جستجوی استاد راهنمای دانشگاهی

Andrea Burattinمشاهده پروفایل

دانشیار

• Process Mining
• Business Process Management
• Petri Nets
• +۶ مورد دیگر

Andrea Burattin is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark. His work bridges formal methods and practical process analysis, focusing on process mining, business process management, and hybrid modeling techniques. He actively contributes to research in healthcare process optimization, streaming data analysis, and system verification through Petri nets and CCS transformations. UN Sustainable Development Goals: Poverty eradication, environmental protection, and prosperity for all (via process optimization) Active projects: Immersive Process Mining (2024-2027), Usability and Understandability of Hybrid Process Models (2018-2021) His research explores large language model integration with process mining, proposing frameworks like Tiramisù for multi-faceted process visualization and PN2CCS for formal model translation. Recent work emphasizes real-time monitoring, conformance checking, and IoT-driven process analytics. Key trends in his publications include: 1) Streaming process mining pipelines (2022-2025); 2) LLM-plan generation frameworks (2024); 3) Formal verification techniques (Petri nets, CCS); 4) Healthcare process modeling (2019-2023); 5) Behavioral pattern analysis in process compliance. Scientific Awards Best Demo Award (2022, 2016) Best Process Mining Dissertation Award (2014) Best Workshop Paper (EDBA and PODS4H, 2023) As advisor, he supervises PhD projects on process mining and hybrid modeling. His editorial roles include Information Systems reviewer (2024-2025) and past editor for Engineering Applications of AI (2022-2023). Collaborations span Denmark, Italy, and the Netherlands.

Philip Billeمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• String Processing
• +۹ مورد دیگر

Philip Bille is a Professor and Head of the Algorithms, Logic and Graphs section at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), College of Engineering. His research centers on the design and analysis of efficient algorithms, particularly for string processing, compressed data, and data structures. His research interests lie at the intersection of theoretical computer science and practical applications. He focuses on algorithms , data structures , string indexing , pattern matching , and compressed computation . His work enables efficient querying and processing of large-scale, repetitive data, with applications in bioinformatics, intrusion detection, and green computing. The recent publications reflect a strong trend in developing space-efficient and fast algorithms for modern computational challenges. Key themes include compressed data structures , sliding window indexing , finite automata compression , and energy-aware matrix operations . These works demonstrate expertise in balancing theoretical rigor with practical performance. Philip Bille actively supervises multiple PhD students and leads several research projects. He contributes to advancing sustainable computing aligned with UN SDGs. His work integrates algorithmic theory with real-world efficiency. Supervises PhD projects on hierarchical compression, adaptive computation, and vector processor algorithms. Involved in research on green computing, compressed formats, and efficient data models. He is affiliated with the Algorithms, Logic and Graphs group at DTU, a hub for theoretical and applied algorithmic research. The team explores fundamental problems in data representation and processing, pushing the boundaries of what is computationally feasible in terms of time and space.

• Algorithms
• Algorithmics
• Big Data
• +۷ مورد دیگر

Stephen Alstrup is a Professor in the Algorithms and Complexity section at the Department of Computer Science (DIKU), University of Copenhagen, Faculty of Science. His research bridges theoretical computer science with practical applications in modern computational challenges. His primary research interests include: Algorithm design and analysis Graph algorithms and data structures Big Data processing techniques Streaming algorithms and Internet distribution Theoretical foundations with practical implementations Alstrup's work demonstrates how theoretical algorithm research can lead to real-world applications, as evidenced by his development of Octoshape technology for large-scale Internet streaming. His research spans from fundamental theoretical problems to applications in Big Data, cloud computing, and information retrieval systems. He has published extensively with 93 research outputs including journal articles, conference proceedings, and books. His recent work focuses on graph spanners, semantic hashing, recommendation systems, and universal graph structures, showing continued productivity in theoretical computer science. Alstrup actively engages with industry and media, contributing to discussions about Big Data applications, technology innovation, and how businesses can collaborate with universities to access cutting-edge knowledge and funding opportunities. His work has been featured in 10 media contributions discussing practical applications of algorithms in education, municipal IT projects, and business innovation.

Rasmus Paghمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Big Data
• +۷ مورد دیگر

Rasmus Pagh is a Professor at the Department of Computer Science, University of Copenhagen, specializing in algorithms and complexity. His career includes a 2002 PhD from Aarhus University under Peter Bro Miltersen and a tenure at IT University of Copenhagen until 2020. He leads theoretical research with practical applications in big data, databases, and modern computer architecture parallelism. His research interests span algorithms, data structures, and privacy-preserving computing. Recent work includes the ERC-funded project on Scalable Similarity Search and contributions to the BARC center for basic algorithms research. He has collaborated with Google Research (2019-2020) and focuses on theoretical foundations with real-world impact. Key research trends in his 2023-2024 publications include privacy-preserving data analysis probabilistic data structures distributed secure computation noise-robust coding hashing efficiency continual privacy mechanisms Scientific recognition includes 2024 ACM Fellowship ERC grant leadership multiple top-tier conference publications

Chenjuan Guoمشاهده پروفایل

دانشیار

• Machine Learning
• Data Engineering
• Spatio-temporal Data Analysis
• +۴ مورد دیگر

Chenjuan Guo is an Associate Professor at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. She is affiliated with the Data Engineering, Science and Systems group and the AI for the People initiative, and is part of the Daisy - Center for Data-intensive Systems. Her research focuses on machine learning, data engineering, spatio-temporal data analysis, and time series forecasting. Key projects include the Villum Foundation-funded 'Explainable AI for Complex Microbial Community Interactions and Predictions' (2021-2024) and the Astra project on time series analytics in spatial networks (2018-2021). Her research interests span representation learning, autoencoders, path representation, outlier detection, trajectory data analysis, and time series modeling. She has supervised 3 PhD students and contributed to over 60 publications, with a recent emphasis on transformer-based forecasting, neural architecture search, and continuous learning frameworks for spatio-temporal data. Her work bridges theoretical advancements with practical applications in environmental science, cloud computing, and urban mobility systems. Key achievements include developing frameworks like AutoCTS++ for automated time series forecasting and LightGTS for lightweight models. She actively collaborates internationally, contributing to conferences like ECML PKDD and CVPR. Her research is supported by grants from the Villum Foundation and other institutions.

• Graph Theory
• Data Mining
• Network Analysis
• +۲ مورد دیگر

Aristides Gionis is a Research Professor Fellow at Aalto University specializing in advanced network analysis and data mining. His work bridges theoretical graph algorithms with practical applications in dynamic and signed networks. His research focuses on graph theory and temporal network analysis , particularly in discovering dense subgraphs, community structures, and event patterns in evolving networks. Key methodologies include contrastive subgraph mining for explainable AI in brain networks and polarization detection in social systems. His publication trends reveal consistent contributions to top-tier venues like KDD, PAKDD, and ECML PKDD, with emphasis on algorithmic solutions for network segmentation, correlation mining, and interpretable classification. Primary application domains include social networks, neuroscience, and temporal data streams. As a Fellow at Aalto University, he leads research in computational network science without explicit departmental affiliation stated in available sources.

Yan Zhaoمشاهده پروفایل

دانشیار

• Anomaly Detection
• Autoencoder
• Attention (Machine Learning)
• +۵ مورد دیگر

Yan Zhao is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, science, and systems, with a particular emphasis on anomaly detection, machine learning, and spatio-temporal data analysis. He holds a Ph.D. in Computer Science (specific education details not explicitly provided). Research Interests: Dr. Zhao's work spans anomaly detection, autoencoders, attention mechanisms, preference learning, multivariate time series, and computational efficiency. His research often integrates machine learning with real-world applications in spatial crowdsourcing, trajectory analysis, and data privacy. Publications: With 72+ publications, his recent work emphasizes spatio-temporal prediction frameworks, federated learning, and efficient time series analysis. Notable contributions include frameworks for continuous learning on streaming data and privacy-preserving clustering in spatial crowdsourcing. Grants & Supervision: He has supervised one Ph.D. student and actively contributes to research grants focusing on data engineering and smart systems. His work bridges theoretical advancements with practical applications in transportation, social networks, and IoT.

• Greenland
• Climate
• Climate change
• +۵ مورد دیگر

Sune Olander Rasmussen is an Associate Professor at the Niels Bohr Institute , University of Copenhagen , specializing in Ice, Climate and Geophysics . With a background in geophysics, mathematics, and complex systems dynamics, his work focuses on integrating large ice core datasets and developing tools for climate data analysis. Research Interests : Greenland ice core studies, climate change, Dansgaard-Oeschger events, volcanic eruption impacts on climate, firn seismic properties, and probabilistic age modeling. Scientific Contributions : Key developer of the GICC05 time scale, with significant publications in Climate of the Past , Nature Communications , and PNAS . Awards : Carlsberg Foundation Distinguished Associate Professor. Leadership Roles : Chair of the NEEM dating consortium (since 2008) and the INTIMATE research network (since 2011).

Thor Grünbaumمشاهده پروفایل

استاد

• Philosophy of Action
• Philosophy of Mind
• Philosophy of Science
• +۵ مورد دیگر

Thor Grünbaum is a Professor at the Department of Communication and an Associate Professor at the Department of Psychology, both within the University of Copenhagen. He is Head of the Cognition, Intention, and Action Group (CoInAct) and Head of Study for the BSc in Cognitive Data Science program. His work bridges theoretical philosophy and empirical cognitive science. Research Interests: Philosophy of action/mind, philosophy of psychological science, and cognitive science. Specific areas include intention selection, agency, prospective memory, movement consciousness, and the methodology of well-being research. His recent publications focus on intention selection mechanisms, memory systems, and the philosophical implications of cognitive neuroscience. He has secured major grants from the Carlsberg Foundation and Danish Research Council. Teaching: Supervises in theoretical philosophy (philosophy of mind, epistemology, metaphysics) and cognitive science. Service: Executive secretary of the European Society for Philosophy and Psychology and associate editor for multiple journals.

Joel Daniel Anderssonمشاهده پروفایل

پژوهشگر ارشد

• Differential Privacy
• Continual Observation
• Streaming Algorithms
• +۳ مورد دیگر

Joel Daniel Andersson is a Research Fellow at the Department of Computer Science , University of Copenhagen, affiliated with the Faculty of Science . He is a member of the Providentia group under the supervision of Rasmus Pagh , focusing on differentially private algorithms in the continual observation setting. His work bridges theoretical challenges in privacy-preserving data analysis with practical applications in machine learning and statistics. PhD in progress (third-year) at the University of Copenhagen Former Master's student in Engineering Physics (Lund University, 2020) Previous industry experience: Software Engineer at Ericsson (5G protocols) Research Interests Joel's research explores differential privacy in streaming algorithms , particularly for continual observation —a framework where private statistics must be released incrementally over time. He investigates optimal solutions for problems like binary continual counting, comparing noise mechanisms (Laplace vs. Gaussian), and designing efficient algorithms for dynamic data streams. Earlier work includes computational modeling of beam dynamics at CERN for high-energy physics applications. Recent Publications His publications span theoretical computer science and computational physics , appearing in venues like NeurIPS , ICML , FORC , and physics journals. Key themes include: Differential privacy in streaming and machine learning Algorithmic design for continual data release Computational modeling for particle accelerators Privacy expiration and noise mechanism trade-offs Affiliations Joel collaborates with: BARC (Basic Algorithms Research Copenhagen) group Providentia group Adam Smith's lab at Boston University (visiting researcher, Jan–Apr 2025)

• Digital Humanities
• Media Studies
• Political Science
• +۹ مورد دیگر

Johan Lau Munkholm is a Postdoc and Research Fellow at the Department of Culture and Language, University of Southern Denmark. His work bridges Digital Humanities , Media Studies , and Political Science , focusing on Data Privacy , National Security , and Technology Ethics . His research explores the socio-political implications of digital platforms, privacy policies, and surveillance systems. Key themes include GDPR compliance and platform governance Cultural hegemony in streaming services like Spotify Algorithmic accountability in corporate data practices Surveillance studies and geopolitical security Recent publications span topics such as synthetic data ethics (2025), Spotify's aesthetic dominance (2024), and platform power dynamics (2023). He is also involved in the ALIFE project (2023–2026), which examines AI, biomachines, and climate change.

John Peter Merryman Boncoriمشاهده پروفایل

استاد

• Remote Sensing
• Synthetic Aperture Radar (SAR)
• Glaciology
• +۴ مورد دیگر

John Peter Merryman Boncori serves as Head of Microwaves and Remote Sensing at the Department of Space Research and Technology, Technical University of Denmark (DTU). His research directly contributes to UN Sustainable Development Goals through advanced Earth observation techniques. His research spans Remote Sensing , Synthetic Aperture Radar , and Glaciology , with specific expertise in InSAR processing for tectonic monitoring and ice sheet dynamics. Key focus areas include: Greenland Ice Sheet velocity measurement using SAR Multi-scale analysis of active fault systems in Italy Validation of ground motion services using GNSS Subglacial hydrology modeling in polar regions Dr. Boncori actively supervises PhD research in critical areas: Crowd counting via remote sensing (CroCo project) Field-scale soil moisture mapping InSAR error characterization Ship tracking with iceberg discrimination Ice velocity retrieval using TOPS-mode SAR He leads the Microwaves and Remote Sensing group at DTU Space, developing advanced methodologies for radar interferometry and satellite-based deformation monitoring. His team maintains strong international collaborations across Europe for Earth observation projects, with particular emphasis on cryosphere and tectonic applications.

Andres Masegosaمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Networks
• Data Streaming
• +۴ مورد دیگر

Andres Masegosa is an Associate Professor at the Department of Computer Science, Aalborg University (AAU), within The Technical Faculty of IT and Design. He is actively involved in the DarkScience project (2022–present), focusing on metagenomic binning and microbial dark matter analysis. His research interests span Bayesian networks, machine learning, probabilistic graphical models, and educational methodologies in computer science instruction. Key research contributions include advancements in PAC-Bayes theory, genome representation learning, and cold posterior effects in Bayesian models. He has published extensively in top venues like Advances in Neural Information Processing Systems and Transactions on Machine Learning Research. His work often bridges theoretical contributions with practical applications in genomics and education. Masegosa leads the development of tools like InferPy for probabilistic modeling and has contributed to open-source projects such as the AMIDST toolbox. His educational research explores learning styles and active learning strategies, emphasizing live coding and programming exercises. Collaborations include interdisciplinary projects with microbiologists and data scientists, reflecting his expertise in computational methods for complex biological systems. His research portfolio demonstrates a strong focus on scalable probabilistic methods and their real-world applications.

Louise Yung Nielsenمشاهده پروفایل

دانشیار

• Digital Media
• Platform Economy
• Influencer Culture
• +۵ مورد دیگر

Louise Yung Nielsen is an Associate Professor in the Department of Communication and Arts at Roskilde University. Her research focuses on digital media, including digital work environments, platform economies, influencers, gamers, digital embodiment, and visual digital culture. Research interests include: Digital labor and affective economies Embodied online practices Social media's cultural impact Transactional performances in streaming She actively contributes to media and cultural studies through research projects, editorial work for Conjunctions – Transdisciplinary Journal of Cultural Participation , and public commentary on digital culture topics.

Rasmus Rex Pedersenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Datafication
• Cultural Industries
• +۵ مورد دیگر

Rasmus Rex Pedersen is an Associate Professor at Roskilde University's Department of Communication and Arts. He co-leads the Artificial Intelligence and Datafied Communication (AIDa) research group and contributes to the Centre for Digital Citizenship. His work bridges media and communication studies with sociological research on cultural production. Focuses on AI's impact on cultural industries Applies qualitative and mixed digital methods Collaborates with music industry stakeholders Investigates algorithmic governance and datafication His research output spans 27 publications, including book chapters and reports analyzing AI's role in creative labor and cultural policy frameworks. Recent work examines AI-generated music, music streaming economics, and platform governance. Current projects include The Future of Cultural Policy (2023-2025), exploring evidence-based policy evaluations in cultural sectors.