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Søren Wengel MogensenView profile

Associate Professor

• Causal Inference
• Stochastic Processes
• Survival Analysis
• +3 more

Søren Wengel Mogensen is an Associate Professor at the Department of Finance, Copenhagen Business School, Denmark. His research focuses on developing advanced statistical and machine learning methodologies for complex systems analysis. Research Interests: Dr. Mogensen's work spans causal inference, stochastic processes, survival analysis, and time-series modeling. Key themes include: Causal discovery algorithms for industrial and biological systems Graphical representations of dependencies in high-dimensional data Time-varying mediation in survival contexts Bayesian networks for cascade modeling Publication Trends: His recent articles (2021-2025) demonstrate a strong emphasis on theoretical-statistical innovation with applications in healthcare, industrial monitoring, and computational finance. Dominant methodologies include kernel-based independence tests, continuous-time Bayesian networks, and constrained stochastic process modeling.

• Theoretical Computer Science
• Algorithm Design
• Computational Complexity
• +3 more

Thomas Edward Larsen is a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen. His work intersects theoretical and applied computer science, particularly in algorithm design and virtual reality-based training systems. Research focus areas include: Algorithms and Complexity Machine Learning applications Data structures for real-world problems His recent publication (2024) explores situational awareness in virtual reality maritime training simulations, combining computational methods with human-centric design.

Henrik BulskovView profile

Associate Professor

• Natural Logic Systems
• Database Querying
• Machine Learning
• +3 more

Henrik Bulskov is an Associate Professor in the Department of People and Technology (Programming, Logic and Intelligent Systems) at Roskilde University. He holds an MSc and PhD. His work focuses on natural logic systems, database querying, and ontology-based information retrieval. He has contributed to projects such as SEAFACTS (digital maritime history platform) and NorDigHealth (regional digital health solutions). Education: MSc and PhD (specific disciplines not explicitly stated) Research interests include formal logic systems integration with databases, machine learning applications in bioinformatics, and semantic summarization through ontologies. Recent work explores query optimization in natural logic knowledge bases and disparity analysis in neural networks. His publications highlight advancements in computational logic for knowledge management and biomedical text analysis. Bulskov has participated in international conferences and contributed to media discussions on big data applications. Advising: No explicit student advisees listed Grants: Principal/Co-investigator in multiple projects including SIABO (2007–2012) and Duuoo Analysis (2021) Labs/Teams: Involved in interdisciplinary teams focusing on bioinformatics, health tech, and digital humanities through collaborative projects.

Troels PedersenView profile

Associate Professor

• Wireless Communication
• Radio Channel Modeling
• MIMO Systems
• +5 more

Troels Pedersen is an Associate Professor at the Department of Electronic Systems, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on stochastic radio channel modeling, MIMO systems, radar signal processing, and positioning technologies. Key projects include WHERE2 and NEWCOM++, addressing wireless hybrid estimators and network excellence in communications. Expertise in reverberant channel modeling, propagation graphs, and Bayesian inference methods Developed models for in-room radio channels using point processes and Galton-Watson trees Contributions to MIMO radar tracking, clutter mitigation, and drone swarm micro-Doppler analysis Active in wireless positioning, cooperative networks, and IoT interference modeling with 99+ publications in journals/conferences. Supervised 1 PhD candidate and contributed to EU-funded research initiatives.

Mark Philip PhilipsenView profile

Assistant Professor

• Computer Vision
• Robotics
• Machine Learning
• +3 more

Mark Philip Philipsen is an Assistant Professor at the Department of Architecture, Design and Media Technology, Aalborg University. He holds a PhD (ErhvervsPhD) from 2017 to 2019. His research focuses on computer vision, robotics, and machine learning applications in environmental science, infrastructure, and industrial automation. He leads projects such as ReDoCO2 (2020–2025) addressing peatland CO2 emissions and Applications of Vision and Robotics in Meat Production (2017–2020), exploring automation in food processing. His work spans diverse areas, including semantic segmentation of underground utilities using 3D point clouds, peat mapping with graph neural networks, and anomaly detection in agricultural vehicles. Philipsen has contributed datasets like OpenTrench3D and LISA Traffic Light Dataset, advancing research in infrastructure analysis and vision systems. His publications emphasize practical applications of AI in environmental monitoring, robotics, and data-driven solutions for industry challenges. Key achievements include developing synthetic thermal human scenarios (ThermalSynth) and semi-supervised learning models for sewer systems. His research often bridges theory and real-world problems, with a focus on sustainability and automation.

Lisette Espín-NoboaView profile

Visiting Professor

• Computational Social Science
• Network Science
• Algorithmic Fairness
• +9 more

Lisette Espín-Noboa is a Visiting Professor at the Institute of Human-Centred Computing at Graz University of Technology and a senior postdoctoral researcher at the Complexity Science Hub (CSH) in Vienna and the Department of Network and Data Science at Central European University (CEU). Her work bridges computational social science, network science, and AI for social good, with a focus on fairness and inequality. Her research centers on algorithmic fairness in network-based systems, particularly how social structures bias ranking and recommendation algorithms. She investigates when such biases are deemed unfair, aligning with UN SDGs 5 (Gender Equality), 10 (Reduced Inequalities), and 16 (Peace, Justice and Strong Institutions). At CEU, she collaborates on generating high-resolution poverty maps using satellite imagery, mobility data, and machine learning to address SDGs 1 (No Poverty), 3 (Good Health), and 11 (Sustainable Cities). Her methodological expertise includes multimodal data fusion, graph neural networks, and network modeling. The recent articles reflect a strong trend in using computational methods to address societal challenges—particularly poverty estimation, algorithmic bias, and public health insights from digital traces. Her work consistently applies network science and machine learning to promote equity and transparency in AI systems. Scientific Awards: No awards mentioned in the provided text. Lisette has been actively involved in major research projects such as Humanized Algorithms and MAMMOth , focusing on mitigating algorithmic biases in social networks. She has led survey initiatives on network fairness in academic hiring and academic careers in Europe, indicating engagement with systemic equity issues in science. She mentors through teaching and open science, sharing materials on GitHub and launching a podcast to make her research accessible. She is affiliated with research teams at CSH, CEU, and TU Graz, contributing to collaborative efforts in algorithmic fairness and network inequality. Her work with the netin package and open-source teaching materials demonstrates a commitment to reproducible and inclusive science.

Daniele Dell'AglioView profile

Associate Professor

• Computer Science
• Knowledge Graph Embedding
• Differential Privacy
• +6 more

Daniele Dell'Aglio is an Associate Professor at the Department of Computer Science , Aalborg University , affiliated with the Technical Faculty of IT and Design . His research focuses on privacy-preserving data synthesis, knowledge graphs, and semantic data integration. Academic Rank: Associate Professor Department: Computer Science School: Technical Faculty of IT and Design University: Aalborg University Research Interests : Development of privacy metrics for synthetic data generation Heterogeneous graph representation in knowledge graphs Interactive evaluation tools for data privacy Transformer-based biomedical data extraction Integration of gut-brain axis scholarly data Scientific Awards : Best paper at the Deep Learning for Knowledge Graphs workshop (2022) Collaborative Projects include HEREDITARY (Heterogeneous semantic data integration for gut-brain interplay) as Principal Investigator, and partnerships with NASA on Mars exploration data processing. His work spans privacy-preserving technologies, knowledge graph engineering, and biomedical text mining applications.

Lei YouView profile

Assistant Professor

• Mathematical Optimization
• Distributed Data Analytics
• Data-Driven Optimization
• +9 more

Lei You is an Assistant Professor in Applied Mathematics at the Department of Engineering Technology, Technical University of Denmark (DTU), located in Ballerup, Denmark. With a Ph.D. in Computer Science specializing in Mathematical Optimization from Uppsala University (2019), Dr. You has established expertise in distributed data analytics, data-driven optimization, and machine learning interpretability. Dr. You's research focuses on the theories of interpretability and efficiency of machine learning models toward On-Device AI. Key areas of investigation include: Strategies to streamline complex models without performance loss Unraveling the intricate mechanisms of decision-making models Understanding the synergy between model simplification and explainability Distributed data analytics and federated learning systems Optimization techniques for wireless communication and edge computing Dr. You's recent publications demonstrate a strong focus on counterfactual explanations, optimal transport theory, neural network pruning, and multi-objective optimization for federated learning. The research spans applications from explainable AI to 6G-satellite systems and energy-efficient base station management. A notable trend is the integration of causal inference and distributional analysis in machine learning optimization problems, with significant contributions to federated learning efficiency and robustness. Scientific awards and recognition: Doctoral Dissertation Award for Operations Research in Telecommunications and Network Analytics awarded by INFORMS in 2020 Dr. You actively supervises student projects and research activities, with recent supervisions including work on remote sensing assessment, network pruning techniques, Microsoft Graph API optimization, and applications of large language models in business contexts. Current research projects include 'Machine Learning for Beehives Monitoring' and 'InnoTech - TaskForce,' focusing on practical applications of machine learning in environmental monitoring and digital transformation. Dr. You maintains active industry connections through previous roles at Bolt, Wolt (Doordash), and The Boston Consulting Group, ensuring research relevance to real-world challenges.

• Swarm Robotics
• Multi-Robot Systems
• Autonomous Systems
• +7 more

Anders Christensen is a Professor at the Maersk Mc-Kinney Moller Institute, University of Southern Denmark (SDU), where he leads research in swarm robotics, autonomous systems, and drone technologies. He is affiliated with the SDU UAS Center and SDU Climate Cluster, contributing to interdisciplinary projects involving environmental monitoring and AI-driven robotics. His research focuses on swarm robotics , multi-agent coordination , and UAV-based environmental monitoring . He explores decentralized control, task allocation in drone swarms, and bio-inspired autonomous systems. His work bridges theoretical algorithms with real-world applications in wildlife conservation and search-and-rescue operations. The recent publications highlight a strong trend in drone swarm deployment , communication protocols , and decentralized pathfinding , primarily applying AI and distributed computing to robotics. These works span fields such as environmental science, emergency response, and embedded systems, demonstrating interdisciplinary impact. Anders Christensen is actively involved in research projects such as WildDrone , CloudBrain , and SpikeDrone , where he contributes as a project participant or supervisor. He has secured EU and national funding, enabling field trials and technological innovation in drone-based monitoring. He teaches courses including Bio-inspired Autonomous Systems and Introduction to Robotics, Computer Vision, and Artificial Intelligence , shaping the next generation of robotics researchers.

Peter YenView profile

Research Fellow

• Condensed Matter Physics
• Topological Data Analysis
• Complex Adaptive Systems
• +9 more

Peter Yen is a Senior Research Fellow at the School of Physical and Mathematical Sciences (SPMS), Nanyang Technological University (NTU), Singapore. His research lies at the intersection of physics, data science, and economics, with a focus on applying topological methods to complex systems, particularly financial markets. He holds a Ph.D. in Condensed Matter Physics from National Central University (NCU), Taiwan. Ph.D. in Condensed Matter Physics, National Central University (NCU), Taiwan Postdoctoral experience at NCU and National Sun Yat-Sen University (NSYSU) His research interests center on complex adaptive systems , leveraging topological data analysis (TDA) and persistent homology to extract meaningful structural features from high-dimensional data. His work spans condensed matter physics , biophysics , and econophysics , with a recent emphasis on identifying topological signatures preceding financial market crashes. He also has expertise in first-principles calculations of quantum materials, including multiferroics and 2D systems. The recent publications highlight a strong trend toward interdisciplinary applications of topology , particularly in financial and economic systems. The articles demonstrate a consistent use of persistent homology to analyze time series, networks, and market dynamics, aiming to detect early warnings of systemic risk. Keywords include econophysics, data science, computational modeling, and complex networks. Currently, no scientific awards are listed. Peter Yen has not advised any students listed in the provided information, and no grant details are available. His career trajectory shows a strong research focus with affiliations at leading institutions in Taiwan and Singapore. He is actively involved in the complexity science community, participating in events such as the Econophysics Colloquium and the Complexity Science Hub Workshop, indicating engagement with international research networks.

J. Andreas BærentzenView profile

Associate Professor

• Computer Graphics
• Shape Modelling
• Real-Time Rendering
• +5 more

Professor J. Andreas Bærentzen is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), where he has held academic positions since 2001. His research focuses on computer graphics, shape modeling, real-time rendering, and geometry processing. He holds a MSc Eng (1998) and PhD (2003) from DTU. His work emphasizes topological adaptability in shape representation and efficient geometry processing, with notable contributions to structural topology optimization, 3D reconstruction, and virtual reality applications. Education: MSc Eng, Technical University of Denmark, 1991–1998 PhD, Technical University of Denmark, 1998–2003 Research Interests: Interactive 3D modeling and real-time graphics Topology optimization and structural infill design Shape modeling using distance fields and deformable hypersurfaces Applications in biomedical imaging (e.g., white matter dynamics) and environmental engineering (e.g., tree geometry simulation) His recent publications highlight advancements in neural network-based shape representation, skeletonization for tree reconstruction, and inverse-designed structural infill for engineering. He supervises multiple PhD students and leads projects on robotic manufacturing and virtual reality visualization. Bærentzen collaborates internationally, including visits to Stony Brook University (USA) and Padova University (Italy). Advisees & Projects: PhD Students: Rui Cui, Thomas D. V. Christiansen, Elias Theil Gæde Key Projects: 'Generative Methods for Brain Tissue Phantoms', 'Neural Form Representation', 'Graph Algorithms with Geometric Applications' His work bridges theoretical geometry processing with practical applications in architecture, biomedical engineering, and environmental science, leveraging DTU's interdisciplinary research ecosystem.

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.

Karsten Wedel JacobsenView profile

Professor

• Theoretical solid-state physics
• Computational materials design
• Electronic structure methods
• +3 more

Karsten Wedel Jacobsen is a Professor in the Department of Physics at the Technical University of Denmark (DTU), specializing in theoretical solid-state physics and computational materials design. His work focuses on quantum mechanical calculations for material design at the atomic scale, with applications in nanotechnology and energy-related materials. He has led major research centers like the Center for Atomic-scale Materials Design (CAMD) and contributed to open-source software like GPAW. He holds academic leadership roles, including directing CAMD and serving on DTU's educational committees. Education: PhD in Theoretical Physics (University of Copenhagen, 1987), M.Sc. in Physics (University of Copenhagen, 1984). Research Interests : Theoretical nanoscale physics, electronic structure methods, molecular electronics, and computational materials discovery. His research bridges quantum mechanics and practical applications, such as solar energy materials and catalyst design. Publications & Trends : Over 218 publications, with recent work emphasizing machine learning in materials design, high-throughput screening for 2D materials, and computational studies of catalytic interfaces. Key themes include atomic-scale simulations, defect engineering, and energy-related material discovery. Scientific Honors : Elected Member of The Danish Academy of Natural Sciences (DNA) (1994) Elected Member of Danish Academy of Technical Sciences (ATV) (2001) Reinholdt W. Jorch's Award (2004) Advising & Grants : Supervised 25 PhD students, including current advisees working on electrosynthesis, electrocatalysis, and machine learning in quantum materials. Active in research funding through grants like the Danish Research Councils and the Lundbeck Foundation. Leads interdisciplinary projects on computational modeling and open-source software development. Labs & Teams : Director of CAMD (2010–2012), a hub for atomic-scale materials design research. Collaborates globally through networks like CAMP and MIKA Advisory Board, advancing theoretical and computational methods in materials science.

Søren HøjsgaardView profile

Associate Professor

• Statistics
• Graphical Models
• Bayesian Networks
• +7 more

Søren Højsgaard is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His work bridges statistical theory, computational methods, and pedagogical innovation, with a strong focus on graphical models, Bayesian networks, and computer algebra systems in R. His research interests lie at the intersection of statistics, machine learning, and software development. He is particularly known for developing R packages such as caracas , gRbase , and sparta , which facilitate symbolic computation and efficient inference in probabilistic models. His work supports both advanced research and accessible teaching in data science. The recent publications highlight a consistent trend toward integrating symbolic mathematics with statistical computing, improving scalability in Bayesian network predictions, and enhancing statistics education through tools like Quarto and R. His contributions span theoretical algorithms, software implementation, and educational applications. Active contributor to open-source statistical software Focus on model-based prediction and symbolic computation Emphasis on teaching innovation using computational tools He has been involved in academic outreach through conference presentations and media engagement, discussing topics ranging from R programming to workplace safety modeling. His leadership role in the Department of Mathematical Sciences was recently highlighted in university communications. Søren Højsgaard leads and contributes to projects that combine rigorous statistical methodology with practical implementation, supporting both research and education in modern data science.

• 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–).