Mahmoud Abouamer is a Researcher at Aalborg University's Department of Electronic Systems within The Technical Faculty of IT and Design. His work focuses on wireless communications and network optimization. Focus areas: Intelligent Reflecting Surfaces (IRS), Multiuser Systems, Digital Twin Technology Recent research trends include hypernetwork-based resource allocation and URLLC in 5G/6G systems Email: mahmoudabo@es.aau.dk His publications analyze wireless channel modeling, phase-dependent RIS configurations, and joint uplink-downlink optimization. Current projects emphasize practical implementations of IRS in future networks.
Dr. Mehdi Shafiei Joud is an Assistant Professor in the Department of Sustainability and Planning at Aalborg University , affiliated with the Technical Faculty of IT and Design. His research focuses on integrating satellite geodesy, machine learning, and remote sensing to advance environmental monitoring and Earth system dynamics analysis. Key projects include developing the Real-Time Earth Monitoring and Prediction Systems (REMPS) to assess climate-related risks like land subsidence and sea-level rise. Education : PhD in Satellite Geodesy (KTH Royal Institute of Technology, 2018) MSc in Geographical Information Systems (K.N. Toosi University of Technology, 2007) MSc in Geodesy (K.N. Toosi University of Technology, 2005) BSc in Surveying Engineer and Geoinformatics (K.N. Toosi University of Technology, 2002) His work emphasizes post-glacial rebound modeling , satellite gravity missions (GRACE/GRACE-FO) , and machine learning-driven data assimilation . Research outputs span interdisciplinary topics such as hydrological monitoring, crustal stress prediction, and climate resilience infrastructure. He actively contributes to UN Sustainable Development Goals related to environmental sustainability and education. Grants & Advising : No explicit grants or advisees listed in the text. Labs/Teams : None explicitly mentioned, though his REMPS initiative implies collaborative computational and geospatial teams.
Birger Andersen is a Professor in the Department of Engineering Technology and Didactics at the Technical University of Denmark (DTU), specializing in Energy Technology and Computer Science. His research actively contributes to UN Sustainable Development Goals through IoT security, wireless communication, and machine learning applications. He leads multiple projects including Machine Learning for Beehives Monitoring and Towards The 6G Authentication Protocol. Andersen's research spans post-quantum cryptography for IoT devices, security vulnerabilities in autonomous vehicle infrastructures, and wireless protocol analysis (LoRaWAN/NB-IoT). His work integrates machine learning for environmental monitoring and addresses jamming attacks on critical communication systems. Key focus areas include cryptographic algorithm implementation on microcontrollers and securing next-generation 6G networks. Recent publications reveal strong trends toward hybrid post-quantum cryptography solutions for resource-constrained IoT devices and 6G authentication systems. His research emphasizes practical security implementations in wireless communication protocols, with significant attention to LoRa-based systems and autonomous vehicle infrastructures. The work consistently bridges theoretical cryptography with real-world hardware constraints. Andersen supervises PhD student Turnip, T. N. (6G Authentication Protocol) and Master's student You, L. (Nature Assessment via Remote Sensing). His research is funded through projects like Machine Learning for Beehives Monitoring (2023-2026) and Jamming Against Critical Wireless Communication (2022-2023), with industry collaborations through InnoTech - TaskForce and F2D2: The Community for Dynamic Data. He is integral to DTU's Cybersecurity Labs and contributes to the Summer School in Cyber Security. His F2D2 project builds smart city cybersecurity frameworks through the Community for Dynamic Data initiative, addressing urban security challenges while advancing green transition technologies.
Morten Rieger Hannemose is an Assistant Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His research focuses on computer vision, medical imaging, 3D reconstruction, and AI-driven healthcare solutions. He is actively involved in projects like 'Fighting Cancer with Generative AI' and 'Decision support AI for Skin Lesions,' supervising multiple PhD students in these domains. His work contributes to UN Sustainable Development Goals related to health and innovation. Research interests include neural networks, medical image analysis, and computational imaging. Notable achievements include developing methods for digitizing material appearances and estimating diagnostic difficulty in skin lesion diagnosis. He collaborates internationally on topics such as monocular 3D pose estimation and multimodal data fusion. Morten has supervised PhD students in areas like crowd counting through remote sensing, generative AI for cancer diagnostics, and diffusion models for image segmentation. His projects often bridge theory and application, such as creating the 'Sportspose' 3D sports pose dataset. His email is mohan@dtu.dk , and his website is mortenhannemose.github.io .
Morten Mørup is Professor at DTU Compute, Technical University of Denmark. His research develops machine learning methods for life sciences, focusing on tensor decompositions, Bayesian inference, and complex network analysis. Education: PhD from DTU Informatics (2008) with research visits to Stanford and UC Berkeley. Research Expertise: Unsupervised learning, neuroimaging data analysis, and statistical network modeling applied to neuroscience and educational analytics. Awards: EliteForsk travel scholarship (2006), Lundbeck Foundation Fellowship (2012), and Ingeborg og Leo Dannins Scholarship (2021). Recent Publications focus on graph representations, educational data mining, and speech separation models.
Mathies Brinks Sørensen is a Visiting Postdoctoral Research Fellow at the Novo Nordisk Foundation Center for Biosustainability and a Postdoc in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). Their work spans interdisciplinary fields including metabolomics, nuclear magnetic resonance (NMR) spectroscopy, and remote sensing. Affiliation: Novo Nordisk Foundation Center for Biosustainability (Visiting Postdoc) Department: Applied Mathematics and Computer Science (Statistics and Data Analysis) Research Focus: Sørensen's research centers on integrating machine learning with analytical techniques like NMR and remote sensing to study microbial metabolomics. Key areas include deconvolution algorithms, experimental design optimization, and applications to food security via soil microbiome analysis. Publications Trends: Recent work combines satellite imagery with soil microbiome data (2025), advances metabolomics experimental frameworks (2024), and develops NMR deconvolution tools (2024). Their PhD thesis (2023) established foundational metabonomics methods for microbial secondary metabolites. Laboratory & Collaborations: Based at DTU, they collaborate with experts in spectroscopy (Gotfredsen), computational modeling (Strube), and environmental science (Clemmensen), contributing to sustainable development goals related to food security and planetary health.
Anders Riddersholm Bargum is a Researcher at Aalborg University's Technical Faculty of IT and Design , affiliated with the Department of Architecture and Media Technology . His work spans virtual reality, artificial intelligence, and advanced audio signal processing. Research Focus: Virtual Reality immersion, AI-driven voice conversion, and spatial audio mixing. Collaborations: Active in international networks for digital audio effects and sound computing. Key Publications (2025-2022): Explored AI speech synthesis in VR (2025), non-parallel voice conversion frameworks (2024), and spatial audio design principles (2022). Research trends emphasize deep learning integration, signal alignment techniques, and immersive audio environments. Technical Contributions: Developed differentiable all-pass filters for phase response estimation and optimized voice conversion systems at high sampling rates. His Virtual Reality research includes studies on avatar realism and social presence through synthetic speech.
Thiusius Rajeeth Savarimuthu is a Professor and Head of Unit at SDU Robotics , part of the University of Southern Denmark . His work bridges robotics, computer vision, and biomedical applications, with a focus on AI-driven medical imaging, surgical robotics, and human-robot interaction. His research explores diabetic retinopathy diagnostics via deep learning, robot-assisted ultrasound , bioimpedance sensing , and robot suturing . Projects like AIRCARE (AI-augmented robotics for cancer care) and RAPID (Robotic Arterial Puncture Device) highlight his commitment to healthcare innovation. Recent publications emphasize AI in medical imaging , robotic needle guidance , and automated diagnostics . His work is recognized through awards including the euRobotics Entrepreneurship Award 2023 and KUKA Innovation Award 2022 . Scientific Awards euRobotics Technology Transfer Award (2023) euRobotics Entrepreneurship Award (2023) KUKA Innovation Award (2022) Best Student Paper Award (2021) Innovation Prize - SDU (2020) He teaches Medical Imaging courses and supervises PhD projects on data-efficient robot learning and biomedical AI methods , reflecting his dedication to advancing robotics education and healthcare technology.
Gökçe Aydos is an Assistant Professor at the Department of Engineering Technology and Didactics Energy Technology and Computer Science, Technical University of Denmark. Her research focuses on fault-tolerant computing, FPGA-based systems, and hardware acceleration for machine learning. She holds a prominent position in embedded systems reliability and space computing domains. Her work emphasizes error detection mechanisms in hardware architectures, with notable contributions in parity-based error solutions and soft error mitigation techniques. Recent studies investigate trends in machine learning hardware design and scalable on-board computer systems for scientific missions. Research themes include: Soft error detection in FPGAs Fault-tolerant spaceborne computing Hardware-software co-design for reliability Machine learning accelerators Publications span from 2012 to 2023, showing sustained focus on embedded system reliability with applications in aerospace and medical imaging. No scientific awards have been listed in available records. Advising and grants information is not explicitly stated in provided materials. Current affiliations include affiliation with DTU's engineering technology programs and potential involvement in medical imaging instrumentation projects through digital PET research.
Mohammad Bokaei is a PhD Fellow at the Department of Electronic Systems within The Technical Faculty of IT and Design at Aalborg University, Denmark. His research integrates deep learning with wireless communication systems, focusing on speech transmission under channel constraints. His core research interests include: Wireless Communications: Channel modeling and adaptive transmission techniques Deep Learning: Neural network applications for communication systems Speech Processing: Real-time transmission and enhancement algorithms Signal Processing: Theoretical frameworks for low-latency systems Joint Source-Channel Coding: End-to-end optimization approaches Matrix Completion: Low-rank recovery for harmonic signal analysis Analysis of his 2024 publications reveals concentrated innovation in deep learning-driven wireless speech systems. Key trends include channel-configurable architectures, Gaussian channel optimization, and latency-constrained joint transmission-enhancement frameworks. His work bridges theoretical signal processing with practical wireless applications, particularly for assistive communication devices. No scientific awards were documented in the source material. Details regarding student supervision or research grants were not provided in the available information. No specific laboratory affiliations or research teams were referenced in the scraped content.
Henrik Stang is a Professor in the Department of Civil and Mechanical Engineering, specifically within the Structures and Safety division at the Technical University of Denmark (DTU). His research focuses on structural integrity, concrete durability, and advanced computational modeling in civil engineering. He is actively involved in cutting-edge projects related to digital fabrication, sustainable construction, and structural health monitoring. His research interests include high-performance concrete, reinforced concrete engineering, finite element methods, reinforcement corrosion, carbonation processes, 3D concrete printing, and structural dynamics. These areas align with UN Sustainable Development Goals related to sustainable cities and infrastructure. His work integrates experimental investigations with numerical simulations to enhance the safety and longevity of civil engineering structures. The recent publications reflect a strong trend toward digitalization in construction, including 3D concrete printing, data-driven structural assessment, and physics-informed machine learning for structural monitoring. His work spans materials science, structural mechanics, and environmental durability, with applications in both onshore and offshore engineering systems. Scientific Awards: Best Paper Award at the Digital Concrete 2020, 2nd RILEM International Conference on Concrete and Digital Fabrication Advising and Grants: Henrik Stang supervises several PhD students and is a key supervisor or co-supervisor in multiple doctoral projects, including those on digital twins for 3D concrete printing, fatigue damage characterization, and data-driven decision support for offshore structures. He leads the Villum Center for Advanced Structural and Material Testing (CASMaT) and participates in projects involving nonlinear system identification and damage detection in structural systems. Labs and Teams: He is the project manager of CASMaT (Villum Center for Advanced Structural and Material Testing), a multidisciplinary research center focused on advanced testing of structural materials. The team includes experts in composites, fatigue, finite element modeling, and non-destructive testing, and employs techniques such as X-ray computed tomography and digital image correlation.
Edward Alexandru Todirica is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark. His research focuses on multimedia applications, embedded systems, and software engineering, with a particular emphasis on distributed multimedia and real-time systems. He has contributed to the development of virtual seminar room technologies and educational curriculum design for IT programs. His work spans digital signal processing, internet of multimedia things, and system components. Key collaborations include industry partners like Siemens and Danfoss. Publications highlight trends in educational innovation, multimedia systems, and real-time software engineering. No scientific awards are explicitly mentioned in the available data.
Indra John Heckenbach is a Guest Researcher at the University of Copenhagen's Department of Cellular and Molecular Medicine, affiliated with the Molecular Aging Program. He completed his Ph.D. in 2023 focusing on deep learning approaches to characterize cellular senescence in aging and age-related diseases. His research integrates computational biology with molecular aging studies, with key interests including: Application of deep learning to senescence detection in medical imaging NAD+ metabolism in aging and disease processes Development of predictive models for cancer risk assessment Metabolic reprogramming in cellular aging Heckenbach's publications demonstrate a consistent focus on developing AI-driven diagnostic tools for age-related pathologies, particularly using nuclear morphology patterns to predict disease risk. His collaborative work spans clinical trials examining NAD+ therapeutics and fundamental research into metabolic regulation of aging.
Valkyrie Arline Savage is a Tenure Track Assistant Professor in the Human-Centred Computing section of the Department of Computer Science (DIKU) at the University of Copenhagen's Faculty of Science. Her research bridges the gap between physical input devices and digital systems, with a focus on creating interfaces that can be custom-designed, fabricated, or simply picked up to solve specific user problems in particular contexts. Dr. Savage studies physical input devices (like mice, game controllers, or surgical tools) as the ultimate bridge between humans and computers. Her work pushes for deeply custom interfaces that fit specific user needs for particular times, places, and tasks. She is fascinated by the interaction between sensing (which takes physical information and digitizes it) and fabrication (which takes digital information and physicalizes it). Her methodology centers on systems research, where project outputs are functional, novel systems that users can interact with directly, with contributions often coming from the algorithms required to make these prototypes work. Dr. Savage's recent publications demonstrate a strong research trajectory in innovative fabrication techniques that integrate electronics with traditional manufacturing processes. Her work spans 3D printing innovations, laser cutting applications, textile integration, and novel sensing mechanisms. Key themes across her research include democratizing fabrication technologies, combining digital and craft-based methods, and developing responsive input devices that adapt to specific user requirements. Her publications in top-tier venues like CHI, UIST, and TEI showcase her contributions to computational fabrication and human-computer interaction. Dr. Savage actively works with students in her fabrication lab, which is equipped with 3D printers, lasercutters, electronics tools, a CNC mill, and other fabrication equipment. Her advising focuses on interdisciplinary projects that explore the boundaries between technology and craft, including 3D printing combined with crochet, underwater 3D printing applications, and lasercut slot-together circuits. She has published 22 research outputs including journal articles, conference proceedings, a PhD thesis, and a patent, demonstrating consistent productivity in her field. Her research has practical applications across multiple domains including maritime automation, medical interfaces, and general human-computer interaction. The citations and downloads of her work indicate growing impact in her field, with applications spanning from specialized industrial contexts to everyday user interfaces. Dr. Savage's work represents a significant contribution to making fabrication technologies more accessible and integrated for creating custom interactive systems.
Niklas Gesmar Madsen serves as a Guest Researcher within the Machine Learning section at the University of Copenhagen's Department of Computer Science (DIKU), affiliated with the SCIENCE AI Centre and TreeSense research initiative. His work bridges theoretical machine learning with practical applications across quantum computing, medical diagnostics, environmental sustainability, and cross-cultural systems. His research spans quantum machine learning for biomolecular simulations, environmentally sustainable AI addressing energy consumption in models, fairness in recommender systems , and medical applications including EEG-based brain-computer interfaces and clinical decision support. Recent work demonstrates expertise in optical neural networks, quantum hardware calibration, and culturally adaptive AI systems for healthcare and culinary domains. Analysis of his 2025 publications reveals a multidisciplinary focus: 40% target quantum computing applications (biomolecular simulations, qubit control), 30% address AI ethics/sustainability (fairness, carbon footprint), and 30% develop medical/environmental tools (EEG analysis, tree resource monitoring). His work consistently integrates hardware constraints with algorithmic innovation. No scientific awards were documented in available sources. No information regarding student supervision or grant funding was identified in institutional records. Madsen operates within DIKU's Machine Learning section, leveraging the department's dedicated compute cluster and contributing to the TreeSense Centre for Remote Sensing and Deep Learning of Global Tree Resources. This initiative combines airborne laser scanning with deep learning for biodiversity monitoring, while the SCIENCE AI Centre provides cross-departmental collaboration on foundational and applied AI research.
