions cover topics like 3D gaming for submarine training and data visualization aesthetics. Funded projects include studies on executive functions in children and algorithmic employee matching, totaling over $500k in grants. Labs & Teams: His research involves collaborations with industry partners like CingleVue and AusIndustry, focusing on innovative educational tools and assessment systems. He leads initiatives to integrate digital literacy into curricula and supports multidisciplinary teams in project management and skill development.

Anna Farihaمشاهده پروفایل

استادیار

• Data Management
• Human-Centered Systems
• Usability Engineering
• +۴ مورد دیگر

Anna Fariha is an Assistant Professor in the Kahlert School of Computing at the University of Utah. She co-leads the Data Management Research Center for Human-centered, Efficient, and Scalable Systems. Her research focuses on enhancing data system usability, explainability, and trustworthiness through algorithmic innovations and practical implementations. She holds a Ph.D. from the Manning College of Information and Computer Sciences at the University of Massachusetts, Amherst, under Prof. Alexandra Meliou. Education: Ph.D. in Computer Science, University of Massachusetts Amherst (2021) Master's in Computer Science, University of Massachusetts Amherst (2020) Undergraduate work in Computer Science, unspecified institution Her research interests include data wrangling tools, constraint discovery, conversational AI for data science, and human-centered database systems. Recent work emphasizes automated data summary recommendations, constraint violation detection, and educational tools for data science programming. Grants & Awards: NSF CIRC: ENS/Grand: POWDER-ENS Award (2024) Stena Center Seed Grant for Fintech Data Analysis (2025) Microsoft Research Dissertation Grant (2020) SIGMOD 2022 Comprehensive Reproducibility Award Teaching: Recently taught Advanced Database Systems , Human-Centric Data Management , and Deep Learning courses at the University of Utah.

Fan Wenfeiمشاهده پروفایل

استاد

• Database systems
• Database theory
• Big data
• +۳ مورد دیگر

Professor Wenfei Fan is a Chair of Web Data Management at the University of Edinburgh since 2006. He holds adjunct roles at Huawei Edinburgh Research Laboratory and the International Research Center on Big Data at Beihang University. His research focuses on database systems, theory, big data, data quality, and constraint applications. He is a Fellow of the ACM and Royal Society of Edinburgh, and recipient of the ERC Advanced Grant (2015). His work includes foundational contributions to XML constraints, data quality management, and graph databases, with over 160 top-tier publications and 8 patents. Education: BSc and MSc from Peking University (1985, 1988); PhD from the University of Pennsylvania (1999). Professional Roles: Editor for TODS, TCS, VLDBJ, and TBD; PC Chair for PODS, CIKM, APWeb, and others. Key awards include the Roger Needham Award (2008), Yangtze River Scholar (2007), and multiple best paper awards at SIGMOD, PODS, VLDB, and ICDE. He has led over 15 grants totaling €6M and advised students who all received top conference awards. His labs and initiatives drive industry collaboration, with deployed solutions at Huawei for big data query optimization.

Wenfei Fanمشاهده پروفایل

استاد

• Database Theory
• Data Science
• Information Visualization
• +۹ مورد دیگر

Wenfei Fan holds the Personal Chair in Web Data Management at the School of Informatics, University of Edinburgh . He is affiliated with the Database Group , LFCS (Laboratory for Foundations of Computer Science), Bell Labs , and the International Research Center on Big Data at Beihang University. His research spans database theory, data science, and big data systems. PhD in Computer and Information Science, University of Pennsylvania, USA MS in Computer Science, Peking University, China BS in Computer Science, Peking University, China His research interests focus on database theory and systems , including big data, data quality, data integration, database security, distributed query processing, query languages, social networks, and XML. He has pioneered techniques for graph pattern matching, data dependencies, and scalable algorithms. Recent publications emphasize graph data management , rule-based systems , and ML-data quality integration . Key themes include incremental computation, GPU-accelerated graph cleaning, entity linking, and logic-driven ML. Articles like 'Rock' and 'MiniClean' demonstrate hybrid approaches for real-world data challenges. Fellow of the Royal Academy of Engineering (FREng), 2023 Foreign Member, Chinese Academy of Sciences (ForMemCAS), 2019 Fellow of the Royal Society (FRS), 2018 ERC Advanced Fellowship, 2015 ACM Fellow (FACM), 2012 Roger Needham Award, 2008 He leads the ERC project: Resource-Bounded Graph Query Answering and contributes to systems like GRAPE and GraphScope , which parallelize sequential algorithms and process large graphs. He mentors students and collaborators globally, with a focus on data quality and graph analytics.

• Computer Networks
• Distributed Systems
• Data Mining
• +۳ مورد دیگر

Wesley W. Chu is a Professor in the Computer Science Department at the University of California, Los Angeles . He earned his B.S.E. and M.S.E. in Electrical Engineering from the University of Michigan (1960, 1961) and Ph.D. in Electrical Engineering from Stanford University (1966). His career spans roles at IBM, Bell Laboratories, and UCLA since 1969. B.S.E. (EE), University of Michigan (1960) M.S.E. (EE), University of Michigan (1961) Ph.D. (EE), Stanford University (1966) Dr. Chu's research has evolved from foundational work in computer communications , distributed databases , and real-time systems (notably statistical multiplexing and ATM network development) to modern focus areas in intelligent information systems , data mining , and medical digital libraries . His methodologies for approximate matching and query relaxation underpin systems like CoBase , KMeD , and CoXML , with applications in logistics, healthcare, and web technologies. His contributions to data security and privacy (ISP project), XML schema transformation , and medical image systems have earned recognition through multiple best paper awards and the IEEE Technical Achievement Award (2003). He has held editorial and organizational roles at major conferences including SIGCOMM, VLDB, and ER.

Bogumiła Hnatkowskaمشاهده پروفایل

استادیار

• Software Engineering
• Analysis and Design of Information Systems
• Software Development Methodologies
• +۳ مورد دیگر

Dr. Bogumiła Hnatkowska serves as Assistant Professor at the Institute of Informatics within the Faculty of Computer Science and Management at Wrocław University of Science and Technology. Her academic career spans software engineering research and education with emphasis on model-driven approaches and quality assurance methodologies. Her research interests include: Software Engineering Analysis and Design of Information Systems Software Development Methodologies Model-Based Software Development Domain-Specific Languages Software Quality Recent publications (2021-2025) reveal concentrated research in model-driven engineering, business rules processing, and ontology integration. Key trends involve textual specification languages for use-cases, automated test generation mechanisms, and formal transformations for ontologies – demonstrating consistent application of theoretical rigor to practical software development challenges across agile and model-based contexts. Scientific Awards: No scientific awards were mentioned in the provided text Dr. Hnatkowska has served as principal investigator for multiple State Committee for Scientific Research grants including UML extensions for multimedia systems (2000), real-time systems analysis (2005), and model-driven database design (2008). Her teaching portfolio includes Software Engineering, Software System Development, and Advanced Programming Techniques courses where she supervises team projects providing students with hands-on development experience. She actively participates in partner programs including Visual Paradigm's Academic Training Partner Program (providing UML/BPMN/agile tools) and IBM Academic Initiative, supporting her research in software engineering methodologies and educational tool development.

Lei Chenمشاهده پروفایل

استاد

• Data-Driven Machine Learning
• Crowdsourcing-Based Data Processing
• Uncertain and Probabilistic Databases
• +۳ مورد دیگر

Lei Chen is a Chair Professor and Director of HKUST Big Data Institute at the Hong Kong University of Science and Technology , where he has served since 2005. His research spans data-driven machine learning , crowdsourcing systems , and uncertain database processing , with notable contributions in privacy-preserving spatial queries and graph neural networks . Ph.D. in Computer Science, University of Waterloo (2004) MS in Computer Science, Asian Institute of Technology (1997) BS in Computer Science, Tianjin University (1994) His work focuses on: Spatial Crowdsourcing - Efficient task assignment and privacy frameworks Graph Processing - Novel indexing for heterogeneous networks Explainable AI - Human-centric model interpretation techniques Uncertain Data - Probabilistic query processing with crowdsourcing Recent publications cluster around secure data federation , distributed graph training , and privacy-preserving mobility systems , reflecting his leadership in ACM and IEEE communities. Students include 15 active Ph.D. candidates and 20+ graduated researchers now at institutions like BeiHang University and Huawei Noah's Ark Lab . Awards: ACM Fellow (2024), VLDB Best Paper (2022), SIGMOD Test-of-Time Award (2015).

• Data Governance
• Semantic Modeling
• Knowledge Graphs
• +۵ مورد دیگر

Zaiwen Feng is a researcher actively contributing to data governance, semantic modeling, and causal inference. His work focuses on knowledge graphs, graph-based methods, and service-oriented architectures through collaborations with institutions like the University of Queensland and universities in China. Research Focus : Graph Differential Dependencies, Entity Resolution, Causal Effect Estimation, and Ontology Alignment Methodologies : Machine Learning, Variational Autoencoders, Prompt Engineering, and Semantic Retrieval Application Areas : Biomedical Data, Property Graph Recommendation, and Process Model Repositories Key trends in his publications include automated semantic modeling , neural approaches for entity resolution , and causal inference with graph structures . He frequently collaborates with researchers like Keqing He, Wolfgang Mayer, and Selasi Kwashie across conferences such as HPCC, BIBM, and WISE.

Oscar Romero Moralمشاهده پروفایل

استاد

• Data Management
• Data Science
• Data Governance
• +۷ مورد دیگر

Oscar Romero Moral is a Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Department of Services and Information Systems Engineering at the Barcelona School of Informatics (FIB). He leads research in the inSSIDE, inLab FIB, and DTIM groups, focusing on data management, data science, and big data technologies. His work emphasizes knowledge graphs, data governance, and machine learning integration with data systems. Affiliations: UPC, inSSIDE, inLab FIB, DTIM Group Research Interests: Data Management, Data Engineering, Big Data, Knowledge Graphs, Data Governance, Machine Learning Integration He has authored over 276 academic contributions, including peer-reviewed articles on federated healthcare data systems, GPU-accelerated workflows, and graph-driven data integration. His recent work addresses challenges in heterogeneous computing, automated data governance, and scalable data architectures. Romero has served on the program committees of major conferences like VLDB, ICDE, and EDBT, and led competitive research projects in data systems and analytics. He collaborates extensively with industry partners and academic institutions, driving innovations in distributed data management and edge computing.