Massimo De Vittorio is a Professor at the Department of Health Technology, Technical University of Denmark, specializing in optomechanical biointerfaces, drug delivery, and biomedical sensing. His research bridges physics-informed machine learning with advanced optical systems for biomedical applications. Active in developing neural network architectures for modeling turbid media Focus on energy harvesting and piezoelectric biopolymers for implantable medical devices His projects include Photoacoustic Sensors for the Brain (2025–2028), Piezoelectric Drug Delivery Devices (2025–2028), and Nature-Inspired Energy Harvesting in the Gut (2025–2027), supervising multiple PhD candidates. Current research trends emphasize digital twin technologies , machine learning interpretability , and biocompatible material design across health technology domains.
Mirgita Frasheri is a Tenure Track Assistant Professor at the Department of Electrical and Computer Engineering, Aarhus University. Her research focuses on autonomous systems, multi-agent systems, and digital twins, particularly in the context of cyber-physical systems and robotics. Education: PhD in Computer Science from Mälardalen University (2020), specializing in Modelling and Control of Collaborative Adaptive Autonomous Agents. Mirgita's research explores the application of digital twins for enhancing cyber-physical systems, fault tolerance mechanisms in autonomous robotics, and federated digital twin platforms. Her work bridges theoretical advancements with industry-oriented solutions. Recent publications highlight trends in systematic reporting for digital twins, federated architectures, and autonomous agent integration in sensor networks. She is actively involved in collaborative projects, including agricultural robotics and CPS optimization. She participated in the 20th Overture Workshop and contributes to interdisciplinary research in digital twin services and mobile sensor networks.
Jonas Vinther is a Research Fellow at the Department of Computer Science , University of Copenhagen, specializing in Machine Learning and its intersections with quantum computing, medical data analysis, and sustainability. He is also an external PhD student in the Quantum Information Science & Technology program at the Niels Bohr Institute. Email: jonas.vinther@nbi.ku.dk , jonas.vinther@di.ku.dk Location: Universitetsparken 1, 2100 København Ø His research spans quantum machine learning , AI ethics , medical imaging , and environmentally sustainable AI , with recent publications on topics ranging from quantum neural networks to fairness in recommender systems . He contributes to the SCIENCE AI Centre and collaborates on initiatives like TreeSense for global tree resource monitoring.
Silvia Tolu is an Associate Professor at the Technical University of Denmark's Department of Electrical and Photonics Engineering, specializing in Neurorobotics. She leads the NeuroRobotics Technology Lab (NRT-LAB), focusing on bio-mimetic control architectures for compliant robotic systems. Her research integrates neuroscience, computer science, and biology to develop solutions for assistive robotics and neurodegenerative disease diagnosis. Her research interests span: Neuro-robotics and neuromorphic engineering Bio-inspired control systems and adaptive motor control Machine learning for robotic applications Human-robot compliant interaction Cerebellar control models Publications primarily focus on neurorobotics, bio-inspired control, and human-robot interaction, with recent advances in learning-based control systems for soft robots and aerial manipulation. Awards include the AEG Elektrofonden Research Grant and funding for human-robot interaction safety research. Current projects include LOCOPD (Lundbeck Foundation), AEROTRAIN (EU Marie Curie ITN), and compliant human-robot interaction systems. She supervises multiple PhD students in neurorobotics and maintains international collaborations across Europe and Asia. Laboratory resources include advanced robotic platforms for musculoskeletal and soft robot control.
Gert Frølund Pedersen is a Professor at the Department of Electronic Systems , Aalborg University , within the Technical Faculty of IT and Design . His research focuses on antennas, propagation, and millimeter-wave systems. He leads projects like DRONES (30 million DKK from Innovationsfonden) for drone-based electromagnetic signature analysis and EcoSurf6G for energy-efficient reconfigurable surfaces in 6G networks. With over 758 research outputs and 21 PhD students supervised, he contributes extensively to wireless communication advancements. Antenna Engineering Millimeter-Wave Systems Reconfigurable Intelligent Surfaces Deep Learning in Antenna Design His recent work emphasizes millimeter-wave IoT applications , UWB propagation channels , and 5G/6G antenna arrays . Publications highlight innovations in transmitarray antennas , liquid crystal polarization control , and metasurface design using AI. His research spans from fundamental electromagnetic safety to cutting-edge wireless infrastructure. Notable awards include Best Reading Paper of the Issue (IEEE Transactions on Microwave Theory and Techniques, 2020), ESI Highly Cited Paper (2019), and Ridder af Dannebrog (2021). He frequently engages with media to address public concerns about mobilstråling (mobile radiation) and its safety.
Anders Schmidt Kristensen is an Associate Professor at Aalborg University's Faculty of Engineering and Science, affiliated with the Esbjerg Energy Section and the Danish Centre for Risk and Safety Management. His work spans mechanical engineering, computational mechanics, and risk analysis, with a focus on structural design optimization and safety systems. Specializes in CAD-integrated structural modeling and finite element method (FEM) simulations Active in offshore energy and space debris mitigation research Key technologies: drag sail systems for satellite deorbiting, wind turbine power quality control, and tube-tubesheet joint modeling Recent research trends include: Structural health monitoring under thermal stress Optimized evacuation protocols for sports venues Advanced numerical modeling techniques for industrial components
Dimitris Chrysostomou is an Associate Professor in the Department of Materials and Production at Aalborg University, Denmark. He leads the Robotics & Automation Group and directs the AI:Cybernetics Lab. His work focuses on developing safe, intuitive robotic systems for industrial and social contexts, emphasizing human-robot interaction (HRI), AI ethics, and collaborative robotics. Chrysostomou has over 15 years of research experience, funded by EU frameworks and national grants, with over 70 peer-reviewed publications. Education: PhD in Robot Vision (2013) and Diploma in Production Engineering (2006) from Democritus University of Thrace. He coordinates courses in robotics and manufacturing technology, emphasizing problem-based learning models. Administrative roles include heading the Robotics and Automation Group and the Aalborg Robotics Challenge steering committee. Research interests span AI-driven robotics, ethical implications of robot behavior, and HRI evaluation methodologies. Key projects include SAPIENT (2025-2027) for robotic intelligence and RIACT (2024-2025) on collaborative robot technology. Editor-in-Chief of *Industrial Robot* journal, IEEE Senior Member, and leader in euRobotics standardization initiatives. Notable contributions include virtual assistants for industrial robots, trust evaluation frameworks in HRI, and energy-based approaches for collaborative robotics. Active in conference organization, editorial roles, and industry collaborations, including co-founding AI startups.
Brian Nielsen is an Associate Professor at Aalborg University's Department of Computer Science, under The Technical Faculty of IT and Design. His research focuses on Distributed Systems, Embedded Systems, Real-Time Systems, Model Checking, IoT Networks, and Autonomous Vehicles. He has been actively involved in projects such as domOS (operating system for smart buildings), FED (Flexible Energy Denmark), and compositional verification of multicore avionics systems. His work emphasizes model-based validation, formal methods, and industrial applications, particularly in safety-critical systems. Recent research highlights include energy-efficient motion planning for autonomous vehicles, comparative analysis of network simulators, and sigfox-based IoT modeling. He has received awards including the Application Coordinator Pool and START funds (both in 2007). Key Projects: domOS, FED, compositional verification of multicore systems Grants: Multiple EU-funded projects (e.g., Horizon Europe) and industry collaborations Awards: Application Coordinator Pool (2007), START funds (2007) His publications span over 80 articles, with a focus on real-time systems, IoT interoperability, and model-driven development. He has advised two PhD students and has been actively involved in organizing international conferences and workshops.
Kaare Mikkelsen is an Associate Professor in the Department of Electrical and Computer Engineering at the Faculty of Technical Sciences, Aarhus University, specializing in biomedical engineering with a focus on ear-EEG technology for sleep and auditory neuroscience applications. His research pioneers non-invasive monitoring systems using ear-centered EEG sensors, developing machine learning frameworks for automatic sleep staging and auditory attention decoding. Key contributions include personalized sleep scoring algorithms that adapt to individual physiological variations and deep learning models for decoding brain responses to natural speech, enabling applications in hearing assistance and brain-computer interfaces. Recent publications (2022-2025) demonstrate a cohesive research trajectory centered on overcoming real-world challenges in wearable EEG: improving long-term reliability through electrode configuration studies, developing standardized data pipelines, and validating ear-EEG against clinical polysomnography. His work bridges engineering innovation with clinical sleep medicine, emphasizing at-home deployment and user-specific adaptation. Dr. Mikkelsen leads significant research projects including: Event based attention detection (2021-2024) Ear-EEG sleep monitoring (2015-present) His work receives funding from Danish research councils and involves collaborations with clinical partners for validation studies in naturalistic settings. As part of Aarhus University's Biomedical Engineering group, he utilizes advanced laboratories for sensor development and signal processing, contributing to the department's strategic focus on healthcare technology innovation.
Ulrik Dam Nielsen is an Associate Professor in the Section for Fluid Mechanics, Coastal and Maritime Engineering at the Department of Civil and Mechanical Engineering, Technical University of Denmark (DTU). He also held an external position as Associate Professor II at the Norwegian University of Science and Technology (NTNU) from 2014 to 2023, reflecting strong international collaboration. His work contributes to UN Sustainable Development Goals related to sustainable maritime operations and clean energy. His research focuses on naval architecture and ship motion dynamics , particularly in the context of sea state estimation , added resistance in waves , and real-time prediction of vessel responses . He integrates data analytics , estimation theory , and machine learning to develop methods for monitoring hydrodynamic performance and enhancing maritime safety and energy efficiency. A central theme of his work is using ships as mobile wave sensors—transforming operational vessels into 'sailing wave buoys' for environmental monitoring. His recent publications show a clear shift toward data-driven methodologies, especially machine learning applications in sea state estimation, added resistance modeling, and performance monitoring. These works span journals like Ship Technology Research and Journal of Offshore Mechanics and Arctic Engineering , and conferences such as IEEE MetroSea, highlighting interdisciplinary innovation at the intersection of classical marine engineering and modern AI. Best Paper Presented by a Young Researcher Award (First Classified), 2024 (jointly awarded) He actively supervises PhD students—such as R. E. G. Mounet, M. Mittendorf, J. P. Tomy, and A. Oikonomakis—on projects funded by DTU and collaborative initiatives. His leadership in projects like WEFOSWAB (Wave Estimation and Forecasting Using Ships as Buoys) and data-driven added resistance modeling underscores his role in advancing smart maritime technologies. He also contributes to open science through the public release of datasets such as NetSSE . He teaches core courses including Introduction to Ships and Floating Structures , Marine and Ocean Engineering , and Ship Operations , shaping the next generation of maritime engineers.
Per Kvols Heiselberg serves as Professor and Deputy Head of Department for Research at the Department of Construction, Urban and Environmental Engineering within the Faculty of Engineering and Science at Aalborg University. He leads the Research Group for Energy in Buildings, focusing on building physics and energy efficiency. His research spans over three decades with significant contributions to zero-energy buildings and hybrid ventilation systems. His research interests encompass building physics, energy efficiency of buildings, design and operation of low- and net-zero energy buildings, the role of buildings as 'prosumers' in future smart energy systems, and data analysis for online optimization and fault detection of building operations. He has been internationally recognized for developing concepts for zero-energy buildings and hybrid ventilation through leadership in large international projects. His recent publications (2023-2025) show a strong focus on practical applications of building energy systems, including AHU performance tracking, fault detection methodologies, occupant behavior analysis, and advanced ventilation strategies. His work demonstrates a progression from theoretical building physics to practical implementation and monitoring in real-world buildings. Det Bæredygtige Element, Personprisen (2018) ELforsk Prisen 2017 As indicated by his extensive publication record (over 637 publications) and supervision of 14 PhD students, Professor Heiselberg maintains significant research activity and academic leadership. His work spans theoretical research, practical implementation, and policy influence, as evidenced by his recent involvement with the Danish Climate Council. He has organized major conferences including CLIMA 2016 and the Nordic Passive House Conference, demonstrating his leadership in the field. Professor Heiselberg leads research teams working on energy-efficient building technologies, including the Research Group for Energy in Buildings at Aalborg University. His projects often involve interdisciplinary collaboration across multiple institutions and countries, focusing on practical solutions for sustainable building design and operation.
Ming Shen is an Associate Professor at the Department of Electronic Systems, part of The Technical Faculty of IT and Design at Aalborg University. His research focuses on antennas, millimeter-wave systems, and AI-driven RF sensors with applications in 5G/6G communications, biomedical engineering, and smart systems. His research interests span antenna design (including phased arrays, metamaterials, and compact structures), AI integration in electromagnetic systems, and medical sensor technologies. Recent projects include drone-based electromagnetic signature analysis, vibration energy harvesting for pacemakers, and smart healthcare systems for posture recognition and surgical site infection monitoring. Key projects include DRONES: Drone-Obtained Electromagnetic Signatures (2024–2028), Sensor Intelligence for Healthcare and Sports (2022–2027), and DeepBone (2021–2022), which explored deep learning for surgical infection detection. His work also bridges machine learning and electromagnetic design, with breakthroughs in surrogate modeling and automated antenna optimization. Ming Shen has supervised 6 PhD students and published over 160 peer-reviewed articles. Notable contributions include AI-assisted NLOS sensing, ultra-wideband antenna innovations, and medical applications such as electrical impedance-based bone healing assessment.
Mohammad Naser Sabet Jahromi is an Assistant Professor at the Department of Architecture, Design and Media Technology, Aalborg University, Denmark. He is affiliated with the Visual Analysis and Perception Centre for AI Ethics, Law and Policy. His research focuses on explainable AI (XAI), biometrics, machine learning, and ethical AI applications in legal and medical domains. He actively participates in interdisciplinary projects like REPAI: Responsible AI for Value Creation (2023-2027), which explores AI ethics, computational discourse analysis, and value-driven AI systems. His educational background is not explicitly detailed in the provided text, but his research trajectory indicates strong expertise in computer science and AI systems. Key research interests include interpretable machine learning models, privacy-preserving biometric systems, and AI applications in asylum adjudication and educational assessment. Recent work emphasizes developing XAI frameworks like SIDU-TXT for NLP, verifying machine unlearning mechanisms, and automating large-classroom assessments. His projects bridge technical AI advancements with societal implications through collaborations with legal and ethical scholars. Notable contributions include datasets evaluating XAI methods in medicine and methodologies for transparent AI decision-making. He has participated in conferences such as ICPR 2024 and JURISIN 2023, showcasing interdisciplinary research impact.
William Henrich Due serves as a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, within the Machine Learning section. His work intersects with the SCIENCE AI Centre and leverages the department's high-performance compute cluster for research in quantum computing, sustainable AI, and medical applications. Research focuses span quantum machine learning (biomolecular simulations, photonic processors), sustainable AI systems (energy efficiency, climate impact), and clinical applications (EEG analysis, medical imaging). His recent publications reveal strong activity in quantum-classical hybrid systems, with 8/15 recent papers addressing quantum computing challenges. The work emphasizes practical implementations in medical imaging and resource-constrained environments. His research aligns with DIKU's Machine Learning section priorities including medical imaging biomarkers and sustainable computing. Key infrastructure includes TreeSense for remote sensing and the department's dedicated compute cluster. No scientific awards were explicitly documented in the provided materials. Due contributes to DIKU's teaching mission as a Lecturer while engaging with the SCIENCE AI Centre's interdisciplinary initiatives. His work connects with medical imaging applications and quantum computing infrastructure development. Active in the Machine Learning section's research ecosystem, his work intersects with medical imaging analysis and quantum computing applications, utilizing specialized resources like TreeSense for environmental monitoring.
Darko Zibar is a Professor at the Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU), and leads the Machine Learning in Photonics Systems (MLiPS) group. He holds a M.Sc. in Telecommunication and a Ph.D. in Optical Communications from DTU (2004, 2007). As a Visiting Professor, he has contributed to research at Politecnico di Torino, Friedrich Alexander University of Erlangen, University of California Santa Barbara, and University of Colorado, Boulder. Research Interests: Machine learning applications in optical communication systems Digital signal processing for classical and quantum photonics Optimization of Raman amplifiers and frequency combs Nonlinear fiber optic transmission modeling Photonic reservoir computing with silicon microring resonators Scientific Contributions: His work includes record-breaking achievements in optical phase noise measurement (approaching quantum limits) and programmable gain Raman amplifier design (S+C+L band). He has received prestigious awards such as the ERC Consolidator Grant (2017), Humboldt Bessel Research Award (2021), and Villum Investigator Award (2023).
