Beatriz Soret is an Associate Professor at Aalborg University's Department of Electronic Systems, part of The Technical Faculty of IT and Design. Her research focuses on satellite communications, IoT, wireless networks, and AI-driven network systems. She leads and collaborates on projects like STELLAR (2019-2021) and SATNEX V WI Y4.6 (2024-2025), addressing latency, reliability, and real-time data challenges in 6G and satellite networks. Her work emphasizes distributed computing, edge computing for Earth observation, and semantic communication frameworks. Key publications include advancements in RAN slicing for VR traffic, coded distributed computing, and AI-integrated network layers. She actively contributes to special issues on distributed intelligence and 6G technologies. Her research spans theoretical and experimental analyses of delay, age of information, and network performance in scenarios like LEO satellite constellations and industrial IoT. She collaborates with institutions globally, advancing non-terrestrial networks and smart connectivity solutions.
Affiliations & Roles Michele Albano is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design, focusing on research in IoT, Cyber-Physical Systems, and Edge Computing. He leads the Productive4.0 project (2017–2020), funded by Horizon Europe, addressing Industry 4.0 challenges in product lifecycle management. His work integrates formal verification tools like Uppaal with real-world applications in robotics, energy systems, and blockchain-based platforms. Research Interests Albano's research spans IoT architecture optimization , energy-efficient systems , and model-driven engineering . He develops tools for autonomous exploration algorithms (MAES), edge-cloud resource orchestration, and fault-tolerant computation offloading. His work bridges theoretical models (e.g., Uppaal SMC) with practical implementations in smart grids and robotic systems. Recent projects include blockchain-based crowdsourcing for machine learning and energy-aware thermal dynamics estimation in buildings. Collaborations & Impact He collaborates with the European Industry 6.0 community, contributing to the Arrowhead Framework for interoperable IoT systems. His research outputs include 112 publications, with 2025 highlights in human-inspired robotics and cognitive cloud frameworks. Media coverage in 2024–2025 highlights his work on green IT and secure API generation. Albano advises students on system modeling (e.g., ACSmt plugin development) and edge computing optimization. Labs & Teams His research group focuses on Cyber-Physical Systems and Smart Grids , with contributions to tools like RoutesMobilityModel and FlexHousing. He actively participates in workshops on New Trends in Software Architecture (SATrends '24) and IEEE conferences on Industrial Informatics.
Alex Elkjær Vasegaard is a Postdoctoral Researcher in the Department of Materials and Production at Aalborg University's Faculty of Engineering and Science, specializing in human-AI decision systems for satellite and drone operations. His work focuses on making space technologies accessible for societal impact in smaller nations like Denmark. Education Ph.D. in Operations Research (2020-2023) MSc in Mathematics and Economics (Operations Research) (2017-2019) BSc in Mathematics and Economics (2014-2017) Research Focus Alex develops preference-based optimization frameworks to align autonomous systems with human decision-makers, particularly for disaster response and smart city operations . His core expertise spans multi-criteria decision analysis (MCDM), satellite scheduling algorithms, and UAV coordination, with strong contributions to UN Sustainable Development Goals through accessible Earth observation technologies. Publication Trends His 2024-2025 work shows intense focus on satellite image acquisition scheduling and drone detection systems , integrating MCDM with novel path algorithms. Key themes include human preference modeling in Earth observation, AI-driven oncology solutions, and open-source frameworks like EOSpython, bridging operations research with real-world societal applications. Awards ICRCA 2021 Best presentation award (2021) Rejselegat for matematikere travel grant (2019) Collaborations and Outreach He actively collaborates across engineering and computer science domains, evidenced by 6 similar research profiles in scheduling and UAV systems. His media feature 'BESLUTNINGER FRA HIMLEN' (2024) reached 6 Mendeley readers and 4 X users, highlighting public engagement with space technology decision-making. Research Group Alex operates within Aalborg University's Section of Applied Systems Research (Artificial Intelligence for Operations Research), developing practical tools for satellite mission planning and drone surveillance systems with societal impact.
Rune Hylsberg Jacobsen is a Professor at the Department of Electrical and Computer Engineering, Aarhus University. His work bridges energy systems, drone technology, and blockchain applications, with a focus on smart grids, autonomous systems, and decentralized infrastructure. Research interests include: Security frameworks for prosumer-driven energy systems using blockchain mmWave and LEO satellite communication protocols Homomorphic encryption for smart meter privacy Cooperative drone swarm navigation and infrastructure inspection Earth observation via CubeSats for climate research His recent publications highlight advancements in: Zero trust security models for renewable energy certificates Transport protocol optimization in satellite networks AI-driven charging window scheduling for drone fleets Decentralized identity management in Web3 infrastructure Key projects include: DISCO-2: Student CubeSat for Arctic climate monitoring Drones4Safety: Safety-critical inspection systems VPP4SGR: Virtual Power Plant networks
Yingguang Chu is an Associate Professor at the Institute of Mechanical and Electrical Engineering, University of Southern Denmark, specializing in Mechatronics. His research focuses on co-simulation systems, digital twins, and marine engineering applications. Research Interests : Digital twin technologies, co-simulation frameworks, marine crane systems, hydraulics, and control algorithms. Projects : Active participant in the ShippingLab II project (2024-2027) on digital twins and human factors in vessel performance. Research Output Trends : Recent work emphasizes physics-inspired neural networks for mobile machinery dynamics, environmental load sensitivity analysis, and Vico-based co-simulation systems for marine operations. Publications frequently address crane path planning, digital twin implementation, and dynamic response optimization. Scientific Awards : IEEE Robotics and Automation Magazine Best Paper Award (2024) Teaching Responsibilities : Supervises master's theses on topics like CFD simulations, membrane valve systems, and fluid mechanics, while teaching mobile hydraulics courses.
Vinay Chakravarthi Gogineni is an Assistant Professor at The Maersk Mc-Kinney Moller Institute , University of Southern Denmark (SDU), specializing in SDU Applied AI and Data Science . His research integrates advanced AI techniques into healthcare, industrial IoT, and fusion energy systems, with a strong focus on federated learning, privacy-preserving AI, and ethical machine learning practices. Education: Ph.D. in Distributed Machine Learning, Indian Institute of Technology Kharagpur (Aug 2019) Research Interests: Dr. Gogineni's work spans Deep Learning , Federated Learning , and Graph Data Analysis . He develops personalized AI models for healthcare applications, including cervical cancer screening, colorectal cancer detection, and dementia prediction. His innovations in Machine Unlearning ensure compliance with GDPR and the EU AI Act, while his work on Physics-Informed Neural Networks enhances predictive accuracy in fusion energy systems. Key areas include self-supervised learning, continual learning, and fairness-aware AI. Scientific Awards: HC Ørsted Research Talent Award (2024, Denmark) ERCIM Alain Bensoussan Fellowship (2019) Best Paper Award , APSIPA ASC-2021, Tokyo Professional Roles: IEEE Senior Member and editorial board member of IEEE Sensors Journal . He teaches courses on AI for Healthcare Data and Calculus and Linear Algebra , fostering interdisciplinary education in applied AI.
Thomas Kjær Rasmussen is an Associate Professor and Head of the Transportation Systems Modelling section at the Department of Technology, Management and Economics, Technical University of Denmark (DTU). His research lies at the intersection of transportation science, behavioral modeling, and urban mobility, with a strong emphasis on sustainable transport systems. His research interests include transportation systems modelling , route choice behavior , cyclist and pedestrian safety , stochastic user equilibrium , and the application of crowdsourced data to model near-crashes and travel behavior. He actively contributes to advancing choice modeling techniques, particularly in integrating behavioral realism into dynamic traffic assignment. The recent publications highlight a consistent focus on improving the behavioral realism of route choice models, addressing route overlap, and leveraging novel data sources such as bicycle airbag helmet sensors. These works span methodological advances in equilibrium modeling and empirical applications in urban networks like Copenhagen. Scientific Awards: The International Choice Modeling Conference (ICMC) award for the Most innovative application of choice modelling (2022) Advising and Grants: Rasmussen is actively involved in supervising multiple PhD projects, serving as main supervisor or co-supervisor in areas such as human energy expenditure in mobility, dynamic traffic assignment, bicyclist behavior, and next-generation route choice models. These projects reflect sustained research funding and institutional support for advanced transportation research at DTU. Labs and Research Teams: He is a key member of a vibrant research group in transportation modeling at DTU, collaborating closely with Prof. Otto Anker Nielsen and other researchers such as Luke C. Duncan, David P. Watling, and Marie Paulsen. The team focuses on behavioral realism in transport models and leverages large-scale datasets for empirical validation.
Kristoffer Almdal is a Professor in the Department of Chemistry, Technical University of Denmark (DTU), specializing in Physical Chemistry with a focus on Polymers and Functional Interfaces. He has held significant leadership roles including Head of Section at DTU and Head of Department at Risø National Laboratory, and has been a professor at DTU since 2008, indicating continued active status. PhD in Polymer Chemistry and Analysis, University of Copenhagen (1985–1989) MSc in Physical Organic Chemistry, University of Copenhagen (1977–1985) His research centers on polymer synthesis, particularly anionic polymerization, and the self-organization of block copolymers—linear and branched—with applications in functional materials, sensors, and biomaterials. He investigates mesophase structures, rheology, polymer degradation, and interfaces in composites using advanced analytical methods like small angle scattering and size exclusion chromatography. Recent publications (2024–2025) highlight work in eco-friendly nanogels for wound care, fatigue in epoxy resins, biomass-derived carbon aerogels for energy storage, and phase change materials in 3D-printable construction. These reflect a strong trend toward sustainable, multifunctional materials with applications in healthcare, energy, and construction. The research integrates fundamental polymer physics with practical engineering challenges, often through interdisciplinary collaboration. Kristoffer Almdal actively supervises PhD students in diverse projects, including: Synthesis of ABC-miktoarm star block copolymers Ion pairing in polyelectrolytes 3D printing with phase change materials Acoustic polymer lenses for ultrasound Block copolymer patterning of 2D materials He has delivered invited and keynote talks on polymer degradation, elongational flow, and self-organization, demonstrating recognition in the field. His work contributes to UN Sustainable Development Goals related to sustainable materials and clean energy. While no specific awards are listed, his extensive publication record (433 outputs), patents, and leadership in funded research projects underscore his impact. He is involved in multiple research groups and collaborative networks focused on polymer science, materials engineering, and sustainable technologies. His lab emphasizes cross-disciplinary innovation, bridging chemistry, physics, and engineering to develop next-generation functional materials.
Ignacio Rodriguez Larrad is a researcher at Aalborg University , affiliated with the Department of Electronic Systems under the Technical Faculty of IT and Design . His work focuses on wireless communication engineering, industrial IoT, and path loss modeling. He is currently involved in the 5G-enabled autonomous mobile robotic systems project. Current affiliations: Aalborg University, Spanish Researchers in Denmark (Vice-chairman) Research interests include 5G wireless networks, industrial IoT deployment, real-time locating systems, and antenna engineering. His work addresses practical challenges in factory environments and rural connectivity solutions. Recent publications highlight applications in corrosion mapping, robotic control, and network integration. Key trends involve deep learning for industrial automation, UWB technology, and multi-connectivity frameworks. Scientific Awards : 5G-prisen (2019) - Recognizing 5G research contributions Neal Shepherd Memorial Best Propagation Paper Award (2017) - Radio propagation studies Ignacio has co-supervised PhD research and participated in industry-focused projects. His activities include media engagement and presentations on industrial wireless systems at conferences.
Sebastian Bro Damsgaard is a Researcher at the Department of Electronic Systems within The Technical Faculty of IT and Design at Aalborg University in Denmark. His research focuses on advancing wireless technologies for industrial and rural applications, with expertise in 5G, Wi-Fi 6, multi-connectivity solutions, and IoT integration. He actively contributes to projects funded by Innovation Fund Denmark, including cybersecurity for power grids and autonomous robotic systems. Research Interests: His work spans wireless communication paradigms, emphasizing: Industrial connectivity (Wi-Fi 6, 5G reliability in factories) Rural network solutions (satellite-terrestrial integration) IoT applications in agriculture and infrastructure Edge computing and cybersecurity for critical systems Publication Trends: Recent articles (2024-2025) demonstrate empirical analyses of multi-technology networks (5G/Wi-Fi/satellite) in challenging environments. Dominant themes include scalability testing, path loss modeling for sensors, and machine learning-enhanced connectivity for IoT use cases in industrial/rural settings. Projects & Collaboration: Key involvements: CyberPE: Power Sentinel (2024-2025): Safeguarding power grids against cyber-physical threats. 5G Enabled Autonomous Mobile Robotic Systems (2022-2024): Enhancing robotic efficiency via 5G connectivity. He collaborates with cross-disciplinary teams at Aalborg University, focusing on real-world wireless deployments.
Sue Charlesworth is a Professor in Urban Physical Geography at Coventry University's Centre for Agroecology, Water and Resilience. Her research focuses on Sustainable Drainage Systems (SuDS), urban pollution, and natural flood management. She has authored over 70 peer-reviewed articles and collaborates globally on projects addressing environmental challenges in urban areas, including informal settlements and refugee camps. Education: She holds a Doctor of Philosophy (PhD) in Physical Geography from Coventry University (1994), with a thesis on urban lake pollution histories. Research Interests: SuDS design in marginalized urban communities, green infrastructure efficacy, urban sediment contamination risks, and climate-resilient water management strategies. Her work integrates environmental science with public health, particularly in reducing disease vectors via improved water management. Projects: Active roles in initiatives like the SOIL NEXUS project (urban soil remediation) and collaborations with GITAM University (India) on water quality. She also co-investigated emerging pollutants from e-waste and urban infrastructure. Grants/Advising: Supervises PhD students and leads research networks like the Arup Network Rail project. Her work bridges academic research with practical urban planning solutions. Labs/Teams: Central to the Centre for Agroecology, Water and Resilience, collaborating internationally on SuDS implementation and natural flood management strategies.
Christos Tachtatzis is a Professor in Applied Artificial Intelligence in the Department of Electronic and Electrical Engineering at the University of Strathclyde. He rejoined the university in 2011, was awarded a Chancellor’s Fellow in 2016, promoted to Senior Lecturer in 2018, Reader in 2021, and Professor in 2024. He leads key strategic initiatives including the Measurement, Digital and Enabling Technologies (MDET) Strategic Theme, co-directs the Laboratory for Innovation in Autism, and serves as Strathclyde lead for the UKRI AI CDT SUSTAIN. He is also a member of the HealthTech Cluster and advises The Data Lab and the Scottish Government on AI applications in agriculture and natural resources. Research Interests: His research spans applied AI with focus on computer vision, multimodal learning, domain adaptation, and explainability. These are applied to sustainable agri-food systems (livestock and arable), digital health, advanced manufacturing, and cybersecurity. His technical expertise includes deep learning, time series analysis, anomaly detection, remote sensing, hyperspectral imaging, and edge/cloud computing analytics. Recent Research Trends: His recent publications reflect a strong trend toward interdisciplinary AI applications, including environmental monitoring via satellite imagery inpainting, urban CO2 emission modeling, synthetic data generation for power grids, infant behavioral analysis, and precision livestock farming using monocular depth estimation. These works highlight his focus on real-world, data-driven solutions across environmental, health, and industrial domains. Scientific Awards: Innovate UK KTP Engineering Excellence Award (2021) Finalist, Herald Higher Education Awards – Outstanding Business Engagement (2022) Strathclyde Team Medal for Innovation in Autism (2018) SIN 2014 Best Paper Award Advising and Grants: He is actively involved in supervising research and leading externally funded projects from UKRI, InnovateUK, and H2020. He is Principal Investigator on multiple grants including Deep Learning for Woodland Soil Biodiversity, FLORA-SAGE (federated learning in agriculture), and the UKRI AI CDT SUSTAIN. He is a co-investigator on projects in digital dairy, infant interaction, and species assessment. He welcomes PhD students and regularly advertises opportunities through SUSTAIN and his professional networks. Labs and Teams: He co-directs the Laboratory for Innovation in Autism and leads the MDET Strategic Theme. He is embedded in the SUSTAIN CDT and collaborates extensively with the HealthTech Cluster, contributing to interdisciplinary research at the intersection of AI, engineering, and societal challenges.
Kaixuan Chen is a Researcher at the Department of Computer Science within The Technical Faculty of IT and Design at Aalborg University , Denmark. His work focuses on AI-driven systems for human activity recognition, trajectory analysis, and deep learning optimization. Research Interests: Chen's research spans Artificial Intelligence , Deep Learning , and Data Engineering . Key areas include Knowledge Graph Embedding , Neural Signal Processing , and Context-aware Spatial Crowdsourcing . Recent Publications demonstrate expertise in contrastive learning , BCI systems , and automated design of lightweight AI models . His work integrates multimodal data for superior performance in dynamic environments. Collaborations include international partnerships in AI and energy systems. Supervised 1 PhD student and contributed to 15+ publications since 2018.
Jaron Skovsted Gundersen is a Research Assistant at the Department of Electronic Systems, within The Technical Faculty of IT and Design at Aalborg University, Denmark. He is actively involved in the Automation & Control group and the Learning and Decisions Lab, focusing on privacy-preserving distributed systems, quantum coding, and decentralized control for infrastructure resilience. His research centers on advanced topics in secure computation and machine learning, including privacy-preserving distributed consensus , secure multi-party computation using Shamir secret sharing , federated learning , and quantum stabilizer codes . His work integrates theoretical foundations with practical applications in critical systems such as water and power distribution networks. The trend in his publications shows a strong emphasis on data privacy in distributed machine learning , leveraging techniques like subspace perturbation and differential quantization. His recent articles span high-impact journals such as IEEE Transactions on Information Forensics and Security and IEEE Journal on Selected Areas in Information Theory, reflecting contributions to both theoretical and applied aspects of information security and control systems. He has been a project participant in the SWIFT research initiative (2019–2024), which investigates decentralized control solutions for electric and water distribution systems. His activities include multiple conference presentations, participation in academic workshops, and public engagement through events like the PDJF Grundfos Prize 'The Stars of Tomorrow' EXPO. He also delivered a lecture on technological solutions in water technology at a national climate meeting in 2022. PhD graduate (March 2021) Active researcher in privacy-preserving machine learning and quantum coding Contributor to resilient infrastructure control systems Regular participant in international conferences and workshops Gundersen is affiliated with the Learning and Decisions Lab at Aalborg University, where he collaborates on cutting-edge research in distributed intelligence, secure computation, and adaptive control systems. The lab fosters interdisciplinary work combining control theory, information theory, and machine learning for real-world applications.
Evangelos Boukas is an Associate Professor in the Department of Electrical and Photonics Engineering at the Technical University of Denmark (DTU), Faculty of Engineering. His research focuses on autonomous robotic systems for marine vessel inspection, confined space navigation, and man overboard detection using UAVs and deep learning techniques. His core research interests include Deep Learning , Unmanned Aerial Vehicles , Computer Vision , and Robotics , applied to solve challenges in marine robotics and autonomous inspection. He develops uncertainty-aware navigation systems, probabilistic segmentation methods, and reinforcement learning frameworks for real-world deployment in complex environments. Recent publications demonstrate a strong trend toward GPU-accelerated deep learning for confined space inspection, with emphasis on uncertainty estimation, domain adaptation, and real-time processing. Key application areas include marine vessel classification, ballast tank inspection, and search-and-rescue operations, often leveraging multimodal sensor fusion. Dr. Boukas actively supervises five PhD students across major research projects: Probabilistic Deep Learning for Autonomous Aerial Inspection (2025-2028) Aerial Robots Design for Confined Spaces Inspection (2024-2027) Active Semantic Segmentation for Confined Spaces using Aerial Robots (2024-2027) Autonomous Aerial Robotics for Man Overboard Incidents (2023-2026) Visual and Multimodal Perception for Construction Robotics (2023-2026) He leads the "Perception and Cognition for Autonomous Systems" research group at DTU, which develops advanced perception algorithms for robots operating in challenging environments like ship ballast tanks and marine vessels, contributing to UN Sustainable Development Goals for industry innovation and marine conservation.
