جستجوی استاد راهنمای دانشگاهی

• Information Retrieval
• Data Mining
• Text Classification
• +۵ مورد دیگر

Allan Hanbury is a Full Professor for Data Intelligence and Head of the Data Science Research Unit at the Faculty of Informatics, TU Wien, Austria. He serves as Faculty Representative responsible for financial affairs and internationalization within the faculty. He is also a faculty member at the Complexity Science Hub Vienna. Roles: Faculty Representative (financial/internationalization), Data Science Research Unit Head Key Projects: Coordinator of EU-funded Khresmoi (medical search), DoSSIER (Marie Curie ITN training network), VISCERAL (big data evaluation), KConnect (medical text analysis) Spin-off: Co-founder of contextflow (radiology image search) Research focuses on information systems engineering and visual computing, with emphasis on: Information retrieval, data mining, text classification Medical data intelligence, health informatics Domain-specific systems for information extraction/retrieval Publications span over 180 refereed works, with recent contributions on LLMs in query generation, clinical trial matching, and systematic review automation. His work bridges academic research with industry applications in healthcare, legal text analysis, and patent mining. Grants include EU Horizon projects (e.g., DoSSIER, BRISE-Vienna), FFG-funded innovation initiatives, and collaborations with organizations like Siemens, Deutsche Telekom, and the Austrian Ministry of Finance.

• Logic Programming
• Computational Complexity
• Nonmonotonic Reasoning
• +۴ مورد دیگر

Mantas Simkus is an Assistant Professor at TU Wien's Institute of Logic and Computation, affiliated with the Database and Artificial Intelligence Group. He previously held an Associate Professor position at Umeå University (Sweden) within the Wallenberg AI, Autonomous Systems and Software Program (WASP). He leads the FWF-funded project 'KtoAPP: Compiling Knowledge into Applications' and contributes to the Cluster of Excellence 'Bilateral Artificial Intelligence'. His research focuses on logic-based data management, knowledge representation, and nonmonotonic reasoning, with applications in semantic web technologies and ontology engineering. Education: Bachelor's in Computer Science from Vilnius University. Research interests include logic programming, computational complexity, description logics, and their integration with databases. He explores techniques for efficient query answering, schema validation (e.g., SHACL), and reasoning under incomplete information. His work bridges theoretical foundations (e.g., complexity analysis, formal semantics) with practical systems (e.g., ontology-mediated query processing). Key projects include 'KtoAPP' (2018–2025), investigating automated knowledge compilation, and contributions to the 'SemDat' and 'OMEGA' initiatives. He teaches courses on deductive databases and semi-structured data at TU Wien. He actively participates in academic service: co-chair of RuleML+RR 2024, editorial board member of the Artificial Intelligence journal, and former co-chair of DL 2019. His research group collaborates on topics like graph databases, answer set programming, and hybrid knowledge representation systems.

• Graph Data Management
• RDF and Semantic Web
• Database Theory
• +۳ مورد دیگر

Maxime Jakubowski is a PostDoc Researcher at TU Wien's Faculty of Informatics, affiliated with the Databases and Artificial Intelligence research group. Based in Room HA0320 at Favoritenstrasse 9, he can be contacted at maxime.jakubowski@tuwien.ac.at and maintains an ORCID profile (0000-0002-7420-1337). His research centers on graph data management within the Semantic Web ecosystem, specializing in RDF validation through shape constraint languages like SHACL and ShEx. He investigates formal foundations, expressiveness boundaries, and practical implementations including SQL compilation and neighborhood-based graph description. Current projects include FRESH (2021–2026), KtoAPP (2018–2025), and TARGET (2024–2028), focusing on graph data theory and implementation. Recent publications (2021–2025) demonstrate consistent contributions to RDF validation standards, with 14 articles addressing shape language formalization, compilation techniques, and provenance tracking. His work bridges theoretical database concepts with industrial applications in knowledge graph validation. Dr. Jakubowski supervises bachelor theses and teaches courses including Management of Graph Data (192.161) and Project in Computer Science. His research collaborations span international projects and the Dagstuhl Seminar 24102 on Shapes in Graph Data. He is an active member of the Databases and Artificial Intelligence research group, contributing to TU Wien's leadership in graph data management and Semantic Web technologies through both theoretical research and practical tool development.

Monika Henzingerمشاهده پروفایل

استاد

• Combinatorial algorithms
• Data structures
• Algorithmic game theory
• +۱ مورد دیگر

Monika Henzinger is a Full Professor of Computer Science at the University of Vienna since 2009. Previously, she held faculty positions at the Ecole Polytechnique Federale de Lausanne (2005-2009) and the University of the Saarland (1999-2005). She has also worked as a Director of Research at Google Inc and as a Research Staff member at Digital Equipment Corporation . Education: Ph.D., Computer Science, Princeton University (1993) Diploma, Computer Science, University of the Saarland (1989) Her research focuses on combinatorial algorithms , data structures , algorithmic game theory , and web information retrieval , with significant contributions to dynamic graph algorithms and probabilistic verification. Her work addresses problems in sponsored search auctions, web mining, and formal verification of stochastic systems. Key trends in her publications include web algorithmics , dynamic graph processing , probabilistic verification , and online optimization . Her research has been recognized through prestigious grants like the ERC European Young Investigator Award and the NSF CAREER Award . Scientific Awards: ERC Advanced Grant (2013) Honorary Doctorate, Technical University Dortmund (2013) European Young Investigator Award (2004) NSF CAREER Award (1995) She has advised PhD students at EPFL and the University of Vienna , including Paul Dütting and Veronika Loitzenbauer . Her editorial roles include serving as Editor of EATCS Monographs in Theoretical Computer Science and on the ACM Research Highlights board. She has led projects such as the Doctoral School Computational Science and Dynamic Graph Algorithms in Directed Graphs .

Shen Heng Taoمشاهده پروفایل

دانشگاهی

• Artificial Intelligence
• Multimedia Computing
• Computer Vision
• +۳ مورد دیگر

Shen Heng Tao is a Distinguished Professor and Dean of the School of Computer Science and Engineering at the University of Electronic Science and Technology of China (UESTC). He is also the Executive Dean of the AI Research Institute and Chief Scientist of Vision Intelligence at the Peng Cheng Laboratory. His academic journey includes a BSc (First Class Honours) and PhD from the National University of Singapore (2000 and 2004), followed by roles at the University of Queensland, where he became a Professor in 2011. His research focuses on artificial intelligence, multimedia computing, and computer vision, with notable contributions to cross-media intelligent analysis and real-time video retrieval systems. He has published over 400 peer-reviewed papers, including 160+ IEEE/ACM Transactions, and holds an H-index of 78. His work has been recognized with awards such as the IEEE Fellow, ACM Fellow, OSA Fellow, and Clarivate Highly Cited Researcher. He has led 15 major research grants, including projects from the Ministry of Science and Technology of China and National Natural Science Foundation. His service includes roles as Associate Editor for journals like ACM Transactions of Data Science and IEEE Transactions on Multimedia, and conference chairs like ACM Multimedia 2021. His lab, the Center for Future Media, explores advanced topics like multimodal learning, trustworthy AI, and human-robot interaction. Current research emphasizes cross-modal reasoning and real-world applications in metaverse, autonomous systems, and smart cities.

• Artificial Intelligence
• Database Systems
• Machine Learning
• +۳ مورد دیگر

Charlotte Moeyersons is a Visiting Scientist at TU Wien's Faculty of Informatics, affiliated with the Databases and Artificial Intelligence department (E192-02). She contributes to research at the intersection of AI and database systems. Her research focuses on advancing database technologies through artificial intelligence methodologies, including machine learning integration in data management systems and computational logic applications. Key areas include optimizing query processing with AI techniques and developing intelligent information retrieval frameworks. Professional presence includes: Faculty profile TISS teaching profile No specific awards or grants are highlighted in the provided information.

• Model-Driven Engineering
• AutomationML
• Cyber-Physical Systems (CPS)
• +۵ مورد دیگر

Manuel Wimmer is a Professor affiliated with the Department of Business Informatics at TU Wien's Faculty of Informatics. His main research area is Model-Driven Engineering , focusing on topics such as AutomationML, Cyber-Physical Systems (CPS), and industrial standards like IEC 62264 and ISA-95. He leads the Network Lab and contributes to interdisciplinary projects involving robotics, cloud computing, and blockchain applications. Research Interests: Model Transformation, Tool Interoperability, Industrial Automation, Educational Methodologies in Software Engineering. Key Technologies: UML, ATL, OPC UA, AutomationQL. Recent work emphasizes bridging metamodeling platforms (e.g., ADOxx/EMF integration) and adapting robotic mission planning systems. He has published extensively in workshops like MDE 2023 and conferences on model-driven engineering. Teaching activities include remote-learning strategies for software engineering education, as documented in his 2021 paper. No explicit awards are listed, but his contributions to standards like AutomationML reflect industry recognition.

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Prof. Allan Hanbury is a full professor at TU Wien's Faculty of Informatics, leading the E-Commerce Research Area. His work focuses on domain-specific systems for information extraction and retrieval, with expertise in machine learning, natural language processing, and medical informatics. Key projects include developing tools like CRUISE-Screening for systematic literature reviews and VoMBaT for high-recall search evaluation. He coordinates initiatives in transparent automated content moderation and image mining for patents. His research bridges theoretical advancements with practical applications in healthcare, legal domains, and digital humanities. Publications span over 240 works, emphasizing reproducibility, explainability in AI, and cross-lingual systems. Active in organizing conferences like PatentSemTech and contributing to CLEF challenges. Advises on thesis topics involving hate speech detection, legal text analysis, and graph-based methods. Collaborates internationally on projects like CSMeD (clinical trial matching) and BRISE (legal corpus development). His work integrates NLP, IR, and data mining to address real-world challenges in information access and AI transparency.

Gabriele Kotsisمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +۶ مورد دیگر

Gabriele Kotsis is a Full Professor at the Institute of Telecooperation, Johannes Kepler University Linz, with extensive contributions to Artificial Intelligence research. Her institutional presence spans multiple departments through interdisciplinary projects while maintaining her primary affiliation with JKU's engineering-focused research units. Her research expertise encompasses: Natural Language Processing and Neural Machine Translation systems Reinforcement Learning applications for Smart Grid optimization Mobile Computing and Multimedia Intelligence frameworks Big Data Analytics and Database Systems innovation Human-centered AI development methodologies Recent publications (2024-2025) reveal a strategic focus on practical AI implementations addressing multilingual communication barriers and energy efficiency challenges. Her work consistently bridges theoretical AI advances with real-world applications across multiple domains. Professor Kotsis maintains an active supervision record with thesis guidance and leads multiple funded research initiatives. Her current portfolio includes: 'Enhancing Neural Machine Translation' project (2025-2026) for Southeast Asian languages 'INTES' regulatory compliance ecosystem (2023-2026) 'Human-centered Artificial Intelligence' initiative (2022-2026) Through her leadership in international conferences (iiWAS, MoMM, DEXA) and editorial roles for major proceedings, she maintains significant influence in the global computer science research community while advancing JKU's research profile.