Find Academic Supervisors

Peter Gjøl JensenView profile

Associate Professor

• Model Checking
• Formal Verification
• Reinforcement Learning
• +8 more

Peter Gjøl Jensen is an Associate Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. His research spans formal methods, artificial intelligence, and practical applications in energy systems. He is affiliated with multiple research groups including Distributed, Embedded and Intelligent Systems, AI for the People, and Artificial Intelligence and Machine Learning. His research interests focus on Model Checking , Formal Verification , Reinforcement Learning , and applications to Cyber-physical Systems . He has developed expertise in applying theoretical computer science to solve real-world problems, particularly in energy optimization and heat pump control systems. His work bridges the gap between formal methods and practical engineering applications. His recent publications show a clear trend toward applying AI and formal verification techniques to energy systems and cyber-physical applications. The research spans theoretical foundations of model checking and reaches into practical implementations for heat pump control, autonomous systems, and environmental management. His work often involves the UPPAAL and Stratego frameworks for verification and synthesis. Dr. Jensen actively supervises PhD students, including Andreas Holck Hoeg-Petersen on the "Explainable and Causally Enforced Reinforcement Learning" project. His research has attracted media attention, particularly for intelligent heat pump control systems that provide significant energy savings. He is involved in multiple research projects including "Explainable and Causally Enforced Reinforcement Learning" (ongoing) and "BEO-COVID: Decision Support for Evaluation and Optimization in UPPAAL" (completed in 2020).

Theresia Veronika RampiselaView profile

Researcher

• Algorithmic Fairness
• Recommender Systems
• Information Retrieval
• +3 more

Theresia Veronika Rampisela is a PhD student and Guest Researcher in the Department of Computer Science at the University of Copenhagen's Faculty of Science, specializing in Machine Learning. She actively contributes to the Algorithms, Data, and Democracy (ADD) project (https://algorithms.dk/), which investigates the societal implications of algorithmic decision-making systems. Her academic background includes a Master's degree in Computer Science from the University of Indonesia, providing foundational expertise in computational methods and data analysis. This education has enabled her transition from early work in information retrieval to specialized research in algorithmic fairness. Rampisela's research program critically examines fairness evaluation in machine learning applications, with particular focus on recommender systems. She investigates both group and individual fairness metrics, exploring their practical implementation challenges and limitations. Her work addresses the fundamental tension between recommendation relevance and equitable treatment of diverse user populations. Earlier research included academic expert finding using semantic techniques and medical applications involving schizophrenia classification with SVMs. Analysis of her publication trajectory reveals progressive sophistication in algorithmic fairness research. Starting with foundational work on expert finding systems, she has advanced to developing novel frameworks like Pareto optimization for balancing competing objectives in recommendation algorithms. Her publications in top venues including ACM Transactions on Recommender Systems and the ACM SIGIR Conference demonstrate growing recognition in this critical field. Mensa International Scholarship (2024) Rampisela collaborates extensively with Maria Maistro, Tuukka Ruotsalo, and Christina Lioma through the ADD project. Her research has significant practical implications for technology companies developing recommendation systems and policymakers regulating algorithmic fairness. She maintains an active scholarly profile documented through her ORCID (https://orcid.org/0000-0003-1233-7690) and demonstrates growing influence with mentions across academic social platforms.

• Differential Privacy
• Algorithms
• Data Streams
• +3 more

Christian Janos Lebeda serves as a Guest Researcher in the Department of Computer Science at the University of Copenhagen, specializing in the Algorithms and Complexity group with a focus on theoretical privacy mechanisms. His research centers on differential privacy , particularly developing efficient algorithms for private statistical estimation in data streams. Key contributions include variance-aware mean estimation techniques and privacy-preserving histogram construction using sketching methods like Misra-Gries, addressing fundamental challenges in high-dimensional data analysis. Recent publications demonstrate a cohesive research trajectory advancing privacy guarantees in streaming algorithms, with work appearing in premier venues such as PODS and Proceedings on Privacy Enhancing Technologies. This reflects growing expertise at the intersection of theoretical computer science and practical privacy preservation for big data systems.

Bin YangView profile

Professor

• Computer Science
• Data Management
• Machine Learning
• +9 more

Bin Yang is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, artificial intelligence, and machine learning, with emphasis on spatiotemporal data analysis, representation learning, and time series forecasting. He leads projects like aSTEP (spatio-temporal data analytics) and Light-AI for Cognitive Power Electronics. Key contributions include advancements in trajectory data processing, anomaly detection, and lightweight neural architectures. Research interests span machine learning, data mining, intelligent transport systems, and material science applications. Notable awards include the IJCAI 2019 Distinguished PC Member and Sapere Aude Research Leader (2018). He has supervised six PhD students and contributed to over 119 publications. His work is supported by grants from Villum Foundation and EU initiatives. Yang is involved in interdisciplinary collaborations, including data-driven decision-making frameworks and environmental monitoring systems. Labs and teams include the Daisy Center for Data-intensive Systems and AI for the People initiatives. Projects emphasize real-world applications in smart cities, traffic forecasting, and sustainable technologies. His research bridges theoretical foundations with practical implementations in domains like oceanography and crowdsourcing systems.

Simonas SaltenisView profile

Associate Professor

• Database Systems
• Spatial Data Management
• Data Engineering
• +3 more

Simonas Saltenis is an Associate Professor in the Department of Computer Science at Aalborg University, affiliated with The Technical Faculty of IT and Design. He is part of the Data Engineering, Science and Systems group and the Daisy Center for Data-intensive Systems. His research focuses on database systems, spatial data management, and data engineering with a strong emphasis on temporal and spatial query optimization. He has been involved in key projects such as TimeCenter (active since 2000) and "Effective Shedding and Efficient Processing of Massive Sensor Update Loads" (2008–2011), both funded by Danish research councils. These projects explore database management systems, temporal data handling, and real-time sensor data processing. His research interests include indexing techniques for spatial and temporal data, points-of-interest retrieval, and probabilistic models for energy use prediction. He has also contributed to conference organization, serving as a general co-chair for MDM 2018. Saltenis has supervised at least one PhD student and has actively engaged in peer review for top journals including the VLDB Journal, IEEE Transactions on Knowledge and Data Engineering, and ACM Transactions on Information Systems.

Tianyi LiView profile

Assistant Professor

• Digital Twin
• Graph Neural Networks
• Energy Engineering
• +8 more

Tianyi Li is a Tenure Track Assistant 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. His research focuses on data engineering, spatiotemporal systems, and digital twins, with applications in energy, transportation, and vehicular networks. Key areas include trajectory data management, graph neural networks, compression techniques, and federated learning. He has contributed to over 37 publications, including works on digital twin frameworks for autonomous driving and smart grids, trajectory prediction systems, and efficient time series compression. His research integrates machine learning, distributed systems, and cybersecurity to address challenges in data-intensive environments. Key Projects: Digital twin-enabled voltage control, federated vehicular networks, trajectory simplification frameworks. Labs: Involved with the Daisy Center, focusing on data-centric systems and scalable solutions. Technical Expertise: Blockchain systems, energy systems optimization, and real-time data processing.

Sigmundur VangView profile

Research Fellow

• Data Engineering
• Data Science
• Data Warehousing
• +3 more

Sigmundur Vang is a PhD Fellow at the Department of Computer Science within The Technical Faculty of IT and Design at Aalborg University, Denmark. His research focuses on data engineering systems with emphasis on data cube implementation and query optimization. His primary research interests span Data Engineering , Data Science , and OLAP Systems , evidenced by his fingerprint analysis showing strong associations with Data Cubes (100%), Star Schema (25%), and Data Warehousing (12%). His work bridges theoretical database concepts with practical Python implementations for big data analytics. His recent publication demonstrates a trend toward accessible data cube tooling in Python, targeting data scientists and analysts working with complex data landscapes. The research output indicates active contributions to conference proceedings in data analytics. No scientific awards or formal advising responsibilities are currently documented in available sources. His operational base is at Selma Lagerløfs Vej 300, 2-1-16, 9220 Aalborg Ø, Denmark, where he contributes to the Data Engineering, Science and Systems research group.

• Database Systems
• Distributed Systems
• Event-Based Systems
• +5 more

Yongluan Zhou is a Professor at the Department of Computer Science , University of Copenhagen , where he co-heads the Data Management Systems Lab (DMS Lab) and serves as Head of Studies for the MSc in Computer Science . His academic journey includes a PhD from the National University of Singapore (NUS) (2007), a postdoc at ETH Zürich (2007–2008), and prior roles as Associate Professor at University of Southern Denmark (SDU) (2008–2017). PhD in Computer Science, National University of Singapore (2002–2007) Postdoc, ETH Zürich (2007–2008) Zhou's research focuses on database systems and distributed systems , with recent emphasis on event-driven systems , scalable stream processing , and big graph analysis . His work bridges theoretical foundations and practical implementations, addressing challenges in data consistency, fault tolerance, and resource optimization in cloud and microservice environments. The trends in his 15 most recent publications (2025–2024) highlight advancements in asynchronous choreographies , blockchain consensus protocols , GPU-accelerated graph processing , and microservices data management . These works integrate formal methods with empirical validation, emphasizing scalability, security, and efficiency in distributed environments. He actively contributes to academic governance as a member of the DEBS Steering Committee (2024–), SSDBM Steering Committee (2022–), and the EDBT Association Executive Board (2020–).

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +3 more

Lennard Hilgendorf is a Researcher at the Department of Computer Science , University of Copenhagen . His work focuses on Machine Learning with applications in quantum computing, medical imaging, natural language processing, and environmental sustainability. Research Trends: His recent publications highlight interdisciplinary work at the intersection of quantum mechanics and machine learning, efficient AI architectures for environmental sustainability, explainable models for medical diagnostics, and multimodal approaches to ecological monitoring. Key keywords include Machine Learning , Quantum Computing , Medical AI , and Environmental Science . Labs & Collaborations: Affiliated with the SCIENCE AI Centre and the TreeSense Centre , which specialize in foundational machine learning research and remote sensing for global tree resources, respectively.

• Machine Learning
• Computer Science
• Natural Language Processing
• +3 more

Mikolaj Tymon Mazurczyk is a Research Fellow at the Department of Computer Science, University of Copenhagen. His research intersects with machine learning and computer science, focusing on areas such as natural language processing, medical image analysis, and biological data modeling. The Machine Learning Section at DIKU explores both theoretical and applied aspects, including information retrieval, medical data analysis, remote sensing, and sustainability. They participate in the SCIENCE AI Centre and collaborate with various research groups. Dr. Mazurczyk's recent publications highlight advancements in optical neural networks, sustainable AI, and quantum computing applications. His work also addresses fairness in recommender systems and interpretability in large language models, as well as clinical informatics and biomedical data analysis.

• Computer Science
• Data Management
• Indoor Location-Based Services
• +4 more

Tiantian Liu is an Assistant Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. Their research focuses on indoor location-based services, trajectory analysis, and spatial data management. Key areas include indoor navigation algorithms, temporal-variation-aware path queries, and uncertainty-aware contact tracing systems. Education details are not explicitly provided in the text. The researcher has collaborated on the Data Management Foundations for Indoor LBS project (2019-2021), contributing to foundational work in indoor spatial databases. Research interests span computer science , data management , and spatial computing . Notable contributions include keyword-aware routing systems (IKAROS), robust trajectory similarity computation (CLEAR), and time-constrained indoor pathfinding frameworks. Their work integrates machine learning, database systems, and algorithmic design to address challenges in indoor environments. In 2021, Liu co-authored the SSTD 2021 Best Paper Candidate . Their research emphasizes practical applications such as crowd-aware path planning and uncertainty modeling in indoor positioning data. Current work explores continuous similarity search for vessel trajectories (2025) and novel methods for handling temporal variations in indoor environments. The researcher's lab focuses on intelligent building systems and context-aware spatial analytics.

Eleni Tzirita ZacharatouView profile

Assistant Professor

• Spatial Data Management
• Geospatial Analytics
• Query Processing
• +1 more

Eleni Tzirita Zacharatou is Assistant Professor in the Department of Computer Science at the IT University of Copenhagen. Her research develops efficient data management tools for modern analytics workloads, with specialization in spatial data processing and geospatial applications. She leads projects on query optimization, spatial indexing, and distributed geocomputation. Her research focuses on improving the efficiency of spatial data analytics through novel indexing structures, approximation techniques, and hardware acceleration. Key areas include spatial join optimizations, geospatial stream processing, and scalable earth observation data systems. She develops specialized algorithms for polygon processing, raster operations, and spatiotemporal indexing. Dr. Zacharatou's publications demonstrate innovations in geospatial query processing, including raster-based approximations for spatial joins, distributed execution of zonal statistics, and FPGA-accelerated sketching techniques. Her work bridges database systems research with practical applications in earth observation, IoT monitoring, and transportation analytics. Awards & Recognition: Distinguished Reviewer (VLDB 2022) SIGMOD Best Demonstration Award (2018) She leads the GeoSpatial Systems Lab, developing open-source tools for processing massive geospatial datasets. Current projects include distributed spatial aggregation frameworks and learned indexing techniques for earth observation platforms.

• Theoretical Computer Science
• Formal Methods
• Programming Languages
• +5 more

Luís Cruz-Filipe is an Associate Professor at the Department of Mathematics and Computer Science (IMADA) within the Faculty of Engineering at the University of Southern Denmark. His research spans theoretical computer science with a focus on formal methods, programming languages, and distributed systems. He maintains an active research profile with numerous publications in top venues. His research interests primarily center around choreographic programming, formal verification, sorting networks, and logic programming. Cruz-Filipe has made significant contributions to the theoretical foundations of choreographic programming, developing formal frameworks for specifying and verifying distributed communication protocols. His work on sorting networks has advanced the understanding of optimal network structures, while his research in approximation fixpoint theory has provided formal foundations for logic programming semantics. Analysis of his recent publications reveals a strong trend toward formal methods applied to distributed systems, particularly in choreographic programming where he has developed theoretical frameworks, verification techniques, and compilation approaches. His work bridges theoretical computer science with practical implementation concerns, often using proof assistants like Coq to ensure correctness. More recently, he has expanded into sustainability topics with work on green software development. As an academic, Cruz-Filipe has contributed significantly to the theoretical foundations of choreographic programming, producing a cohesive body of work that has helped establish this field. His publications demonstrate a consistent focus on formal methods to address challenges in distributed systems, with increasing attention to practical implementation and verification.

DaisyView profile

Researcher

• Data Management
• Data Engineering
• Big Data
• +3 more

Daisy is a research center at Aalborg University under The Technical Faculty of IT and Design, specializing in data-intensive systems. It focuses on data management challenges in IoT, sensor networks, and digital systems, collaborating with industries to drive innovation. The center emphasizes interdisciplinary research and practical applications through projects like aSTEP and Streamspin. Research Focus: Key areas include query processing, trajectory data analysis, data warehouses, social network analysis, and location-based services. The center addresses modern data challenges with experimental and theoretical approaches. Collaborations: Engages in partnerships with global institutions and industries, contributing to projects such as the Inverse Design of Materials Using Diffusion Probabilistic Models. The center has 58 active projects and over 1,350 publications across disciplines. Awards: While individual awards are not listed for Daisy, affiliated researchers have received accolades like the SSTD 2021 Best Paper Candidate and Honorary Doctorates. Infrastructure: Hosts datasets like the 2022 ACM SIGMOD Conference tutorial on spatial data quality and maintains a strong presence in academic and industrial networks.

Juan Manuel RodriguezView profile

Assistant Professor

• Recommender Systems
• Web Services
• Deep Learning
• +3 more

Juan Manuel Rodriguez is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. His affiliation is within The Technical Faculty of IT and Design, specializing in Data, Knowledge, and Web Engineering. Dr. Rodriguez holds a PhD in Computer Science and has a strong focus on research areas including Recommender Systems, Web Services, Deep Learning, and Social Media Analysis. His work often intersects with applications in information retrieval, healthcare systems, and crisis management. His research explores topics such as misinformation detection, transformer-based news recommendation, and clinical data analysis using knowledge graphs. He has contributed to projects like HEREDITARY (2024–2027), which integrates heterogeneous data for gut-brain research. Rodriguez has also engaged in media collaborations, including work highlighted in 'Udforsker Mars: Studerende fra Aalborg samarbejder med NASA' (June 2024). Key technical interests include leveraging neural networks for user behavior modeling, optimizing retrieval systems, and addressing ethical challenges in AI. His recent publications (2020–2025) span domains from pandemic mental health tracking to parking availability prediction, reflecting a multidisciplinary approach to computational challenges.