Thomas B. Moeslund is a Professor at Aalborg University's Department of Architecture and Media Technology within The Technical Faculty of IT and Design. His research focuses on Computer Vision, Machine Learning, and AI applications in robotics, rehabilitation engineering, and maritime surveillance. He leads initiatives like the Center for Rehabilitation Robotics and contributes to AI ethics through the REPAI project. Key research areas include automated fishery monitoring (AutoFish dataset), upper-limb exoskeletons for disabled individuals, and anomaly detection systems. He has published over 550 works and secured grants from organizations like the Louis-Hansen Foundation and AI Denmark. Projects: CRERoB (Rehabilitation Robotics), FLIP (Flexible Laser Production), and REPAI (Responsible AI) Awards: Best Paper Awards at EIBA 2023 and CISBAT 2021 Labs/Teams: Visual Analysis and Perception Group, AI for the People initiative His work impacts policy (e.g., banning beam trawling via marine research) and industry (defect detection in infrastructure). He actively collaborates with international partners and advises on AI ethics in healthcare and governance.
Tung Kieu is a Tenure Track Assistant Professor in the Department of Computer Science at Aalborg University (Denmark), affiliated with The Technical Faculty of IT and Design and the Daisy Center for Data-intensive Systems. His research focuses on data engineering, time series analysis, anomaly detection, and machine learning applications in traffic forecasting and smart systems. Education: Ph.D. in Computer Science (Awarded May 2021). Research interests include time series forecasting, traffic modeling, robust autoencoder architectures for anomaly detection, and spatio-temporal data analysis. His work contributes to UN Sustainable Development Goals related to smart cities and infrastructure. Recent publications explore bias mitigation in text-video retrieval (BiMa), topology-aware traffic forecasting (TEAM), and stochastic routing in uncertain road networks. His frameworks emphasize lightweight algorithms (LightTS), causal relational learning, and continual calibration for quantized models (QCore). Collaborations involve international teams in data management and AI, with notable work on ensemble methods, explainable AI, and transfer learning in smart building systems.
Simon Pedersen is an Associate Professor and Head of the Esbjerg Energy Section at Aalborg University's Faculty of Engineering and Science. His research focuses on offshore robotics, marine engineering, carbon capture technologies, and autonomous underwater systems. He leads projects like NextGen Robotics for offshore wind farm maintenance and CarbonAdapt for CO2 storage infrastructure adaptation. Pedersen holds a PhD in Control Engineering from AAU (2016), an M.Sc. in Intelligent Reliable Systems (2013), and a B.Sc. in Electronics and Computer Engineering (2011). Research Interests: Offshore drones, underwater vehicle navigation, robotics control, carbon capture and storage (CCS), and sustainable energy systems. His work addresses UN SDGs related to clean energy and marine environmental protection. Key Projects: Automatic replantation of large-scale eelgrass (2025–2028) NextGen Robotics for underwater inspection (2023–2025) CarbonAdapt: CO2 storage infrastructure adaptation (2022–2025) Publications: Over 80 papers in journals like Journal of Cleaner Production and Carbon Capture Science & Technology , focusing on robotics, CO2 transportation, and energy systems. Recent work includes frameworks for CO2 impurity monitoring and slip prevention in underwater robotics. Awards: Won the Innovation Project of the Year (2024) for contributions to robotics and subsea infrastructure. Advising: Supervises PhD candidates including Kenneth Simonsen (carbon storage) and Esben Uth (autonomous drones). Active in grants totaling millions from Innovation Fund Denmark. Labs/Teams: Leads the Esbjerg Energy Section’s robotics and marine systems teams, collaborating on projects like offshore crawler robots and subsea inspection systems.
Fredrik Fogh Sørensen is an Assistant Professor at Aalborg University's Faculty of Engineering and Science, specializing in the Esbjerg Energy Section. His work focuses on marine and maritime research, particularly in the areas of underwater robotics and offshore energy systems through AAU BLUE – Marine & Maritime Research. Dr. Sørensen's research interests span Marine Growth, Underwater Robotics, Remotely Operated Vehicles, Localization Systems, Offshore Energy, Control Systems, and Sustainable Energy. His work contributes to UN Sustainable Development Goals related to energy sustainability with research fingerprint showing strong connections to Marine Growth (100%), Remotely Operated Vehicle Engineering (92%), and Offshore Structure Engineering (28%). His publication record demonstrates consistent growth from 2 publications in 2019 to 4 in 2024 with 2 more scheduled for 2025. His research shows a clear progression from foundational work to practical applications in offshore environments with particular emphasis on underwater vehicle technology, localization systems, and control mechanisms for ROVs. Dr. Sørensen has received several prestigious awards for his work: Innovation Project of the Year (2024) Best Session Paper Award (2022) Universitetspris 2022 (2022) He is actively involved in significant research projects including ACOMAR (Auto Compact Marine Growth Remover), ACOMAR II, and DIN-ECO (Boosting Digital Innovation and Transformation Capacity of HEIs in an Entrepreneurial ecosystem). His collaborative network spans multiple institutions with strong connections in marine technology research. Dr. Sørensen was featured in Ugeavisen Esbjerg in February 2023 regarding the Esbjerg University Prize 2022, where he was honored as a civil engineer in Sustainable Energy Technology from AAU Esbjerg.
Petar Durdevic is an Associate Professor at Aalborg University's Faculty of Engineering and Science, within the Energy Section. His research focuses on Reinforcement Learning, Deep Learning, Robotics, and Environmental Engineering, particularly in wastewater treatment optimization and offshore energy systems. He leads projects like OPTIMIZE (Deep Reinforcement Learning for microbial fermentation) and AITEKS (AI-driven tech support). Durdevic collaborates with the Offshore Drones and Robotics and AI for the People teams, addressing challenges in energy technology and sustainable systems. His research interests include predictive control for wastewater treatment, autonomous robotics for pipeline inspection, and AI applications in environmental monitoring. Notable contributions include developing grey-box models for N2O emissions and amphibious quad-rotor systems for infrastructure inspection. Durdevic has supervised one PhD student, Lars D. Hansen, and secured grants from the Novo Nordisk Foundation and Innovation Fund Denmark. Key achievements include advancing fiber rope technology for wind turbines and publishing extensively on topics like deep reinforcement learning, synthetic data generation, and environmental control systems. His work bridges robotics, AI, and environmental science, with applications in offshore energy and sustainable wastewater management. PhD Supervision: Lars D. Hansen (Deep Learning Model Predictive Control for N2O Emission Reduction) Labs/Teams: Offshore Drones and Robotics, AI for the People, BLUE – Marine & Maritime Research Grants: OPTIMIZE (Novo Nordisk Foundation, 2024–2028), AITEKS (Innovation Fund Denmark, 2024–2025)
Chen LI is an Associate Professor in the Department of Materials and Production at Aalborg University, Denmark. His work focuses on advancing smart manufacturing through AI-driven robotics and 5G-enabled production systems. He leads research projects involving historical figure emulation in robotics, human-robot interaction, and AI at the edge for industry 5.0. His interdisciplinary approach bridges software architecture, industrial automation, and cognitive modeling. Key projects: Time-Traveling Conversations (Villum Foundation), Human Robot Interaction dialog systems, and AI REDGIO 5.0 for SME manufacturing Collaborations span institutions like Seoul National University and industry partners Research interests include: AI ethics in dialogue systems Edge computing for real-time manufacturing Virtual assistant architectures Robot speaking style imitation Over 70 publications (2009-2025) span topics like anomaly detection frameworks, defect prediction models, and hybrid power system analysis. Active in conferences such as HRI2021 and workshops on AI in performing arts.
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.
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.
Stephanie Forrest is a Professor in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU), where she also serves as Director of the Biodesign Center for Biocomputation, Security and Society. She is an External Faculty member of the Santa Fe Institute (SFI), where she previously held leadership roles including Co-Chair of the Science Board (2010–2013) and Interim Vice President for Academic Affairs (1999–2000). Prior to joining ASU, she was a Regents Distinguished Professor and Department Chair of Computer Science at the University of New Mexico (2006–2011). B.A., St. John's College M.S., Ph.D., University of Michigan, Computer Science Her research lies at the intersection of biology and computation, focusing on computational immunology, automated software repair, evolutionary computation, and modeling complex systems such as cancer and immune responses. She pioneers bio-inspired algorithms for cybersecurity and develops evolutionary techniques for software optimization and repair. Her work integrates principles from immunology, genetics, and complex adaptive systems to solve problems in computer science. The trends in her recent publications reveal a sustained focus on automated program repair using evolutionary computation, GPU code optimization, and modeling biological systems. Her work increasingly combines machine learning with mutation-based search, explores semantic representations in code repair, and applies bio-inspired models to real-world software and security challenges. ACM/AAAI Allen Newell Award (2011) Presidential Young Investigator Award (1991) Stanislaw Ulam Memorial Lectures, SFI (2013) IEEE Fellow UNM Annual Research Lecture (2012) IEEE S&P Test of Time Award (2020) ICSE Most Influential Paper Award (2019) ACM/SIGEVO Impact Award (2019) SEAMS Best Paper Award (2019) WEIS Best Paper Award (2015) Stephanie Forrest has advised numerous students and researchers, often in collaboration with W. Weimer, C. Le Goues, and M. Moses. Her research is supported by major funding agencies including the National Science Foundation (NSF), Defense Advanced Research Projects Agency (DARPA), Air Force Research Laboratory (AFRL), and the Santa Fe Institute. She has also contributed to public policy as a Jefferson Science Fellow at the U.S. Department of State (2013–2014), advising on cyber-policy. She currently chairs the Government Affairs Committee of the Computing Research Association (CRA). She leads the Biodesign Center for Biocomputation, Security and Society, which brings together interdisciplinary teams to study the co-evolution of technology and society. Her research group has developed tools for intrusion detection (e.g., STIDE, pH, RISE), automated software repair (e.g., GenProg), and biological modeling (e.g., CancerSim). Current projects include engineering diversity for enhanced cybersecurity, measuring internet censorship, and modeling immune and cancer systems.
Claus Melvad is a Professor at the Department of Mechanical and Production Engineering, School of Engineering, Aarhus University. His primary research focuses on developing robotic systems and metrology equipment for extreme environments, including Arctic and rainforest regions. Expertise: Robotics, Metrology, Autonomous Vehicles, Sensor Systems Affiliation: Aarhus University School of Engineering Research Highlights: Melvad specializes in creating rugged, low-cost instruments for environmental monitoring. His work includes: Autonomous drones for biodiversity and disease monitoring in tropical regions Robotic ocean profilers for polar environments Modular autonomous surface vehicles (ASVs) for climate research Custom sensor systems for extreme condition calibration Publication Trends: Recent projects emphasize drone technology for remote biodiversity monitoring, low-cost Arctic instrumentation, and autonomous sampling systems. Keywords include environmental robotics, sensor development, and extreme environment engineering. Projects: Lead initiatives like GlacierPro (autonomous methane profiler), DISCO (student CubeSat program), and NORDACC (autonomous surface vehicle).
Ryutaro Yamashita is an Adjunct Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science. His research spans quantum computing, cryptography, and machine learning. Focus areas include quantum error correction, secret sharing schemes, and adversarial attacks on depth estimation networks. Collaborates on entanglement-assisted codes and finite field applications. Recent work highlights trends in securing quantum information systems and enhancing neural network robustness.
Ruyu Liu is a Research Fellow at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). Their work bridges interdisciplinary research in Energy Economics and Climate Policy with cutting-edge advancements in Computer Science and Artificial Intelligence . Research Interests : Knowledge Distillation Semantic Segmentation Point Cloud Upsampling Neural Network Optimization Machine Learning Applications Climate-Energy Economics Recent Publications highlight trends in AI and computer vision, including: Development of novel knowledge distillation methods to address teacher bias in neural networks. Advancements in semi-supervised semantic segmentation using dual-branch architectures and self-matching strategies. Efficient algorithms for point cloud upsampling leveraging implicit neural representations and spatial hashing. Application of singular value decomposition (SVD) for feature extraction in distillation frameworks.
Muhammad Rizwan Asif is an Assistant Professor in the Department of Electrical and Computer Engineering at Aarhus University (AU Engineering). His research focuses on signal processing, machine learning, and deep learning applied to remote sensing and geophysical data analysis. Research Interests include image processing, computer vision, and leveraging deep learning for environmental monitoring and resource management. His work addresses challenges in noise removal, transient electromagnetic data inversion, and feature extraction in satellite imagery. Current projects: Ethio-Nature (2025–2030), Digitalization of plant-based food samples (2024–2024), and Neural Network AEM Pre-processing (2024–2025). Past projects: SOLGRAS (2024–2025), MapField – Nitrate retention (2020–2022), Flood and Drought – surface NMR (2019–2023), and SiTEM – African groundwater (2019–2023). His expertise bridges engineering and environmental science, with a focus on automated data processing and optimization using scientific machine learning.
Rasmus Schmidt Davidsen serves as a Tenure Track Assistant Professor in the Department of Electrical and Computer Engineering at Aarhus University. His research is centered within the Electronics and Photonics laboratory, where he investigates interdisciplinary topics at the intersection of electrical engineering, photonics, and biomedical applications. His primary research interests include photovoltaics (with a focus on perovskite solar cells, selenium-based photovoltaics, and black silicon technology) and biomedical engineering (specifically neural interfaces, retinal prosthetics, and microfabrication of implantable devices). He also explores microfabrication techniques for carbon microelectrodes and their application in neural stimulation. An examination of his recent publications reveals two dominant research themes: advancing solar cell efficiency through novel materials and structures, and developing biomedical devices for vision restoration. His photovoltaic work addresses critical challenges in tandem solar cells and defect engineering, while his biomedical research pioneers carbon-based retinal implants and long-term retinal tissue culture. Scientific Awards: No scientific awards were mentioned in the provided information. Advising and Grants: There is no information provided regarding graduate students or research grants supervised by Dr. Davidsen. Laboratory Affiliation: Dr. Davidsen is actively involved in the Electronics and Photonics research group at Aarhus University, contributing to both fundamental and applied research in micro- and nanofabrication for energy and biomedical applications.
Carsten Eie Frigaard is an Associate Professor at the Department of Electrical and Computer Engineering, Aarhus University (AU Engineering). His work focuses on signal processing and machine learning applications in biological monitoring systems. Research Themes: Development of computer vision systems for ecological monitoring, object detection of insects in time-lapse imagery, and deep learning applications for camera trap data analysis. Key Collaborations: Co-authorship with K. Bjerge on multiple publications in journals like PLOS Sustainability and Transformation , Sensors , and Computers and Electronics in Agriculture , as well as preprints on bioRxiv and arXiv.org . Project Involvement: Led the "Varroamide detektion på bier" research project from March 2017 to December 2018, focusing on automated monitoring of honeybee infestations.
