جستجوی استاد راهنمای دانشگاهی

Professor Christian Bizer is a leading figure in web-based systems and data integration at the University of Mannheim , where he chairs Information Systems V: Web-based Systems . His research focuses on integrating data from multiple sources using large language models and LLM-based agents, with applications in product data extraction and DBpedia knowledge graph construction. He co-founded the DBpedia project and initiated the WebDataCommons initiative. Current research areas: Entity matching, schema matching, table annotation, information extraction, data discovery Key projects: WebMall benchmark, WInte.r integration framework, Schema.org analysis His work applies to e-commerce data integration and knowledge graph construction, with empirical studies on schema.org adoption. He supervises PhD students including Alexander Brinkmann and Ralph Peeters. Scientific Awards: Best Paper at iiWAS 2024 SWSA Ten-Year Award at ISWC 2019 Yahoo FREP Award 2015 Semantic Web Challenge winners Teaching includes courses on web data integration, web mining, large language models, and data mining for master's programs. He leads the DWS PhD colloquium and team projects on LLM agents for data integration.

• Data Integration
• Entity Matching
• Schema Matching
• +۵ مورد دیگر

Amol Deshpande is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, College of Engineering. With over 160 publications spanning from 2000 to 2025, his research has significantly impacted the database systems community. His work bridges theoretical foundations with practical systems, evidenced by numerous publications in top-tier venues including SIGMOD, VLDB, and ICDE. Professor Deshpande's research focuses on database systems, with particular expertise in graph databases, data management, probabilistic databases, query optimization, and data provenance. His work addresses fundamental challenges in managing complex data, including efficient graph analytics, dataset versioning, streaming data processing, and privacy-preserving data management. Recent research directions include entity-relationship abstractions beyond traditional relations, standalone catalog engines for large data systems, and graph theoretical approaches to dataset versioning. His publication trends show a consistent focus on evolving database technologies, with early work on probabilistic databases and query optimization, transitioning to graph analytics and data provenance, and more recently addressing modern challenges in data cataloging, privacy-first data management, and serverless stream processing. His research spans both theoretical contributions (e.g., approximation algorithms for stochastic optimization) and practical systems building (e.g., RStore, TreeCat). Professor Deshpande has mentored numerous PhD students who have become active researchers in the database community, including Hui Miao, Souvik Bhattacherjee, and Konstantinos Xirogiannopoulos. His collaborative work spans across institutions, with frequent collaborations with researchers from MIT, University of Maryland, and other leading institutions. His research has been supported by major funding agencies and has influenced both academic research and industry practices in data management. The evolution of his work reflects the changing landscape of data management, from traditional relational systems to modern graph and streaming data challenges.

• Database Systems
• Graph Databases
• Data Management
• +۷ مورد دیگر

Riccardo Tommasini is an Associate Professor at INSA Lyon , a leading engineering institution in France. He leads the Stream Processing and Knowledge Graphs research within the DB Team at LIRIS laboratory under Professor Angela Bonifati. His academic journey began with a PhD in Computer Science from Politecnico di Milano under Emanuele Della Valle, with a dissertation titled Velocity on the Web to be published as a Springer book. Research Interests : Advancing stream processing for real-time data systems Extending knowledge graphs with dynamic data Designing graph databases for big data applications Creating query languages for heterogeneous data environments Building data engineering pipelines with Apache Airflow Enabling big graph processing in distributed settings Key Contributions : Developed Zodiac framework for Datalog reasoning under rule amendments (ICDE 2025) Co-authored foundational Streaming Linked Data book with Springer (2023) Created RSP4J API for RDF stream processing (ESWC 2021) Designed challenge-based learning curriculum for Data Engineering courses Scientific Recognition : Received ANR JCJC grant for POLYFLOW project (2024) Awarded Best Resource at ESWC 2021 Managed industrial collaborations with Neo4j, InfluxData, and Confluent Advising & Teaching : Supervises Mohamed Ragab (PhD candidate at University of Tartu) Course Leadership : Foundational Data Engineering course at INSA Lyon and University of Tartu Structured around Apache Airflow , Docker, and graph databases

• Stream Processing
• Knowledge Graphs
• Query Languages
• +۳ مورد دیگر

Daniel Hernández is a Postdoctoral Researcher at the Institute for Artificial Intelligence (KI) under the Cluster of Excellence IntCDC at the University of Stuttgart. He is part of the Analytic Computing group within the Institute for Parallel and Distributed Systems (IPVS). His work focuses on Semantic Web technologies , particularly SPARQL , RDF , and knowledge graph applications in interdisciplinary design workflows . Research Trends : His publications (2015–2025) emphasize semantic query processing , provenance computation , and interoperability between architectural data and knowledge graphs . Key innovations include the eSPARQL language for epistemic queries, NPCS for native provenance in SPARQL, and BHoM to bhOWL for integrating building data with ontologies. Teaching & Collaborations : He has held teaching roles at the University of Stuttgart ( Human-Computer Interaction with Knowledge Graphs ), University of Aalborg ( Group Supervisor ), and University of Chile ( Lecturer for The Web of Data ). Collaborations span institutions like Buro Happold , TU Wien , and INRIA , with publications in journals like Proceedings of the VLDB Endowment and conferences such as WWW and ISWC .

• Semantic Web
• SPARQL
• RDF
• +۳ 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).

• Knowledge Graphs
• Semantic Web
• Databases
• +۴ 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.

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

Eric Leclercq is a researcher at the University of Burgundy, affiliated with the LE2I Lab in Dijon, France. His work spans database systems, social network analysis, and biomedical data integration. He has contributed extensively to polystore systems, tensor decompositions, and category theory applications in data modeling. Fields of Interest : Database Systems, Data Mining, Social Network Analysis, Big Data Analytics, Semantic Web Leclercq's recent research focuses on formal frameworks for data lakes using category theory, multi-level tensor decomposition for social network stratification, and schema migration in multi-model systems. He has published in venues like CAiSE, IDEAS, and RCIS. His collaborations include Annabelle Gillet, Marinette Savonnet, and Nadine Cullot. Notable works include Lambda+ architecture for data processing, polarization analysis in social networks, and tools for tweet collection and biomedical data integration.

• Database Systems
• Data Mining
• Social Network Analysis
• +۳ مورد دیگر

Sebastián Ferrada is an Assistant Professor at the Data & Artificial Intelligence Initiative of Universidad de Chile. He also serves as Young Researcher at the Institute for Foundational Research on Data (IMFD) and Collaborating Researcher at the National Center for Artificial Intelligence Research (CENIA). His research focuses on Knowledge Graphs, with special emphasis on extraction, management, and applications for querying, browsing, and AI systems. His academic background includes: PhD in Computer Science (2021), Universidad de Chile MSc in Computer Science (2017), Universidad de Chile BEng in Computer Science (2017), Universidad de Chile Sebastián's research explores several key areas: Multimedia Databases with applications to Wikimedia Commons images Graph Databases and Knowledge Graphs construction Federated Data Management across heterogeneous RDF sources SPARQL query extensions for similarity-based operations Graph data management and compression techniques His recent publications demonstrate strong trends in knowledge graph construction, similarity-based querying, and efficient graph data management. These works combine theoretical advancements with practical implementations in real-world systems like IMGpedia and MillenniumDB. Scientific achievements include: Best Paper Award at CoopIS 2023 Best Demonstration Award runner-up at SIGMOD/PODS 2024 Best Student Paper (Resources Track) and Best Poster at ISWC 2017 First prize in CLEI 2017 for his Master's thesis Sebastián currently leads the Fondecyt project on graph data management and contributes to the U-Inicia project on AI processes in graph databases. He serves on the editorial board of Transactions on Graph Data and Knowledge.

• Knowledge Graphs
• Graph Databases
• Multimedia Databases
• +۳ مورد دیگر

Prof. Sören Auer is the Director of the German National Library of Science and Technology (TIB) and Professor of Data Science and Digital Libraries at Leibniz Universität Hannover's Faculty of Electrical Engineering and Computer Science . With academic positions at Dresden, Yekaterinburg, Leipzig, Pennsylvania, Bonn, and Fraunhofer Society, his career focuses on semantic technologies , knowledge engineering , and Artificial Intelligence Research Data Management Open Science Knowledge Graphs . He leads the Open Research Knowledge Graph (ORKG) initiative and co-founded DBpedia and eccenca.com . His research spans data science , AI-driven knowledge representation , and digital library systems . He has supervised numerous PhD theses and final-year theses while offering courses on Knowledge Engineering Semantic Web Technologies Data Integration Scientific Data Management . The technical focus includes semantic data interlinking , neuro-symbolic AI , and contextual metadata frameworks . As a recipient of prestigious awards including ERC Consolidator Grant SWSA Ten-Year Award ESWC 7-Year Best Paper Award OpenCourseware Innovation Award , Prof. Auer has led major projects like BigDataEurope and contributes to standards in W3C , NFDI , and EOSC . His recent publications demonstrate advancements in ontology alignment , LLM-driven schema discovery , and hybrid AI systems for scholarly knowledge organization.

• Semantic Technologies
• Knowledge Engineering
• Data Science
• +۷ مورد دیگر

Prof. Magnus Pfeffer is a faculty member at Hochschule der Medien Stuttgart, affiliated with the Faculty of Information and Communication and the Department of Information Science. He holds the academic rank of Professor and is actively engaged in research and teaching in information science, digital libraries, and semantic technologies. His research interests include: Semantic Web and Knowledge Graphs Metadata Management and Interoperability Knowledge Organization and Classification Systems Information Retrieval in Heterogeneous Environments Digital Humanities, particularly Japanese Visual Media (Anime, Manga, Otaku Culture) Integration of Fan-Created and Community Data His recent publications (2022–2018) reveal a strong focus on the Japanese Visual Media Graph project, which builds domain-specific knowledge graphs by integrating data from enthusiast communities using RDF, ontologies, and semantic web standards. This work bridges library science with digital humanities and addresses challenges in licensing, data harmonization, and research utility of crowdsourced metadata. Earlier works focus on automated indexing, classification system mapping, and metadata enrichment in library contexts. Scientific contributions include: Development of ontologies for anime characters, visual novels, and fan databases Legal and technical integration of open-licensed community databases Innovations in retro-classification and metadata provenance Curriculum development in information science, especially IT competencies and pandemic-era teaching He has collaborated with researchers such as Martin Roth, Senan Kiryakos, Heidrun Wiesenmüller, and Kai Eckert. While no formal list of students is provided, his supervisory role is implied through research projects and student co-authorships. He has contributed to major conferences including Dublin Core, TPDL, ECDL, and Digital Humanities. His ORCID (2-2141-6721) and GND (102572836X) identifiers confirm his scholarly presence. No awards or grants are explicitly mentioned in the provided text.

• Semantic Web
• Knowledge Organization
• Metadata Management
• +۱۲ مورد دیگر

Marcelo Arenas is a Professor at the Department of Computer Science and the Institute for Mathematical and Computational Engineering at the Pontifical Catholic University of Chile. He is a Fellow of the Association for Computing Machinery (ACM), former director of the Millennium Institute for Foundational Research on Data, and co-founder of the Center for Semantic Web Research. His Ph.D. in Computer Science was obtained from the University of Toronto in 2005. Research interests include data management , applications of logic in computer science , and Semantic Web technologies. He has published extensively on topics such as SPARQL query complexity , graph database systems , and incomplete database theory , with notable works like MillenniumDB and Foundations of Data Exchange . Scientific accolades: 2016 SWSA Ten-Year Award for "Semantics and Complexity of SPARQL" IBM Ph.D. Fellowship (2004) Nine Best Paper Awards across PODS, ISWC, ICDT, ESWC, WWW, and NeurIPS His work on approximate counting algorithms (e.g., FPRAS for #NFA) and explainable AI frameworks has influenced database theory, while serving on program committees for ICDT 2015, ISWC 2015, and PODS 2018 demonstrates leadership in the field. Current projects focus on probabilistic explanations for decision trees and temporal regular path queries in knowledge graphs.

• Data Management
• Semantic Web
• Database Theory
• +۳ مورد دیگر

Prof. Dr. Katja Zeume serves as Professor and Dean of the Faculty of Computer Science and Communication at the Westphalian University of Applied Sciences in Gelsenkirchen, Germany. Her academic leadership spans theoretical and applied database systems research with emphasis on semi-structured data management. Her educational foundation includes a PhD from the University of Bayreuth (2015), where she completed her thesis "Foundations of Regular Languages for Processing RDF and XML" under the name Katja Losemann. Dr. Zeume's research centers on the theoretical underpinnings of database query languages, particularly investigating regular expressions in SPARQL for RDF and XML data models. Her work bridges formal language theory with practical database applications, addressing challenges in query complexity, data federation, and schema design for unstructured information. Key contributions include foundational analyses of deterministic regular expressions and their computational properties in semantic web contexts. Her publication trajectory reveals consistent focus on theoretical database foundations, evolving from XML schema research toward SPARQL complexity analysis. The body of work demonstrates deep integration of automata theory, complexity analysis, and practical query language design for modern data ecosystems. As Dean of the Faculty of Computer Science and Communication, she oversees academic strategy while maintaining active research engagement. Her teaching portfolio includes core database systems courses covering traditional SQL, NoSQL architectures, web-scale data management, and theoretical database principles.

• Databases
• Information Systems
• Semi-structured Data
• +۵ مورد دیگر

Ştefania-Gabriela Dumbravă is an Associate Professor at Institut Polytechnique de Paris (ENSIIE/Samovar Laboratory). Her research focuses on formal methods for graph database algorithms , including schema design, query processing, and verification. She contributes to international standards through co-chairing VLDB 2026 Demonstration Track and organizing workshops like VLDB Summer School 2025 . Dumbravă serves on editorial boards (e.g., ACM Transactions on Database Systems ) and program committees for top-tier conferences ( SIGMOD , ICDE ). Research Interests include formal verification of graph databases , property graph schema constraints , certified inference engines , and distributed graph systems . She integrates proof assistants (Coq, Isabelle) with practical database development, emphasizing reliability and mechanical correctness . Her work spans theoretical foundations (e.g., category theory , type theory ) and real-world applications (e.g., transit accessibility analysis , SDN controllers ). Selected Scientific Awards : EASST Best Software Science Paper Award (ICGT 2025) SIGMOD Best Paper Award (2023) VLDB Best Regular Paper Runner-Up (2022) D4Gen Hackathon 3rd Prize Teaching includes Graph Databases , Functional Programming , and Formal Languages at ENSIIE, with additional roles at Université Paris-Saclay, ENS Lyon, and Université Rennes 1. She supervises research internships and participates in gender parity initiatives (e.g., Paris-Saclay Women & Science Mentorship Program).

• Formal Methods
• Graph Databases
• Property Graph Schema
• +۵ مورد دیگر

Dr. Stefan Gugler is a Postdoctoral Researcher at the Technical University of Berlin (TU Berlin), affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the Machine Learning Group led by Prof. Dr. Klaus-Robert Müller. His research focuses on integrating machine learning methods, such as diffusion models and Gaussian processes, into theoretical chemistry to address challenges like molecular similarity, reaction networks, and catalyst design. He holds a PhD in quantum chemistry from ETH Zurich (2023), where he developed machine learning approaches for dispersion interactions, and a Master’s degree in computational inorganic chemistry from MIT (2018). His work has been recognized with an MSDE Award for his thesis on transition metal complexes. Gugler’s contributions include open-source software tools like qcscine/readuct and qcscine/puffin , advancing computational chemistry and machine learning workflows. Education: PhD in Quantum Chemistry, ETH Zurich (2023) Master’s in Computational Inorganic Chemistry, MIT (2018) Interdisciplinary Sciences (BSc/MSc), ETH Zurich (2016–2018) Research Interests: Molecular similarity and chemical reaction networks Diffusion models and generative AI for chemistry Machine learning in quantum chemistry and materials science Explainable AI (XAI) for scientific applications Awards: MSDE Award (2018), MIT Labs/Teams: Active contributor to BIFOLD and the qcscine open-source project, focusing on machine learning-driven computational chemistry tools.

• Computational Quantum Chemistry
• Materials Science
• Machine Learning
• +۳ مورد دیگر

Ralf Lämmel is Professor and Head of the Software Languages group at the University of Koblenz. His research spans software language engineering, model-driven development, and semantic web technologies. He specializes in API analysis, data validation (SHACL constraints), and variability management for software systems. Recent work focuses on AI-assisted workflows for archaeology and trust analysis in large language models. Publications demonstrate growing interest in knowledge graph validation and semantic web applications. He has developed tools like the Virtual Platform for software variability and ProGS for property graph validation. Service includes program committee memberships for software engineering conferences and editorial roles. He leads research groups exploring API evolution and RDF validation techniques.

• Software Language Engineering
• Model-Driven Development
• Semantic Web
• +۲ مورد دیگر