Jiri Srba is a Professor at Aalborg University's Department of Computer Science, part of the Technical Faculty of IT and Design. He leads research in the Distributed, Embedded and Intelligent Systems group and contributes to projects like "ControLing wAter In an uRban Environment" and "Collective Adaptive System SynThesIs using Non-zero-sum Games". His office is located at Selma Lagerløfs Vej 300, 9220 Aalborg Øst, Denmark. Contact him at +4599409851 or srba@cs.aau.dk. His core research focuses on formal methods and applied computer science: Model checking and verification of concurrent systems Petri nets and their applications Network protocol verification and synthesis Distributed system correctness Automated reasoning for industrial systems His publication record shows strong emphasis on network verification, model checking optimization, and applying formal methods to environmental systems. Recent work integrates computer science with sustainable engineering, particularly in water management systems and energy control.
Andreas Møgelmose is an Associate Professor at Aalborg University's Department of Architecture, Design and Media Technology under the Technical Faculty of IT and Design. His research focuses on computer vision, artificial intelligence, and their applications in autonomous systems, driver assistance, and industrial vision. He leads projects like AI Color Fashion and Real-world adaption of generative AI for architecture. Møgelmose teaches introductory programming, computer vision, and advanced master's courses, emphasizing practical project-based learning. His work includes developing datasets such as the Multi-view Traffic Intersection Dataset (MTID) and exploring vision-language models for autonomous vehicle safety. He actively engages in media discussions on AI ethics and societal impacts. Research interests span dynamic gesture interpretation for cooperative autonomous vehicles, surgical skill assessment via automated metrics, and multimodal classification of environmental data. He has contributed to over 50 publications, including work on 3D object detection frameworks and generative AI education. Møgelmose collaborates on projects funded by industry partners like COWI and Danish government initiatives. His teaching philosophy centers on blended learning and practical application, fostering innovation in AI and computer vision education. Notable projects include AI:Xpertise Lab (2025–present), which explores AI-driven expertise systems, and collaborations on forest biodiversity analysis using LiDAR and orthophotos. His recent media engagements highlight societal AI challenges, emphasizing responsible implementation in public sectors.
Thomas Dyhre Nielsen is a Professor (MSO) in the Department of Computer Science at Aalborg University . He is a member of the Distributed, Embedded and Intelligent Systems (DEIS) research group and contributes to the Artificial Intelligence and Machine Learning team at the university. His primary research interests lie in the theoretical and applied aspects of probabilistic graphical models , machine learning , and deep generative models . His work spans from foundational methods for learning and inference to the development of frameworks for solving complex decision problems. He is the co-author of the authoritative textbook Bayesian Networks and Decision Graphs . His recent research output reveals a strong trend towards interdisciplinary applications. His 15 most recent publications demonstrate significant work in bioinformatics , using graph neural networks and variational autoencoders for metagenomic binning, and in urban infrastructure , applying reinforcement learning and probabilistic models to optimize stormwater and wastewater management systems. He also has impactful research in healthcare , developing Bayesian network models for clinical risk prediction. Senior area editor for the International Journal of Approximate Reasoning . Principal or Co-Investigator on research projects such as the AMIDST project (developing a Java toolbox for scalable probabilistic machine learning) and PGM 2020 (organizing the International Conference on Probabilistic Graphical Models). Actively supervising research, as evidenced by a current opening for a PostDoc position in probabilistic machine learning. Professor Nielsen leads a research lab focused on probabilistic machine learning, which is part of the larger DEIS and AI/ML teams at Aalborg University. His group develops and applies advanced models to real-world problems in environmental science, healthcare, and intelligent systems.
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.
Elisa Bjerre is an Assistant Professor in the Department of Geosciences and Natural Resource Management at the University of Copenhagen, specializing in geology and hydrological systems. Her research integrates advanced computational methods with environmental science to address critical water resource and climate change challenges. Her research focuses on hydrology , glaciology , and machine learning applications in environmental systems. Key areas include groundwater protection modeling, Greenland ice sheet dynamics, and spatial transferability of predictive models. She develops innovative approaches combining remote sensing data with machine learning to improve water resource modeling and climate impact assessments. Analysis of her publication record reveals strong emphasis on predictive modeling for drainage systems, geological uncertainty quantification in decision-making, and long-term climate trend analysis in polar regions. Her work bridges technical hydrological modeling with practical environmental policy applications, particularly in drinking water protection and sustainable resource management. Her collaborative research spans international projects with significant contributions to understanding Greenland ice sheet warming trends and developing spatially explicit water resource models. Current work demonstrates increasing integration of machine learning with traditional geophysical methods to enhance prediction accuracy in complex environmental systems.
Mads Lykke Dømgaard is a Postdoctoral Researcher at the Department of Geosciences and Natural Resource Management within the Faculty of Science at the University of Copenhagen. His research focuses on glaciology, particularly studying glaciers in Antarctica and Greenland using historical aerial images and satellite data to understand past glacier fluctuations and expand knowledge on historical glaciology. His educational background includes: PhD in Geosciences (2024) from the University of Copenhagen, with dissertation titled "Remote Sensing of the Cryosphere: Expanding Observations on Glacier Dynamics and Related Glacial Phenomena" Dømgaard's research interests center on historical glaciology, utilizing archival aerial photographs and modern satellite data to reconstruct glacier changes over decades. His work primarily examines Antarctic and Greenland ice sheets, focusing on glacier dynamics, ice shelf collapse, and the impacts of climate change on polar regions. He employs advanced remote sensing techniques including satellite altimetry and photogrammetry to analyze long-term cryospheric changes, with particular attention to ice-marginal lake systems and glacier surge mechanisms. His recent publications demonstrate a strong trajectory in polar research, with multiple high-impact papers in Nature Communications and The Cryosphere. The research shows increasing integration of historical data sources with contemporary satellite observations to understand century-scale glacier behavior and their response to climate forcing, with significant attention to both Antarctic and Greenland ice systems. Dømgaard is currently working on the AIR ANTARCTICA Project funded by the VILLUM FOUNDATION, which began as his PhD project in May 2021 under the supervision of Anders Anker Bjørk. His research has received substantial media attention, with publications picked up by over 70 news outlets, blogged about extensively, and widely discussed on social media platforms including X (formerly Twitter) and Reddit. His work has also been referenced in Wikipedia and academic platforms like Mendeley.
Lars Bodum is an Associate Professor at the Department of Sustainability and Planning, part of The Technical Faculty of IT and Design at Aalborg University. His research focuses on geoinformation, urban development, and digital technologies, particularly 3D modeling, AI, and geovisualization for sustainable cities. He co-authored Denmark’s foundational GIS textbook and leads projects like the award-winning The Digital Underground , exploring subsurface infrastructure data. Education: PhD in Urban Planning (2000) and MSc in Chartered Surveyor (1990), both from Aalborg University’s Department of Sustainability and Planning. Research interests include data integration, Citizen Science, and Digital Twins. His work bridges academia with municipal and private sector applications, emphasizing practical solutions for infrastructure management and climate adaptation. Key projects include Effektiv anvendelse af 3D-scanninger (2024–2026) and NBRACER (2023–2027), focusing on climate resilience and nature-based solutions. He has received the 2021 Outstanding Review Award for peer review contributions. Labs/Teams: Danish Centre for Spatial Planning and the Centre for 3D GeoInformation. Collaborations span Denmark and international partners like the University of California, Davis.
Michael Welch is a Senior Researcher at the Danish Offshore Technology Centre (Technical University of Denmark) specializing in Geology and Geophysics. His research focuses on fracture mechanics, fluid flow dynamics, and geomechanical modeling in subsurface environments. Research Interests: Welch investigates fracture propagation across material interfaces, subcritical crack growth, and fluid flow variations in fractured reservoirs. His work integrates seismic data analysis, discrete fracture networks (DFN), and phase-field modeling techniques. Key areas: Fracture network validation, geomechanical controls on chalk/marl reservoirs, mechanical failure risk assessment for CCS operations Methodologies: Linear Elastic Fracture Mechanics (LEFM), poroelastic modeling, photogrammetry hardware development Collaborations: Active in international projects related to North Sea geology, Netherlands diapir structures, and Danish Central Graben analysis.
Lasse Hedegaard Hansen is a Research Fellow in Geoinformatics at Aalborg University's Department of Sustainability and Planning. His research develops visualization methods for subsurface infrastructure using augmented reality and 3D capture technologies. Current projects include 'The Digital Underground' focusing on data integration for utility networks, and 'OpenTrench3D' creating benchmark datasets for semantic segmentation. Field applications include AR solutions for reducing excavation damages through improved utility visualization. Awards include Best Research Demo (2021) for innovations in AR utility mapping. International collaborations address challenges in heterogeneous data integration for infrastructure management.
Michael R. Rasmussen is a Professor and Head of Academic Council at the Department of the Built Environment within Aalborg University's Faculty of Engineering and Science. His research focuses on urban hydrology, environmental engineering, and the application of meteorological radar for rainfall estimation and stormwater management. He leads the Urban Hydrology Research Group and has been involved in numerous projects addressing water quality, flood mitigation, and sustainable urban infrastructure. Key research contributions include advancing methods for integrating weather radar data with rainfall sensors to improve urban drainage system design and real-time control strategies. He has supervised four PhD students and contributed to over 180 publications, including influential works on combined sewer overflow management and bathing water safety. Rasmussen actively participates in academic and industry collaborations, serving on boards such as Dryp A/S and the Department of the Built Environment. His work bridges hydraulic engineering, environmental science, and data-driven solutions, with applications in Denmark and beyond. Notable projects include optimizing stormwater detention ponds using control algorithms and developing models to predict water quality impacts from fecal contamination in lakes.
Chiara Villa is an Associate Professor in the Section of Forensic Pathology at the University of Copenhagen's Faculty of Health and Medical Sciences, serving as daily manager of the Laboratory of Advanced Imaging and 3D modelling (3D LAB). Her work integrates advanced digital technologies with forensic expertise to revolutionize evidence documentation and analysis. Her research focuses on virtual crime scene reconstruction using CT-scanning, photogrammetry, and surface scanning to create victim-specific 3D models that accurately reflect injuries and anatomy. This enables precise simulation of event sequences by combining crime scene digitization with biomechanical knowledge of human movement. Her interdisciplinary work spans forensic anthropology, osteology, mummy studies, and paleopathology, with collaborations extending to clinical medicine, veterinary sciences, and computer sciences. Analysis of her recent publications reveals dominant themes in forensic age estimation, craniofacial development across species, and historical case reconstructions like the Ötzi the Iceman murder investigation. Her work consistently demonstrates how 3D technologies bridge microscopic and macroscopic evidence to create permanent digital archives for forensic reassessment. Scientific awards: No awards were mentioned in the provided text. Advising and grants: The provided text contains no information about students, advising activities, or research grants. As leader of the 3D LAB, Villa develops innovative tools for recording and visualizing forensic findings, with ongoing projects focused on virtual reconstruction of trauma patterns and creating digital repositories that allow future re-examination of evidence when new information emerges.
Elzbieta Pastucha is an Associate Professor at the Maersk Mc-Kinney Moller Institute, University of Southern Denmark (SDU), where she is affiliated with the SDU UAS Center and SDU Climate Cluster. Her research focuses on UAV-based photogrammetry, remote sensing, and geospatial data analysis, with applications in environmental monitoring, urban mapping, and precision agriculture. Education: Ph.D. in Surveying, Photogrammetry, and Remote Sensing, AGH University of Science and Technology (Awarded: January 21, 2016) Research Interests: Her work spans advanced photogrammetric techniques, including smartphone-based 3D modeling, radiometric correction of UAV imagery, and classification of urban environments using neural networks. She applies these methods to solve real-world problems in infrastructure monitoring, geological displacement, and agricultural yield estimation. Her research integrates computer vision, geospatial engineering, and machine learning to enhance data accuracy and usability. Publication Trends: Recent publications (2022–2024) demonstrate a strong focus on UAV photogrammetry, with emphasis on image normalization, deformation correction, and environmental classification. Her work frequently involves interdisciplinary collaboration and appears in high-impact journals such as Remote Sensing , Sensors , and Engineering Geology . Scientific Awards: No awards or fellowships explicitly mentioned in the text. Advising and Grants: While no formal advisees are listed, she is actively teaching courses in statistics, data analysis, and robotics for UAS. She leads and participates in externally funded projects, including the EU-supported 'WildDrone' project as project manager and 'Præcisionsfrøavl' as a project participant, both running from 2023 to 2026. Labs and Teams: She is a key member of the SDU UAS Center and contributes to the SDU Climate Cluster. Her work is conducted within collaborative teams involving researchers from engineering, computer science, and environmental disciplines. She collaborates with researchers across institutions, particularly in UAV-based environmental and agricultural monitoring.
Iris Fernandes is a Postdoctoral Fellow at the Ice, Climate and Geophysics section of the Niels Bohr Institute, Faculty of Science, University of Copenhagen, specializing in computational geophysics and planetary surface analysis. Her work integrates uncertainty quantification with subsurface imaging techniques for geological exploration. She earned her PhD in Geophysics from the University of Copenhagen in 2022, with a dissertation titled Modeling spatial uncertainty in geophysics , which established foundational methods for error analysis in geophysical data acquisition. Dr. Fernandes' research spans planetary physics, focusing on lunar topography enhancement and Martian surface reflectance modeling. Her innovations in upscaling imagery resolutions for Moon surface analysis and quantifying borehole positioning errors have advanced subsurface imaging accuracy. Current projects target Mars' surface properties using reflectance models to interpret geological and climatic history. Her publication portfolio reveals consistent expertise in planetary geophysics, with recent articles (2021–2025) emphasizing lunar/Martian topography, seismic inversion robustness, and uncertainty propagation in geophysical measurements—highlighting interdisciplinary applications from space missions to terrestrial resource exploration. As an active member of the Ice, Climate and Geophysics research group, she collaborates on planetary exploration initiatives and contributes to the Niels Bohr Institute's geophysical imaging infrastructure, leveraging laser altimetry and photogrammetric datasets for extraterrestrial surface characterization.
Rolf Nordahl is an Associate Professor at Aalborg University's Department of Architecture and Media Technology, specializing in Virtual Reality (VR), haptic feedback, and sonic interaction design. His work bridges technology and human experience, focusing on applications for education, healthcare, and social skills development in autism. Research Highlights: Medical VR (pain distraction), immersive mathematics education, and multisensory simulations for empathy-building. Awards: Winner of the 2015 3D User Interfaces Contest; 2014 Honorable Mention for Best Paper. He leads projects such as GAMESS (VR for autism education) and Audio-haptics (Volvo collaboration), while actively organizing workshops like the IEEE VR Workshop on Sonic Interactions. His recent publications explore geometric learning environments and realistic audio-haptic feedback systems.
Henrik Skov Midtiby is an Associate Professor at the University of Southern Denmark's Maersk Mc-Kinney Moller Institute and affiliated with the SDU Climate Cluster and SDU UAS Center. His research focuses on robotics, computer vision, precision agriculture, and marine biology, with applications in UAV technology, environmental monitoring, and autonomous systems. He has led projects such as PotBot (autonomous plant transportation) and LEARNING (climate change interactions), and contributed to studies on harbor porpoise behavior and weed management algorithms. Education: PhD from the Maersk Mc-Kinney Moller Institute. Extensive teaching experience in robotics and computer vision courses for unmanned aerial systems (UAS). Research Interests: Unmanned Aerial Vehicles (UAVs), environmental sensing, machine learning for agricultural automation, marine mammal tracking, and energy-efficient robotic systems. Key projects include drone-based crop monitoring, autonomous navigation algorithms, and bioacoustic studies of marine life. Grants & Collaborations: Project manager for Præcisionsfrøavl (precision seedling) and PotBot. Collaborates with institutions on climate change impacts and marine conservation. Active in workshops like 'Teaching for Active Learning.' Labs/Teams: SDU UAS Center for drone research; SDU Climate Cluster for environmental studies. Involves students in projects combining robotics, AI, and ecological applications.
