جستجوی استاد راهنمای دانشگاهی

Rainer Gemulla is a Professor of Practical Computer Science I: Data Analytics at the University of Mannheim, heading the Data and Web Science Group within the School of Business Informatics and Mathematics. He has been a W3-Professor at the University since 2014, following positions as a senior researcher at Max-Planck-Institut für Informatik (2010-2014) and postdoctoral researcher at IBM Almaden Research Center (2008-2010). His research focuses on machine learning with structured and semi-structured data, particularly knowledge graphs, and developing efficient systems for data-intensive processing. Professor Gemulla's research spans multiple areas including machine learning with structured data (relational data), machine learning with semi-structured data (multi-relational graphs), combining these approaches with unstructured knowledge (text), and developing efficient, scalable methods for data-intensive processing. His work bridges theoretical foundations with practical implementations, as evidenced by numerous open-source software projects including LibKGE, DistKGE, and AdaPM. His recent publications show a strong trend toward knowledge graph embeddings, parameter server architectures, and efficient training methods. The research demonstrates increasing focus on scalability challenges in graph learning, with particular attention to hyperparameter optimization, dynamic resource allocation, and benchmarking methodologies. His work consistently addresses the practical challenges of implementing machine learning systems at scale. Distinguished Reviewer Award at SIGMOD, 2025 Distinguished PC Member Award at EDBT, 2023 Outstanding Reviewer Award at NeurIPS, 2021 Junior-Fellow of the Gesellschaft für Informatik (GI), 2013 IBM's 2011 Pat Goldberg Memorial best paper award Best paper of NIPS 2011 Biglearn workshop Professor Gemulla actively mentors PhD students and has supervised numerous successful doctoral candidates. His leadership extends to administrative roles including Head of examination board for MSc Business Informatics since 2017, and previously serving as Study dean of the WIM faculty (2016-2019) and CIO of University of Mannheim (2022-2024). His research is supported by grants including AWS in Education Research Grant Award (2013) and Google Focused Research Award (2011). The Data and Web Science Group develops multiple open-source software projects including LibKGE (knowledge graph embedding library), DistKGE (multi-GPU training), AdaPM (adaptive parameter manager), Lapse (parameter server), and various tools for information extraction and sequence mining. The group maintains active collaborations with industry partners and academic institutions worldwide, particularly in the areas of knowledge graph research and scalable machine learning systems.

• Machine Learning
• Data Processing
• Knowledge Graphs
• +۳ مورد دیگر

Dr. Sebastian Hellmann is a senior researcher at the Institute of Computer Science , University of Leipzig , affiliated with the Business Information Systems department. He leads the Knowledge Integration and Language Technologies (KILT) Competence Center at InfAI and serves as executive director and board member of the DBpedia Association . His work spans semantic technologies, linked data, and knowledge graphs, with significant contributions to data curation, FAIR data principles, and natural language processing. PhD in Computer Science (2014) at University of Leipzig Contributed to open-source projects like DBpedia, NLP2RDF, and OWLG Author of over 80 peer-reviewed publications (h-index 21, 4300+ citations) Sebastian's research focuses on Knowledge Graphs , Ontology Management , and Data Interoperability , as evidenced by his publications and projects. Recent work includes ClassRank for knowledge graph summarization, DBpedia Databus for dataset management, and the Open Energy Ontology for energy systems analysis. His projects often bridge semantic web technologies with practical applications in data quality, integration, and user-centric tools. Selected publications highlight his expertise in Linked Data , Ontology Archiving , and Agile Knowledge Engineering . He actively participates in academic-industry collaborations through EU H2020 projects like ALIGNED and FREME , as well as the Smart Data Web initiative. His work with DBpedia, Wikidata, and the Semantic Web community underscores his commitment to advancing machine-readable knowledge representation.

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

Min Peng is a Professor at Wuhan University's School of Computer Science. His research focuses on artificial intelligence, machine learning, natural language processing, and knowledge graphs. He has collaborated extensively with institutions like Hefei University of Technology and the University of Chinese Academy of Sciences. His work bridges theoretical advancements in AI with practical applications in finance, social media analysis, and network optimization. Recent contributions include neural-symbolic reasoning frameworks, contrastive learning for knowledge graphs, and financial benchmarking with large language models. Research interests emphasize scalable machine learning models for complex reasoning tasks, explainable AI, and domain-specific applications in finance and social networks. Over 100 publications span venues like WWW, ACL, and NeurIPS, highlighting interdisciplinary impact. Notable projects include SymAgent (neural-symbolic agent frameworks), PIXIU (financial LLM benchmark), and DTC (commonsense machine comprehension). Key technical trends include integrating large language models with structured data, temporal knowledge graph reasoning, and transfer learning across domains. His work often addresses real-world challenges in data efficiency, interpretability, and cross-domain scalability. Current efforts explore financial LLMs, agent-based reasoning systems, and multimodal applications. While no specific grants or awards are listed in the provided data, his prolific publication record indicates sustained research excellence. Collaboration networks include teams in computer science, electrical engineering, and finance disciplines.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Dr. Martin L. Kersten is a leading figure in database systems research at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands. With over three decades of contributions, his work focuses on column-oriented database architectures, scientific data management, and query optimization. Key Research Areas: Database systems, big data processing, query performance analysis, data-intensive scientific applications Projects: MonetDB, SciQL, TELEIOS, ExaNeSt His recent publications emphasize in-database machine learning , query log mining , and exascale computing . He pioneered database cracking and intermediate recycling techniques to enhance query processing efficiency. 2014 SIGMOD Edgar F. Codd Innovations Award for groundbreaking contributions to database technology Collaborations span institutions like ICDE , VLD , and EuroSys workshops. His work bridges theoretical advancements with practical implementations for scientific and industrial applications.

• Database Systems
• Data Management
• Scientific Data Processing
• +۵ مورد دیگر

Tanja Blascheck is a PostDoc Researcher and Margarete von Wrangell Fellow at the Institute for Visualization and Interactive Systems (VIS) at the University of Stuttgart. Her work focuses on visual analytics , eye tracking , and microvisualizations for smartwatches and other wearable devices.

• Information visualization
• Visual analytics
• Eye tracking
• +۱۰ مورد دیگر

Stefan Decker is a full University Professor (Universitätsprofessor) at RWTH Aachen University, Germany, where he heads the Chair of Information Systems and Databases (Informatik 5) within the Faculty of Mathematics, Computer Science and Natural Sciences. He is actively involved in teaching, research, and the supervision of numerous ongoing and completed doctoral, master’s, and bachelor theses. Education & Academic Background Doctorate (Dr. rer. pol.) – field of Information Systems or related (exact institution/year not stated in text). Appointed as University Professor and Chair of Information Systems and Databases at RWTH Aachen University. Research Interests Prof. Decker’s work lies at the intersection of databases, knowledge graphs, semantic web technologies, data science, and cybersecurity . He investigates architectures and algorithms for large-scale, privacy-preserving, decentralized data analytics , develops ontology-driven information systems , and explores the use of large language models (LLMs) for educational technology, anomaly detection, and incident-response playbooks. Additional focal areas include smart energy systems, mixed-reality learning environments, FAIR data principles, and federated machine learning . Scientific Contributions & Trends His recent publications (2022-2025) demonstrate a clear trend toward explainable AI, LLM-enhanced systems, secure data spaces, and semantic interoperability . Key contributions include novel anomaly-detection frameworks for encrypted power-grid communications, knowledge-graph-driven chatbots for higher-education support, and methodological advances in decentralized analytics and FAIR data sharing. These works are disseminated in top-tier venues such as AAAI, IEEE ISGT Europe, ESWC, IDEAL, and various Springer LNCS and IEEE Transactions. Supervision & Grants Doctoral Theses Advised: A. T. Neumann – “Chatbots as professional companions in large-scale community information systems” (2024) S. M. Welten – “Methods for practical data sharing and decentralized analytics” (2025) Master’s Theses Co-Advised: A. R. Küsters – “Object-centric process constraints using variable bindings” (2025) Additionally supervising more than 30 ongoing bachelor, master, and doctoral projects covering topics such as LLM-driven cybersecurity playbooks, knowledge-graph construction for German law, privacy-preserving analytics in smart grids, and mixed-reality learning agents. Principal investigator or senior researcher in large collaborative projects including NFDI4DS, WestAI, champI4.0ns and several EU/national initiatives on sovereign data spaces and AI services. Labs & Teams Prof. Decker leads the Information Systems & Databases (DBIS) Research Group . The group operates well-equipped laboratories for knowledge-graph engineering, mixed-reality applications, privacy-enhancing technologies, and secure distributed analytics . Current team size exceeds 30 researchers including PhD candidates, postdocs, and scientific programmers, supported by national and EU funding streams.

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

Catia Pesquita is an Associate Professor in Computer Science at the Faculty of Sciences of the University of Lisbon , where she is also a Senior Researcher at LASIGE and leads the Health and Biomedical Informatics Research Line . With a multidisciplinary background in Biology and Computer Science, she focuses on Artificial Intelligence and Data Science applications in life and health sciences . Her research spans Semantic Web , Biomedical Ontologies , Knowledge Graphs , and Explainable AI , with significant contributions to ontology matching and semantic similarity . Education: PhD in Computer Science - Bioinformatics (2012) MSc in Bioinformatics (2008) Degree in Cell Biology and Biotechnology (2005) Current Projects: KATY (2021-2024): AI-Empowered Personalized Medicine for cancer treatments. BRAINTEASER (2021-2024): AI for ALS and MS disease progression models. Research Outputs: Developed tools like AgreementMakerLight (AML) , KGsim-benchmark , and the Epidemiology Ontology . Over 133 publications with significant citations (32,909 reads, 3,889 citations). Teaching: Lectures advanced topics in Databases , Data Integration , Bioinformatics , and Big Data . Advocacy: Vice-president of Biodata.pt , promoting biological data valorization in Portugal. Actively involved in initiatives to promote computer science careers to young women .

• Semantic Web
• Biomedical Ontologies
• Knowledge Graphs
• +۷ مورد دیگر

Georg Rehm is an Honorary Professor for Computational Linguistics and Language Technology at Humboldt University Berlin and a Principal Researcher at the German Research Center for Artificial Intelligence (DFKI) in Berlin, where he serves as Deputy Director of the DFKI Lab Berlin. He is also the Head of the German-Austrian Chapter of the World Wide Web Consortium (W3C) based at DFKI Berlin. Rehm has over 300 scientific publications and extensive experience in leading major research projects in computational linguistics and language technology. His research focuses on Natural Language Processing, Computational Linguistics, and Artificial Intelligence, with specific interests in multilingual language technologies, semantic web, digital humanities, and language data spaces. He has led numerous significant projects including European Language Grid, OpenGPT-X, and NFDI4DataScience. Rehm is particularly active in initiatives promoting digital language equality in Europe by 2030. Rehm's recent work demonstrates a strong focus on large language models, scholarly document processing, scientific knowledge representation, and climate-related fact-checking systems. His publications span across multiple high-impact venues including ACL, ESWC, and LREC, with a notable emphasis on practical applications of language technology in real-world scenarios. DFKI Research Fellow (2018) As an active member of the academic community, Rehm regularly serves as an expert for the European Parliament, reviews EU projects, and organizes numerous scientific conferences and workshops. His leadership extends to multiple European initiatives aimed at advancing language technology infrastructure and promoting digital language equality across Europe.

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +۷ مورد دیگر

Prof. Anika Groß is a Professor for Database Systems and Programming at the Department of Computer Science and Languages at Anhalt University of Applied Sciences in Köthen, Germany. She holds a PhD from the University of Leipzig (2014) and has held roles as a PostDoc in strategic research at Daimler AG and as a research assistant at the Interdisciplinary Centre for Bioinformatics (IZBI). She is actively involved in academic governance, serving on the faculty council, board of examiners, and various sustainability and research data management committees. Her research focuses on database systems, knowledge graphs, and ontology engineering, with applications in environmental science, medicine, and material sciences. Education - PhD (Dr. rer. nat.) in Computer Science, University of Leipzig (2014) - Diploma in Bioinformatics, MLU Halle-Wittenberg (2007) - Postdoctoral research at University of Leipzig's Database Group (2008–2013) Research Interests Her work emphasizes temporal graphs, ontology evolution, and semantic data integration. She develops tools like Region Evolution eXplorer (REX) and contributes to projects such as AgriRestore (ecosystem restoration) and MeineWaldKI (AI-based forest monitoring). Her recent focus includes wearable device applications in healthcare and digital twins for lithium value chains. Recent Research Trends Recent publications highlight interdisciplinary applications of knowledge graphs in agriculture and environmental science, leveraging temporal analysis and machine learning for tasks like chemical transformation prediction and bladder monitoring. She also explores cross-lingual semantic annotations in medical forms and holistic clustering of linked data. Professional Roles - Executive Committee Member, GI Database Systems Group - Fellow, Institute for Technologies and Economics of Lithium (ITEL) - Co-Chair of multiple workshops including FGDB-Workshop @ LWDA 2018 and BigDS@BTW 2017 Labs & Collaborations She leads research teams in projects funded by DFG, European Union (EFRE), and ITEL, collaborating with organizations like Daimler AG and ANU. Her lab focuses on advancing database systems and interdisciplinary data science applications.

• Database Systems
• Knowledge Graphs
• Ontologies
• +۷ مورد دیگر

Yang Lei is an academic affiliated with the University of Melbourne's Department of Computing and Information Systems. Their research focuses on knowledge graphs, FAIR data principles, large language models (LLMs), and ontology engineering. They contribute to initiatives like the Open Research Knowledge Graph (ORKG) and NFDI4DataScience, emphasizing reproducibility, scholarly metadata, and systematic literature reviews. Recent work includes applications of LLMs for abstract summarization, leaderboard extraction, and entity recognition in scholarly documents. Yang also explores challenges in FAIR Digital Objects, machine-actionable workflows, and interoperability in research data management.

• Artificial Intelligence
• Data Science
• Knowledge Graphs
• +۴ مورد دیگر

Kiril Gashteovski is a PhD candidate at the Chair of Practical Computer Science I: Data Analytics within the School of Business Informatics and Mathematics at the University of Mannheim. He is supervised by Prof. Dr. Rainer Gemulla and affiliated with the Data and Web Science Group. His primary research focuses on open information extraction, natural language processing, and machine learning applications. He concurrently holds a position as Research Scientist at NEC Laboratories Europe GmbH in Heidelberg, Germany. Education: PhD candidate (University of Mannheim, ongoing), prior academic background includes studies and research roles at institutions in Bulgaria, France, and North Macedonia. Research interests emphasize developing efficient open information extraction systems, knowledge base construction, and text analytics applications. His work bridges theoretical advancements with practical implementations in areas like entity linking and corpus development. Publications span topics such as OPIEC corpus development, citation-centered extraction, and toolkit creation for entity aspect linking. He actively presents at venues like AKBC and EMNLP. Advising and supervision include leading team projects like 'Complaint Handling via Text Analytics for Roche' and 'Data analytics on a ticketing system' with Abbvie. He also teaches courses in data mining and machine learning. Labs/Teams: Core member of the Data and Web Science Group at University of Mannheim, collaborating with industry partners like NEC Labs Europe.

• Open Information Extraction
• Natural Language Processing
• Data Mining
• +۳ مورد دیگر

Goran Glavaš is a Professor at the University of Würzburg's Faculty of Mathematics & Computer Science, holding the Chair for Natural Language Processing (Computer Science XII) and affiliated with the Center for Artificial Intelligence and Data Science (CAIDAS). His research focuses on computational semantics, multilingual/low-resource representation learning, and democratizing language technologies through fairness and sustainability. Former Assistant Professor at University of Mannheim (2017-2021) Interim Associate Professor at LMU Munich (2021-2022) Doctorate in 2014 at University of Zagreb under Jan Šnajder Recent research trends emphasize cross-lingual learning, multilingual knowledge integration, and ethical AI frameworks. His group contributes to robust multilingual models, vision-language systems, and sustainable NLP applications in social sciences. Outstanding Paper Award at ACL 2024 (IRCoder) Outstanding Paper Award at EACL 2024 (Kardeş-NLU) Extensive publications in EMNLP, ACL, NAACL, EACL, and TACL Advises a team of researchers at the University of Würzburg's NLP Chair, including Benedikt Ebing, Gregor Geigle, and Fabian David Schmidt. Leads the WüNLP group within CAIDAS, focusing on democratizing language technologies.

• Computational Semantics
• Multilingual Representation Learning
• Low-Resource NLP
• +۲ مورد دیگر

Elena Simperl is a Professor at King's College London, UK, with former affiliations at the University of Southampton and Karlsruhe Institute of Technology. Her work focuses on knowledge graphs, semantic web technologies, and AI-driven data management. She leads research in collaborative knowledge engineering, dataset search, and AI ethics, contributing to projects like the TheyBuyForYou platform for public procurement transparency. Her research interests span knowledge representation, crowdsourcing, and human-AI collaboration. Notable contributions include advancing methods for knowledge graph construction, improving data quality via crowdsourced and automated approaches, and exploring the societal impact of AI systems. She has co-edited major conferences such as ISWC and ESWC, and her work bridges technical innovation with practical applications in public policy and information systems. Key projects include developing frameworks for dataset usability, AI-ready data infrastructure, and systems for fact-checking visual content. Her collaborations span academia and industry, addressing challenges in data governance, misinformation detection, and ethical AI deployment.

• Knowledge Graphs
• Semantic Web
• Data Quality
• +۵ مورد دیگر

Andreas Thor is affiliated with the University of Applied Sciences for Telecommunications Leipzig, Germany. He has been actively publishing in computer science since 2004, with a focus on database systems, data integration, bibliometrics, and educational technology. His research spans several domains, including database systems, entity resolution, ontology matching, and more recently, e-assessment and digital learning tools. He has made significant contributions to bibliometrics through the development and application of CRExplorer for Reference Publication Year Spectroscopy (RPYS). His work also includes the design of educational tools like DMT and FeeDI for automated assessment in higher education. The recent articles (2021–2023) reflect a strong trend toward educational technology, particularly in the development of tools for e-assessment, Jupyter Notebook integration, and knowledge graphs for learning. Earlier works (2004–2012) focus on core database research such as entity resolution, data fusion, and MapReduce-based systems. The publications collectively demonstrate a transition from foundational database research to applied educational informatics. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: No information is available regarding students advised or grants received. However, his extensive publication record and leadership in workshop proceedings (e.g., GI-Workshop Grundlagen von Datenbanken) suggest involvement in academic mentoring and collaborative research initiatives. Labs and Teams: Andreas Thor has collaborated extensively with researchers at the University of Leipzig, particularly with Erhard Rahm, Toralf Kirsten, and Stefanie Scherzinger. His work on bibliometrics involves collaboration with Lutz Bornmann, Loet Leydesdorff, and Robin Haunschild. These collaborations indicate participation in research groups focused on data management and scientometrics.

• Database Systems
• Data Integration
• Entity Resolution
• +۱۲ مورد دیگر

Martin Theobald is a Professor in the Department of Computer Science at the University of Luxembourg's Faculty of Science, Technology and Communications. Previously affiliated with University of Ulm, Germany, his research spans database systems, information retrieval, and knowledge extraction with over 120 publications since 2002. His work bridges theoretical database foundations with practical applications in large-scale data processing. His research focuses on: Probabilistic and uncertain database systems Stream processing frameworks (notably the AIR architecture) Knowledge extraction from heterogeneous data sources Integration of machine learning with database systems Efficient query processing for structured and semi-structured data Recent publications demonstrate an evolving research trajectory toward real-time data stream processing with machine learning integration. His work on the AIR (Asynchronous Iterative Routing) framework and its extensions (TensAIR, OPTWIN) addresses critical challenges in concept drift detection, neural network training on streaming data, and efficient resource utilization. These contributions sit at the intersection of database systems, distributed computing, and machine learning, with applications in knowledge graph construction and question answering systems. Martin Theobald has mentored numerous researchers including Mauro Dalle Lucca Tosi, Alessandro Temperoni, and Vinu E. Venugopal, who have become active contributors to the database community. His collaborative network spans institutions across Europe, with frequent partnerships with researchers from University of Ulm, Max Planck Institute, and other European universities. His laboratory work focuses on developing scalable systems for processing evolving data streams, with particular emphasis on creating lightweight architectures that maintain high performance while minimizing resource consumption. Current projects involve integrating knowledge graphs with real-time analytics and developing adaptive systems that can handle concept drift in streaming environments.

• Database Systems
• Information Retrieval
• Probabilistic Databases
• +۴ مورد دیگر