جست‌وجوی استاد و پژوهشگر

Arka Ghoshمشاهده پروفایل

پژوهشگر

• Heterogeneous data integration
• Ontology-based data access (OBDA)
• GeoBigdata (Spatial and Temporal Raster) integration and management
• +۷ مورد دیگر

Arka Ghosh is a doctoral student and knowledge engineer at Umeå University , Department of Computing Science. He is affiliated with the AI for Data Management research group led by Prof. Dr. Diego Calvanese. Research focus: Virtual Knowledge Graphs (VKGs), heterogeneous data integration, and management of geospatial/raster data Email: arka.ghosh@umu.se Research Areas: Ontology-based data access (OBDA), semantic querying with SPARQL and NLP, Description Logic formalisms, raster/vector data processing, and temporal-spatial data modeling. Publications: Recent work presented at RuleML+RR 2024 and 2023 conferences includes extending VKG systems to handle large-scale satellite raster data and reformulating queries across heterogeneous data sources.

Xiang Chenمشاهده پروفایل

دانشیار

• Empirical Software Engineering
• Mining Software Repositories
• Software Testing and Maintenance
• +۵ مورد دیگر

Xiang Chen is an Associate Professor at the Department of Software Engineering, School of Artificial Intelligence and Computer Science, Nantong University, China. He received his B.Sc. degree from Xi'an Jiaotong University in 2002 and his M.Sc. and Ph.D. degrees in computer software and theory from Nanjing University in 2008 and 2011 respectively. He is an editorial board member of Information and Software Technology and serves as a program committee member for prestigious conferences including FSE 2026 and ASE 2025. Chen is also a senior member of the China Computer Federation (CCF) and active in various academic committees. Chen's research focuses on empirical software engineering, mining software repositories, and software testing and maintenance, with particular emphasis on applying AI techniques to software engineering problems. His work spans large language models for software engineering, security vulnerability analysis, code change representation, and regression testing. He has published over 110 papers in top-tier journals and conferences including IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology. His recent publications demonstrate a strong trend toward integrating AI techniques, particularly large language models, with traditional software engineering practices. The research spans code generation evaluation, deep learning framework testing, vulnerability detection, and automated program repair, showing a consistent focus on improving software quality through innovative testing and analysis techniques. ACM SIGSOFT Distinguished Paper Award (ICSE 2021) ACM SIGSOFT Distinguished Paper Award (ICPC 2023) Top 1% CNKI Highly Cited Scholar (2024) Top 2% Scientist by Stanford University (2023-2025) NASAC 2019 Prototype Competition First Prize Chen has successfully advised numerous graduate and undergraduate students who have gone on to prestigious institutions including Nanjing University, Tsinghua University, and Zhejiang University. Many of his students have won national programming competitions and received scholarships. His research group, smartSE, actively works on projects funded by the Natural Science Foundation of China and various provincial research programs. Chen also serves as a reviewer for top journals including IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology.

Gerhard Weikumمشاهده پروفایل

استاد

• Databases
• Information Systems
• Knowledge Graphs
• +۷ مورد دیگر

Gerhard Weikum is a leading professor and researcher in the field of databases and information systems at the Max Planck Institute for Informatics, part of the Max Planck Society in Saarbrücken, Germany. He heads the Databases and Information Systems department, where his work bridges foundational database research with cutting-edge applications in knowledge graphs, information retrieval, and natural language processing. His research interests include knowledge graph construction and curation, data integration, question answering, conversational search, and the integration of large language models with structured knowledge. He has made significant contributions to the development of methods for creating comprehensive and reliable knowledge bases from web and text sources, with a focus on scalability, quality, and usability. The recent publications highlight a strong trend towards hybrid AI systems that combine the strengths of knowledge graphs and large language models, particularly in the domains of conversational search, recommendation systems, and complex question answering over heterogeneous data. His work emphasizes efficiency, robustness, and faithfulness in AI-generated responses. ACM Web Conference WSDM CIKM Advances in Information Retrieval Journal of Web Semantics IEEE TKDE EMNLP He has advised numerous PhD students and researchers who have gone on to make their own contributions in areas such as knowledge base completion, data curation, and question answering. His scientific work is highly collaborative, involving researchers from various institutions and industrial partners. Gerhard Weikum is a central figure in the database and semantic web research communities, with a sustained record of high-impact publications and leadership in advancing the field of information systems.

Craig Macdonaldمشاهده پروفایل

استاد

• Information Retrieval
• Web Information Retrieval
• Enterprise Information Retrieval
• +۴ مورد دیگر

Craig Macdonald is a Professor of Information Retrieval in the School of Computing Science at the University of Glasgow. His research focuses on information retrieval across diverse domains, including web, enterprise systems, social media, and smart cities. He has led projects like the SMART FP7 initiative, developing a search engine integrating sensor and social media data. Macdonald is a core developer of the Terrier IR platform and has contributed to TREC tracks such as Blog, Microblog, and Web. His PhD thesis addressed expertise search in enterprise environments, leading to practical systems like the SICSA expert search tool. He has organized conferences including CIKM 2011 and contributed to BCS IRSG and ACM SIGIR. His work spans theoretical advancements and applied systems, emphasizing reproducibility and open-source tools like PyTerrier. Education: BSc (1st Class Honours) in Computing Science (2004), PhD in Information Retrieval (completed). Research Interests: Expert search, blog/web/enterprise IR, sensor data integration, and recommendation systems. Notable projects include the SMART search engine for smart cities and contributions to TREC's Web track since 2014. He has also pioneered methods in query expansion, diversification, and sensitivity review systems. Publications (selected): Over 150+ papers in top venues like SIGIR, CIKM, and ECIR, focusing on retrieval models, recommendation systems, and evaluation frameworks. His work on RAG pipelines, pruning techniques, and contextual recommendation has driven practical advancements in AI-driven IR.