جست‌وجوی استاد و پژوهشگر

Søren Kejser Jensenمشاهده پروفایل

استادیار

• Time Series Management
• Lossy Compression
• Renewable Energy Analytics
• +۵ مورد دیگر

Søren Kejser Jensen is a Tenure Track Assistant Professor at the Department of Computer Science, Aalborg University. He is affiliated with The Technical Faculty of IT and Design and the Daisy - Center for Data-intensive Systems. His research focuses on time series management systems, lossy compression techniques, and their applications in renewable energy analytics, IoT data management, and edge computing. He has contributed to the development of ModelarDB, a scalable model-based time series management system. Key research areas include: Optimizing lossy compression for sensor data and wind turbine analytics Designing scalable database solutions for high-frequency data streams Integrating machine learning with compressed IoT data Exploring freshness metrics in communication systems His publications highlight advancements in edge-cloud architectures, model-based data management frameworks, and holistic analytics for renewable energy systems. Collaborations extend to global projects like 6G wireless systems and AI-driven multi-network deployments.

Michael Martinمشاهده پروفایل

استاد

• Semantic Web Technologies
• Data Science
• Linked Data Management
• +۴ مورد دیگر

Prof. Dr. Michael Martin serves as Professor for Data Management Systems at TU Chemnitz, where he heads the Emergent Semantics research group as part of the AKSW network. His academic work bridges theoretical research and practical applications of semantic technologies, with strong connections to both TU Chemnitz and the University of Leipzig where he teaches courses in E-Business, Software Engineering and Software Management. Michael Martin's research focuses on Engineering of Web Applications using Semantic Web Technologies, Data Science, and Management of Linked (Open) Data. His work explores how semantic technologies transform traditional web applications into data-driven systems, with particular emphasis on knowledge graph engineering, LLM-assisted semantic technologies, and industrial applications in sectors like steel and copper production. Recent work demonstrates growing interest in applying these technologies to crisis management and resilience research. His publication record shows a clear evolution from foundational semantic web technologies toward integrating large language models with knowledge graphs. Current research trends include developing benchmarks for LLM capabilities in knowledge graph engineering (LLM-KG-Bench), creating ontology-based digital representations for industrial processes (KupferDigital, StahlDigital), and building practical tools for geo-spatial data integration with semantic technologies. These works demonstrate increasing sophistication in applying semantic technologies to real-world industrial challenges. Michael Martin leads significant research projects including LEDS (Linked Enterprise Data Services funded by BmBF), SlideWiki (EU-funded), and previously contributed to major initiatives like LOD2, LATC, OntoWiki, and the Digital Agenda Scoreboard of the European Commission. His work demonstrates strong grant acquisition capabilities across both national and European funding programs, with clear translational impact from academic research to practical applications. As head of the Emergent Semantics research group within the AKSW network, Martin leads a team focused on practical applications of semantic technologies. The group develops tools like OntoWiki for semantic web application development and participates in creating knowledge graph platforms for industrial applications and crisis management, demonstrating strong industry-academia collaboration and real-world impact of semantic technologies.

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

Zhan Su serves as a Guest Researcher within the Programming Languages and Theory of Computation group at the Department of Computer Science, Faculty of Science, University of Copenhagen. His research bridges theoretical computer science with practical applications in artificial intelligence and privacy. Education: Ph.D. in Computer Science, University of Copenhagen, 2024 (Thesis: Information Propagation in Modular Language Modeling and Web Tracking) Research Interests: Dr. Su's work centers on quantum-inspired neural architectures for natural language processing and information retrieval systems. He pioneers density matrix representations for semantic modeling and investigates conversational search mechanisms enhanced by large language models. His research extends to privacy vulnerabilities in educational technology, examining web tracking evolution across digital learning platforms. The integration of quantum computing principles with classical machine learning frameworks represents a distinctive thread throughout his publications. Publication Trends: Analysis of Dr. Su's 8 publications (2023-2025) reveals three dominant research trajectories: (1) Quantum-AI hybrid systems (40% of output), featuring entanglement-embedded networks and density matrix word representations; (2) Conversational information retrieval (38%), with innovations in history-aware dense retrieval and query alignment; (3) Privacy/security applications (22%), particularly in online education ecosystems. His 2025 works demonstrate increasing sophistication in quantum-classical model fusion, while 2024 outputs show substantial conference presence at ACL, CIKM, and WWW venues. Advising and Grants: No advisory roles or grant funding details are documented in the available materials. Current research appears conducted within collaborative frameworks at the University of Copenhagen. Labs and Teams: Dr. Su operates within the Programming Languages and Theory of Computation research environment at DIKU (Department of Computer Science), engaging with interdisciplinary teams focused on language modeling, quantum computing applications, and web security analysis. His collaborations span institutions including the University of Copenhagen's Department of Computer Science and external partners in natural language processing research.

• Corpus Linguistics
• Computational Linguistics
• Natural Language Processing
• +۴ مورد دیگر

Philip Diderichsen is a Researcher at the Department of Nordic Studies and Linguistics, University of Copenhagen, holding a Ph.D. and serving as a Special Consultant focused on corpus linguistics and Danish language technology. His work centers on developing linguistic resources for digital and spoken Danish interaction, including corpus infrastructure and language resource curation. His primary research spans Corpus Linguistics, Computational Linguistics, and Digital Humanities with dedicated emphasis on Danish language analysis. Key interests include corpus development for digital communication, spoken language representation, OCR correction for historical texts, loanword evolution, and language resource management. Analysis of his 2019-2025 publications reveals consistent innovation in Danish language resource creation, featuring digital interaction corpora (DanDIGI), spoken language annotation frameworks, historical text restoration techniques, and language change visualization tools. His collaborative work integrates computational methods to address challenges in linguistic data infrastructure. Diderichsen actively contributes to the Language Change Center (Sprogforandringscentret), presenting on data infrastructure and future research directions. His conference engagements include the Nordic Dialectology Conference and specialized workshops on digital interaction corpus development, reflecting strong regional collaboration in Nordic linguistics.

• Programming Languages
• Formal Methods
• Logic in Computer Science
• +۹ مورد دیگر

Dmitriy Traytel is an Associate Professor in the Department of Computer Science at the University of Copenhagen, affiliated with the Software, Data, People & Society research section. His work bridges formal methods, programming languages, and runtime verification, with a strong emphasis on correctness and efficiency. His research focuses on the logical and formal foundations of programming systems, including syntax with bindings, higher-order logic, type systems, and temporal logics. He develops verified tools and frameworks for runtime monitoring, policy enforcement, and query evaluation, often using interactive theorem provers like Isabelle/HOL. His recent publications demonstrate a consistent contribution to top venues such as POPL, CAV, and TACAS. The trends in his recent articles show a deep engagement with runtime verification , particularly in developing explainable , efficient , and first-order monitoring techniques. He combines theoretical rigor with practical tool building, as seen in systems like WHYMON and TimelyMon. His work often intersects with security policies, functional programming, and formal semantics. He has not been mentioned in the text as receiving scientific awards, but his publication record in premier venues indicates high scholarly impact. While no students are explicitly listed, his role as an Associate Professor and active researcher suggests involvement in advising. There is no mention of specific grants, but his participation in a Promotion Programme implies institutional support. He is part of a vibrant research environment within the Department of Computer Science, which is involved in the SCIENCE AI Centre, suggesting interdisciplinary collaboration potential. Traytel maintains a personal website and ORCID profile, and his contact information is publicly available. He is actively contributing to the research output of the department, with 59 recorded publications, including journal articles and conference proceedings.

Peter Dologمشاهده پروفایل

دانشیار

• Recommender Systems
• Web Engineering
• Machine Learning
• +۵ مورد دیگر

Peter Dolog is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design. His research focuses on Recommender Systems, Web Engineering, Machine Learning, and Health Informatics. He leads projects such as the MEco Medical Ecosystem and the IWIS Intelligent Web and Information Systems initiative. His work emphasizes explainable AI, knowledge graphs, and social media analytics for public health monitoring. Notable contributions include hypergraph-based recommendation models and sensitive information detection in legal documents. Projects include MEco (medical event surveillance) and IWIS (web systems personalization), funded by EU and national grants. His research spans 148 publications, with recent work on explainable recommendation systems and neural networks for medical data analysis. Collaborations include the University of Vienna and international conferences like ECIR and RecSys. Media coverage highlights his work on using social media for epidemic detection. Key research areas include recommendation systems with attention mechanisms, knowledge graph integration, and ethical AI applications in healthcare. His methodologies bridge machine learning with user-centric design, emphasizing transparency and explainability in AI-driven systems.

Christian Thomsenمشاهده پروفایل

دانشیار

• Data Engineering
• Data Warehousing
• Business Intelligence
• +۵ مورد دیگر

Christian Thomsen is an Associate Professor at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. His research focuses on data engineering, time series management, and smart energy systems. He leads the Data Engineering, Science and Systems and Daisy - Center for Data-intensive Systems groups. Key projects include domOS (operating system for smart buildings), FED (flexible energy systems), and ODA (open data for sustainability). He has extensive experience in semantic interoperability, IoT integration, and large-scale data analytics. Thomsen's work spans energy data management, scalable time series systems (e.g., ModelarDB), and ETL process frameworks (e.g., PygramETL). His contributions to semantic models for multidimensional queries and open-source tools for business intelligence reflect his commitment to practical, scalable solutions for complex data challenges. Media engagements highlight his role in national energy data initiatives and smart building technologies. He supervises PhD students in data-intensive systems and collaborates internationally on projects like the Erasmus Mundus IT4BI doctoral college. His research bridges academia and industry, addressing real-world issues in energy systems, IoT interoperability, and big data infrastructure.

Kristian Torpمشاهده پروفایل

استاد

• Trajectory Data
• GPS Data Management
• Energy Engineering
• +۵ مورد دیگر

Professor Kristian Torp is affiliated with the Department of Computer Science at Aalborg University, where he leads research in the Daisy Center for Data-Intensive Systems and participates in the BLUE Marine & Maritime Research initiative. His work focuses on spatial data management, trajectory analysis, and green mobility solutions. He has contributed to projects like Streamspin (smart push technology), TransDB (GPS data management), and REMOTE (traffic monitoring). Torp has published over 90 peer-reviewed articles and holds six awards for best papers and demos at international conferences. His research explores energy consumption optimization for electric vehicles, vessel trajectory prediction, and maritime data analysis, emphasizing practical applications in transportation and environmental sustainability. Research interests include trajectory data analytics, GPS-based systems, and data privacy in mobility contexts. Key contributions involve developing efficient spatial data warehouses, automated traffic monitoring frameworks, and frameworks for eco-routing and CO2 reduction. His work often bridges theoretical computer science with real-world infrastructure challenges, such as optimizing roundabout efficiency and reducing emissions through data-driven approaches. Awards: Best Paper (2018, 2014), Best Demo (2018, 2013), Best Poster (2014) Labs: Daisy Center, BLUE Research Group Grants: EU FP6, Viking Tempo Program Current projects include ACTIVE (vessel trajectory similarity search) and Mobispaces (energy-efficient mobility data architecture). His work is widely cited in transportation, environmental science, and database systems domains.

Hua Luمشاهده پروفایل

استاد

• Computer Science
• Data Management
• Location-Based Services
• +۵ مورد دیگر

Hua Lu is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design and the Daisy - Center for Data-intensive Systems. Their research focuses on data management, indoor positioning, mobility data, and location-based services. Key contributions include trajectory analysis, time series forecasting, and uncertain data processing. Research interests span computer science fundamentals, with emphasis on query processing, data quality management, and pervasive computing. Projects include managing mobility data for the Internet of Things, indoor location-based systems, and data-driven trajectory monitoring. Recent work addresses robust trajectory similarity computation and contact tracing in indoor environments. Notable achievements include the SSTD 2021 Best Paper Candidate and the 2019 Best Vision Paper Award. Grants and collaborations involve Danish-Sino big data initiatives and indoor-space trajectory management. Lu has led multiple research projects on mobile data management and indoor LBS since 2008. Professional activities include editorial roles and participation in international conferences. The Daisy Center collaboration highlights focus on scalable data systems. Media coverage includes a 2020 article on university-military ties.

Panagiotis Karrasمشاهده پروفایل

استاد

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

Panagiotis Karras is a Professor of Computer Science at the University of Copenhagen and a faculty affiliate at Aarhus University. His research centers on robust and versatile methods for data access, mining, and representation. He has held academic positions at Aalborg University, Skoltech, Rutgers, NUS, the University of Zurich, and DTU, reflecting a broad international academic footprint. His research interests lie at the intersection of database systems, data mining, and machine learning, with a focus on graph data management, data privacy, indexing, influence maximization, and clustering. He aims to develop scalable and efficient algorithms that maintain high utility under privacy and accuracy constraints. His recent publications reveal a strong trend in graph-based methods, including graph alignment (GRASP, FREDE), influence and diffusion modeling, data anonymization, and adaptive indexing. These works appear in premier venues such as VLDB, SIGMOD, KDD, ICDE, and AAAI, indicating sustained high-impact contributions. He has been recognized with the 2008 Young Scientist Award in Physical/Mathematical Science from HKIS. Karras is actively seeking motivated PhD students and has supervised numerous collaborative projects. While specific grant details are not listed, his continuous publication output and affiliations suggest active research funding. His work often involves interdisciplinary collaboration, particularly in graph analysis and privacy-preserving technologies. He has contributed significantly to the development of efficient data synopses, anonymization frameworks (e.g., SABRE, L-opacity), and scalable graph processing systems, often in collaboration with leading researchers in the database and data mining communities.

Finn Årup Nielsenمشاهده پروفایل

دانشیار

• Neuroinformatics
• Text Mining
• Data Mining
• +۸ مورد دیگر

Finn Årup Nielsen is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the Cognitive Systems department under Applied Mathematics and Computer Science. His career spans neuroinformatics, text mining, and social media analysis, with a focus on integrating neuroscience and computer science. Education : Master in Engineering (1993-1996), Technical University of Denmark Research Interests : Neuroinformatics: Developing software and databases for brain imaging data and neuroscience text analysis Social Media Analysis: Mining Twitter and Wikipedia for citation patterns, sentiment, and topic trends Knowledge Graphs: Wikidata integration, lexeme alignment, and semantic representation Environmental Informatics: Applying AI to sustainable assessment through projects like DreamsKG Project Supervision : Supervised PhD projects on federated learning, neural network explainability, and big data analytics at DTU Contributed to foundational neuroimaging research in the Human Brain Project since 1994

Inge Li Gørtzمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Design and Analysis of Algorithms
• +۷ مورد دیگر

Inge Li Gørtz is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where she leads research in algorithms and data structures. She is affiliated with the Algorithms, Logic and Graphs section and has been a principal investigator on externally funded research projects. She also serves as chairman of the Danish Society for Computer Science since 2009. Research Interests: Her work centers on the design and analysis of algorithms, with a focus on approximation algorithms, pattern matching, graph algorithms, and compressed data representations. She investigates efficient query processing, string indexing, and algorithmic challenges in highly repetitive data, including applications in bioinformatics and data streams. The recent publications highlight a strong trend in theoretical and practical algorithm design, particularly in string indexing, compressed automata, and dynamic data structures. These works span venues like SOFSEM and STACS, emphasizing innovations in sublinear query time, sliding window models, and memory-efficient representations. Postdoc stipend from Carlsberg Foundation (2006–2008) for 'Approximation of Transportation on Demand Problems', kr 865,183 Advising and Grants: She actively supervises multiple PhD students on projects such as biomedical image segmentation using graph cuts, hierarchical compression of DNA data, and dynamic graph algorithms. She has served as main or co-supervisor in several funded PhD projects. She has been principal investigator on a Carlsberg Foundation grant and has participated in multiple Danish Council for Independent Research projects. Labs and Teams: She is a core member of the Algorithms, Logic and Graphs (ALG) section at DTU, contributing to a vibrant research environment focused on theoretical computer science and algorithmic problem solving.

Troels Andreasenمشاهده پروفایل

دانشیار

• Computer Science
• IT
• Databases and knowledge bases
• +۶ مورد دیگر

Troels Andreasen is an Associate Professor at the Department of People and Technology, Roskilde University. His research focuses on advanced query systems, artificial intelligence, and knowledge base modeling. Computer Science Databases and knowledge bases Fuzzy logic and quantities Intelligent search engines His work explores natural logic frameworks for database querying, flexible query answering systems, and intelligent information retrieval methods. Recent publications analyze computational intelligence for search systems and logic-to-database integration techniques. Notable projects include: SEAFACTS (2023-2025) - Digital platform for maritime history MemoryMechanics (2020-2023) - Memory systems research NorDigHealth (2019-2021) - Regional digital health solutions Kulturportalen (2014-2017) - Cultural heritage platform He has served on program committees at international conferences and participated in external organizations like University of Granada and Aalborg University. Media engagements highlight his expertise in search engine evolution and fuzzy logic applications since 2005.

• Data Management
• Graph Algorithms
• Software Systems
• +۳ مورد دیگر

Konstantinos Skitsas serves as an Assistant Lecturer and Visiting PhD Student at the Department of Computer Science, University of Copenhagen, affiliated with the Software, Data, People & Society (SDPS) section under Head of Section Dmitriy Traytel. His research focuses on data management systems and graph algorithms, specifically developing scalable techniques for subgraph matching and spectral filtering. This work addresses computational challenges in processing large-scale graph-structured data, with applications in social network analysis and pattern recognition. The SDPS section emphasizes interdisciplinary collaboration with industry to create software systems aligned with human and societal needs. His 2025 publication “Pilos” represents current work in graph processing efficiency, contributing to IEEE ICDE proceedings. The SDPS research environment actively explores topics like offline speech recognition and process discovery as indicated in section news. As part of the Department of Computer Science, he contributes to teaching while conducting PhD research within Copenhagen’s computational ecosystem, with office locations at Universitetsparken 1 (2100 Copenhagen Ø) and Sigurdsgade 41 (2200 Copenhagen N).

Panagiotis Karrasمشاهده پروفایل

استاد

• Data Management Systems
• Graph Algorithms
• Data Mining
• +۳ مورد دیگر

Panagiotis Karras is a Professor at the Department of Computer Science , University of Copenhagen , with a faculty affiliate status at Aarhus University . His research focuses on robust and versatile methods for data access, mining, and representation. Education: MSc in Electrical and Computer Engineering from National Technical University of Athens , PhD in Computer Science from University of Hong Kong Research Interests: Spanning data management systems, graph algorithms, and privacy-preserving data publishing, his work addresses challenges in efficient query processing, influence propagation in networks, and semantic-aware data compression. Notable contributions include adaptive indexing techniques, graph alignment frameworks, and algorithms for fair stable marriage problems. Recent Publications (2024-2025): Focus on graph alignment, subgraph matching, and influence maximization, with applications to knowledge graphs, social networks, and time-series data analytics. Scientific Recognition: 2008 Young Scientist Award from the Hong Kong Institute of Science Keynote invitations at ACM SIGKDD and IEEE ICDE Professor Karras actively seeks motivated PhD students to collaborate on these research directions. His lab maintains a powerful compute cluster and modern facilities for data-intensive research.