جست‌وجوی استاد و پژوهشگر

Nicolaj Søndberg-Jeppesenمشاهده پروفایل

مدرس پاره‌وقت

• Bayesian Networks
• Cyber-Physical Systems
• Healthcare Informatics
• +۴ مورد دیگر

Nicolaj Søndberg-Jeppesen is a Part-Time Lecturer at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on Bayesian networks, cyber-physical systems, and their applications in healthcare, industrial diagnostics, and decision support systems. Key research interests include: Development of Bayesian network models for predictive maintenance and health monitoring in industrial systems Integration of operational data into continuous improvement frameworks Application of probabilistic graphical models in healthcare logistics and emergency management Design of smart manufacturing architectures for diagnostics and predictive maintenance Notable projects include: Animated Computer Games (ongoing since 2010) Extending the scope of graphical models for decision problems (ongoing since 2010) His recent work emphasizes: Patient flow prediction in emergency departments Risk assessment in home care deliveries Object-oriented Bayesian networks for component-level diagnostics Publications span applications in healthcare informatics, industrial engineering, and game theory, with a focus on practical implementations of probabilistic models.

Kai Zhaoمشاهده پروفایل

پژوهشگر ارشد

• Time Series Analysis
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Kai Zhao is a Research Fellow in the Department of Computer Science at the Technical Faculty of IT and Design, Aalborg University, Denmark, specializing in time-series analytics and machine learning applications. Research Interests: Time Series Analysis Machine Learning Data Mining Contrastive Learning Graph Neural Networks Anomaly Detection His recent publications (2023-2025) demonstrate a cohesive focus on graph-based and contrastive learning techniques for time-series challenges, particularly addressing few-shot scenarios, multivariate forecasting, and anomaly detection in IoT contexts. Methodological innovations include dynamic graph modeling and generative contrastive frameworks. Scientific Awards: No awards mentioned in available documentation. As Principal Investigator, he led the project 'Time Series Analyse with Few-shot Learning' (2022-2023), advancing few-shot learning applications for time-series analysis. His collaborative network spans multiple institutions, evidenced by co-authorships with researchers from Aalborg University and international partners. His research integrates theoretical frameworks with practical implementations, showing strong emphasis on robustness and adaptability in real-world time-series applications.

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

Lenka Tetkováمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Lenka Tetková is a Postdoctoral Research Fellow at the Technical University of Denmark , affiliated with the School of Computing and the Cognitive Systems department. Her work focuses on Artificial Intelligence , Machine Learning , and their applications in Speech Recognition , Image Classification , and Biological Data Analysis . Her research explores Transformer stacks in speech models, convex decision regions in deep networks, and explainable AI for biological systems. She also investigates model pruning to reduce computational effort while maintaining performance. Her recent publications highlight trends in speech representation models , neural network convexity , and hybrid symbolic-AI approaches for resource-constrained environments. These works span computer science , engineering , and biological data analysis . Lenka collaborates with researchers like Dr. Lars Kai Hansen and contributes to interdisciplinary projects at the intersection of AI , signal processing , and life sciences .

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +۳ مورد دیگر

Han Tang is a Research Fellow at the Department of Computer Science , University of Copenhagen , specializing in Machine Learning . He is affiliated with the Machine Learning section, which engages in both theoretical and applied research across domains like information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. Research activities at the department include collaboration with the SCIENCE AI Centre and the TreeSense center for remote sensing and deep learning of global tree resources. The section also provides access to a powerful compute cluster for advanced computational tasks. His recent work includes research on contextual molecule representation learning, leveraging chemical reaction knowledge for data-driven applications in computational chemistry and artificial intelligence. This aligns with the department’s focus on integrating machine learning with interdisciplinary challenges.

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.

• Network Analysis
• Social Networks
• Data Mining
• +۶ مورد دیگر

Michele Coscia is an Associate Professor in the Department of Data Science at the IT University of Copenhagen. He also serves as the Head of Programme for the BSc in Data Science. His research focuses on network analysis, social networks, data mining, and their applications in understanding human mobility, complex systems, economic development, and memetics. He has contributed to projects such as the Pioneer Centre for Artificial Intelligence and leads initiatives like ROMNET (Past social network reconstruction from material culture data) and Work2Vec (exploring deep learning in healthcare and work hours analysis). His research interests encompass interdisciplinary network science, including misinformation dynamics, ideological polarization, and the impact of social media systems. He has developed methodologies like the generalized Euclidean measure for multilayer networks and explored cultural data analytics through case studies like Italian music history. His work also addresses real-world challenges, such as quantifying the effects of violence on migration patterns and optimizing network sampling strategies for cost efficiency. Michele Coscia has received the Årets Forskningsmiljø 2022 award, recognizing his contributions to fostering outstanding research environments. He actively engages with media, discussing topics like misinformation and data visualization. As a principal investigator, he oversees projects funded by institutions such as the Villum Foundation and Danish National Research Foundation. His research spans academic collaborations across countries, reflecting his global network and commitment to advancing network theory and its applications.

• Data systems
• Knowledge graphs
• Large-scale data analysis
• +۴ مورد دیگر

Zoi Kaoudi is an Associate Professor at the IT University of Copenhagen, affiliated with the Data, Systems, and Robotics school and the Data-intensive Systems and Applications department. Her research focuses on advancing data systems, knowledge graphs, and large-scale data analysis. She leads the Rank4QO project (2024–2027), funded by the Carlsberg Foundation, which explores query optimization using ranking algorithms. Her work emphasizes machine learning integration in data management systems, including frameworks like Apache Wayang, which unifies diverse data analytics platforms. Key collaborations include projects with Volkswagen Group and SAP, addressing dynamic graph processing and knowledge graph embeddings. Notable contributions include innovative approaches to parameter management (e.g., Good Intentions ), automated data science pipelines ( DORIAN ), and efficient graph processing algorithms. Her recent publications (2023–2025) highlight advancements in machine learning systems, distributed data processing, and adaptive optimization techniques. Current projects aim to bridge machine learning and data management through frameworks like Wayang and Dorian, with applications in air cargo revenue management and semantic web systems.

Paul Hünermundمشاهده پروفایل

استاد مدعو

• Causal Inference
• Econometrics
• Innovation Economics
• +۳ مورد دیگر

Paul Hünermund is a Tenure Track Adjunct Professor at the Department of Strategy and Innovation, Copenhagen Business School. His research focuses on causal inference, econometrics, and innovation economics, with notable contributions to strategic management methodologies and AI-driven innovation systems. Current research trends in his publications include: (1) causal inference techniques in management science; (2) impact of AI on knowledge diffusion and innovation ecosystems; (3) control variable selection in empirical analysis; and (4) post-conflict innovation dynamics. His recent work appears in journals like Econometrics Journal and Organizational Research Methods . He has contributed to debates on innovation policy, AI applications for SMEs, and research productivity challenges, with media appearances in Danish and international outlets. His work aligns with UN Sustainable Development Goals related to innovation and sustainable economic growth. Supervised work includes 6 projects, though specific advisees aren't named in the provided texts. The ORCID identifier 0000-0001-9163-038X is associated with his academic profile.

Magdalena Regina Tyzlik-Carverمشاهده پروفایل

دانشیار

• Critical Data Studies
• Digital Communication
• Curating Data
• +۷ مورد دیگر

Magdalena Regina Tyzlik-Carver is an Associate Professor of Digital Communication and Culture at Aarhus University’s Department of Digital Design and Information Studies. Her research focuses on curating data, critical data practices, posthuman theory, and new materialism, with an emphasis on digital transformation driven by AI, semantic web, and data infrastructures. She co-directs the Centre for Critical Data Practices and is affiliated with the Digital Aesthetics Research Centre and Environmental Media and Aesthetics programs. Her work includes participatory methods for data practices beyond BigTech extractivism, as well as exhibitions like Fermenting Data and Ghost Factory . She has co-authored Boundary Images (2023) and edited Executing Practices (2018). Her research explores speculative methods, open data principles, and knowledge graphs to reimagine data futures. She has curated exhibitions addressing networked images, automated systems, and digital art history. Key projects include Fermenting Data (2021), ScreenShots (2019), and Corrupting Data (2017). She holds a PhD and is an associate researcher at London South Bank University’s Centre for the Study of the Network Image. Recent publications include speculative curatorial frameworks in Companion to Curatorial Futures (2025) and explorations of distance in digital spaces ( Everything Is a Matter of Distance , 2025). Her work bridges theory and practice, advocating for inclusive, ethical data practices through exhibitions, academic writing, and collaborative research initiatives.

Theis Erik Jendalمشاهده پروفایل

پژوهشگر

• Recommender Systems
• Knowledge Graphs
• Graph Neural Networks
• +۴ مورد دیگر

Theis Erik Jendal is a Researcher at the Department of Computer Science, Aalborg University, affiliated with the Technical Faculty of IT and Design. He specializes in Recommender Systems and Knowledge Graphs, focusing on areas like explainable AI, graph neural networks, and inductive recommendation architectures. He participates in the Poul Due Jensen Professorate in Big Data and Artificial Intelligence (2019–2025), addressing challenges in query processing, knowledge graphs, semantic web, and open data. Key research interests include hypergraph models for explainable recommendation, gated architectures in knowledge graphs, and addressing challenges in knowledge graph embeddings. His work emphasizes interpretability, similarity search, and practical use cases in AI systems. Contributions include 5 peer-reviewed publications since 2020, spanning conferences like ECIR and CIKM, and a dataset contribution to the Yelp Collaborative Knowledge Graph (Zenodo, 2023). His research bridges theory and application, particularly in improving recommendation systems through advanced graph-based methodologies.

• Data Management
• Location-Based Services
• Trajectory Data
• +۷ مورد دیگر

Christian S. Jensen is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His primary research focuses on data management, spatiotemporal systems, and AI-driven solutions for mobility and cyber-physical systems. He leads projects like DiCyPS (Data-Intensive Cyber-Physical Systems) and MALOT (Managing Mobility Data Quality for Location of Things). He has published over 700 papers, with recent work emphasizing time series forecasting, trajectory analysis, and edge computing. Notable contributions include frameworks like Memory Guided Transformers and TEAM for traffic prediction. His work has been recognized with awards including the IEEE TCDE Impact Award (2019) and the Order of Dannebrog (2016). Jensen actively collaborates internationally, holding roles in organizations like the Max Planck Institute and the Villum Foundation. His research bridges theory and practice, addressing real-world challenges in smart cities, energy systems, and autonomous driving.

Ernests Lavrinovicsمشاهده پروفایل

پژوهشگر

• Machine Translation
• Transfer Learning
• Large Language Models
• +۴ مورد دیگر

Ernests Lavrinovics is a PhD fellow at the Department of Computer Science , Aalborg University , affiliated with the Technical Faculty of IT and Design and Data, Knowledge and Web Engineering research group. His work focuses on Natural Language Processing (NLP) , Large Language Models (LLMs) , and addressing challenges in machine translation and knowledge graph integration for resource-poor languages. His research explores critical NLP themes including hallucination detection in LLMs , adapter-based regularization techniques for multilingual transfer, and multitask benchmark development for creole languages. He contributes to datasets like MultiHal and CreoleVal , emphasizing semantic grounding and cross-lingual evaluation. Recent publications analyze adapter architectures as regularizers in low-resource settings (ACL 2025) He co-developed MultiHal (2025), a multilingual dataset for LLM hallucination evaluation His work spans knowledge graph applications , transfer learning , and multilingual representation challenges As an active academic, he has organized events like the Copenhagen NLP Symposium (2025) and AAU NLP Symposium (2024). He participates in technical programs at venues such as ACL and MRL Workshops , with citations in Scopus and Mendeley readership.

Mark Strembeckمشاهده پروفایل

دانشیار

• Complex Systems
• Social Network Analysis
• Emotion Detection in Social Media
• +۷ مورد دیگر

Mark Strembeck is an Associate Professor at the New Media Lab, part of the Institute for Information Systems at the Vienna University of Economics and Business (WU). He is also a faculty member at the Complexity Science Hub and a key researcher at the Secure Business Austria research center, reflecting his interdisciplinary engagement in complex systems and information security. His research focuses on the analysis of complex systems, particularly in social media environments. Key interests include emotion detection, bot-human interactions, structural communication patterns during crises, and misinformation dynamics. His work combines network science, data analytics, and computational social science to understand human behavior in digital spaces. Mark's recent publications (2017–2024) reveal a strong trend in analyzing emotional motifs, community structures, and information diffusion during high-impact events such as wars, elections, and natural disasters. His studies often involve multiplex and temporal network models applied to platforms like Twitter, Facebook, and YouTube. He has collaborated extensively with researchers such as E. Kušen and M. Moser, contributing to journals like Computers in Human Behaviour , Applied Network Science , and IEEE Internet Computing . His methodological expertise spans sentiment analysis, motif detection, and network modeling. Mark Strembeck received his doctoral degree in business informatics from WU in 2003 and completed his habilitation in 2008. He holds two diploma degrees from the University of Essen, Germany. Prior to his academic career, he worked as a software developer and researcher in Germany and Austria. Scientific Contributions and Affiliations: Associate Professor, New Media Lab, WU Vienna Faculty Member, Complexity Science Hub Key Researcher, Secure Business Austria Active contributor to COMPLEXIS, ASONAM, and SNAMS conferences He has advised no students listed in the provided text and has not received any explicitly mentioned scientific awards. His work is supported by participation in national and international research projects, though specific grants are not detailed. He is involved in teams focusing on crisis informatics, bot detection, and emotional dynamics in social networks.

Johannes Wachsمشاهده پروفایل

دانشیار

• Network Science
• Data Science
• Complexity Science
• +۱۴ مورد دیگر

Johannes Wachs is an Associate Professor at the Institute of Data Analytics and Information Science, Corvinus University of Budapest, and a Research Fellow at the Centre for Economic and Regional Studies. He is affiliated with the Complexity Science Hub Vienna, where he has been a faculty member since April 2020. His interdisciplinary research bridges data science, network science, and complexity to study digital economies, open source software, and societal challenges. PhD in Network Science, Central European University (2019) MS in Applied Mathematics, Central European University (2012) BS in Mathematics and Economics, Tulane University (2009) His research focuses on the application of network and data science to understand social, economic, and technical systems. Key interests include open source software ecosystems, AI’s impact on knowledge sharing, corruption detection, urban inequality, and digital innovation. He uses large-scale digital trace data to model complex behaviors in online communities, software development, and public policy. His recent publications reveal a strong trend in analyzing digital platforms such as GitHub and Stack Overflow, studying brain drain in tech, and assessing climate and health risks in Austria. His work combines network modeling, machine learning, and empirical analysis to uncover patterns in human behavior and systemic risk. IMF Anti-Corruption Challenge Winner (2020) Principal Investigator, CRISP Project (2021–2024), funded by FFG Grants from Hungarian Research Funding Agency (OTKA), City of Vienna, and WU Projects Johannes Wachs actively supervises PhD and Master’s students, including Hannah Schuster and Brigi Németh. He has taught courses in computational social science, social networks, and data mining at institutions including RWTH Aachen, CEU, and WU Vienna. He is launching a new MSc in Social Data Science at Corvinus in 2025. He leads the CRISP project, which builds semantic data pools for real-time crisis response and intervention, integrating heterogeneous data sources for impact forecasting and policy transparency.