جست‌وجوی استاد و پژوهشگر

Kim Dremstrupمشاهده پروفایل

استاد

• Biomedical Engineering
• Brain-Computer Interfaces
• EEG Analysis
• +۷ مورد دیگر

Kim Dremstrup is Head of Department at the Department of Health Science and Technology, Faculty of Medicine, Aalborg University, Denmark. He is a leading researcher in biomedical engineering with a focus on brain-computer interfaces (BCI), EEG analysis, sleep research, and rehabilitation technology. He has played a pivotal role in establishing BCI and sleep research at AAU and leads the Aalborg BCI group, recognized internationally for early applications in stroke, spinal cord injury, and ALS rehabilitation. PhD in Computerized Sleep Analysis (1993) MSc in Biomedical Engineering (1984) His research centers on decoding brain signals for clinical applications, with major contributions including the invention of the Nightingale Automatic Sleep Analyzer and co-invention of the EDF bio-signal format used globally. His work enables assistive technologies for severely disabled individuals, particularly through hybrid control systems integrating brain and tongue signals. He is also involved in telerehabilitation and neonatal care technologies. Recent publications highlight advancements in error-related potential detection in stroke and motor disabilities, hybrid BCI-tongue control for ALS, and assistive robotics. His work spans neuroscience, biomedical engineering, robotics, and clinical medicine, with a strong translational focus. Inventor of the Nightingale Sleep Analyzer Co-inventor of EDF bio-signal format President, Danish MedTech Society (DMTS) Danish representative in IFMBE Chairman, Den Nordjyske Trivselsfond He actively supervises PhD students and participates in multiple research projects, including assistive robotics and multimodal control systems. He is also engaged in public discourse through media contributions on digital health and personalized medicine. He leads the Center for Rehabilitation Robotics and contributes to UN Sustainable Development Goals in health and well-being.

• Cardiology
• Heart Sound Analysis
• Infective Endocarditis
• +۳ مورد دیگر

Claus Holst-Hansen is a Physician Scientist at Aalborg University Hospital's Department of Cardiology within the Faculty of Medicine. He maintains an active research profile with publications spanning from 2007 to 2015, demonstrating sustained scholarly activity in cardiac medicine. His research interests focus on cardiology with particular expertise in heart sound analysis, coronary artery disease diagnostics, infective endocarditis treatment protocols, and stem cell applications in myocardial infarction. His work bridges clinical practice and engineering approaches to cardiac diagnostics, as evidenced by publications in both medical and biomedical engineering journals. Analysis of his publication trends shows a progression from foundational work in heart sound processing toward clinical applications in endocarditis treatment and regenerative medicine. His research demonstrates interdisciplinary collaboration across cardiology, infectious disease, and biomedical engineering domains. Professional activities include a presentation at the European Resuscitation Council in 2007, indicating engagement with broader medical education initiatives. His publications have garnered significant citations, with the heart sound segmentation paper accumulating 263 Scopus citations, reflecting substantial impact in the field.

Jesper Rasmussenمشاهده پروفایل

پژوهشگر

• Plasma Physics
• Fusion Energy
• Collective Thomson Scattering
• +۷ مورد دیگر

Jesper Rasmussen is a Senior Researcher in the Department of Physics at the Technical University of Denmark (DTU), working within the Plasma Physics and Fusion Energy research group. He is based at DTU’s campus in Kgs. Lyngby and is actively engaged in experimental and computational research related to nuclear fusion diagnostics. His research focuses on advanced diagnostic techniques for magnetically confined plasmas, particularly Collective Thomson Scattering (CTS) for measuring fast ions in fusion devices such as ASDEX Upgrade and Wendelstein 7-X. His work contributes directly to the development of diagnostic systems for future reactors like ITER and DEMO. Key areas include ion dynamics, wave-particle interactions, microwave instrumentation, and velocity-space tomography. The recent publications highlight a strong trend in developing and refining CTS diagnostics, improving real-time burn control capabilities, and advancing high-frequency microwave systems for plasma monitoring. His work bridges experimental physics, signal processing, and fusion engineering, with applications in both tokamaks and stellarators. Best talk at DFS 2016 He supervises multiple PhD students and is involved in active research projects related to ion dynamics, plasma heating, and microwave diagnostics. His collaborations span international fusion laboratories and include joint work with the ASDEX Upgrade and W7-X teams. He has contributed to the development of ultrafast digitizers and high-power microwave measurement systems. He leads and contributes to laboratory initiatives focused on fusion plasma diagnostics, particularly the CTS group at DTU, which develops and applies scattering techniques for energetic particle measurements in fusion plasmas.

Aleksandr Razumovمشاهده پروفایل

پژوهشگر ارشد

• Phase Noise
• Noise Characterization
• Optical Frequency Combs
• +۳ مورد دیگر

Aleksandr Razumov is a Postdoctoral Researcher at the Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU), focusing on phase noise characterization in photonic systems. His work integrates advanced signal processing with optical engineering to enhance frequency comb stability for telecommunications and metrology applications. Razumov earned his PhD from DTU in 2025 with a thesis titled "Subspace Tracking for Phase Noise Characterization of Optical Frequency Combs", supervised by Professor Darko Zibar. His educational background centers on photonics and signal processing methodologies. His research spans Phase Noise analysis, Optical Frequency Comb technology, and algorithm development including Subspace Tracking and Bayesian Filtering. These efforts target noise reduction in laser systems, with direct applications in optical communications and precision measurement. Key collaborations include DTU researchers like Mirov, Da Ros, and Zibar. Recent publications (2024-2025) reveal a concentrated focus on subspace-based noise characterization techniques, demonstrating methodological evolution from foundational tutorials to specialized comb applications. This trajectory highlights growing sophistication in tackling noise sources within Cr:ZnS laser systems. No scientific awards are documented in available records, though his work shows measurable impact through conference presentations and journal publications. Razumov contributes to DTU's photonic research ecosystem as both a recent PhD graduate and active postdoc, continuing his doctoral project "Subspace Tracking for Phase Noise Characterization of Optical Frequency Combs" (2022-2025) without indication of external grants or student supervision.

• Machine Learning
• Natural Language Processing
• Health Informatics
• +۳ مورد دیگر

Maher Harring Kassem serves as a Guest Researcher within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His position falls under the research staff category, reporting to Head of Section Professor Yevgeny Seldin, and contributes to the department's mission in theoretical and applied machine learning research. His research spans Machine Learning , Natural Language Processing , Health Informatics , Sustainable AI , Quantum Machine Learning , and Cross-Cultural Computing . This interdisciplinary profile integrates computational methods with real-world applications in mental health analysis, culinary adaptation systems, emotion recognition, and environmental sustainability, reflecting the department's focus on domains like medical data analysis and biological modeling. Analysis of his 2024-2025 publications reveals a distinct trend toward high-impact interdisciplinary work. Key themes include sustainable AI development (addressing energy consumption in models), quantum-biomolecular applications (free energy calculations), cross-cultural NLP systems (recipe adaptation), and clinical AI (nursing values evaluation). His output demonstrates technical depth across optical neural hardware, EEG-based semantic relevance, and fairness-aware recommender systems, while consistently tackling societal challenges like climate impact and healthcare equity. No scientific awards or fellowships were documented in the available materials. As a Guest Researcher, Kassem leverages the department's powerful compute cluster and participates in initiatives like the SCIENCE AI Centre and TreeSense project for remote sensing of global tree resources. His collaborative work spans medical imaging analysis, quantum computing applications, and sustainable AI development, utilizing the university's infrastructure for large-scale computational tasks in domains ranging from wetland conservation to quantum photonic computing.

• Human-Computer Interaction
• 3D Printing
• Wearable Computing
• +۳ مورد دیگر

Daniel Lee Ashbrook is an Associate Professor in the Department of Computer Science at the University of Copenhagen (DIKU). He specializes in Human-Centred Computing, focusing on tangible user interfaces, wearable technology, and computational fabrication. His work bridges hardware innovation with practical applications in interactive design. Research Themes : Embedded sensors, 3D-printed interactive systems, pneumatic computing, and accessible fabrication tools. Collaborations : Regularly works with researchers like Victor Savage and Hyeon-Jeong Kim on novel interface technologies. Recent publications highlight advancements in tactile feedback systems, multi-material fabrication, and sensor integration in everyday objects. His projects often combine physical and digital elements for intuitive user experiences. Key trends include democratizing hardware prototyping and enabling electronics-free interactivity. Awards : ISWC 2012 Best Papers Award. Contact : Email dan@di.ku.dk or call +45 53638312.

• Machine Learning
• Computer Science
• Quantum Computing
• +۳ مورد دیگر

Mathias Nygaard Larsen is an Instructor at the Department of Mathematical Sciences and Department of Computer Science (DIKU) at the University of Copenhagen. His research spans interdisciplinary domains including Machine Learning , Quantum Computing , and Computational Modeling , reflecting collaborations between mathematical and computer science communities. His publications highlight innovative approaches in Quantum-enhanced computational methods Explainable AI systems Biomedical data analysis Cross-cultural algorithmic frameworks Current work focuses on environmentally sustainable AI practices and quantum-classical hybrid models for biomolecular simulations, utilizing Copenhagen's advanced compute infrastructure.

• Machine Learning
• Artificial Intelligence
• Data Analysis
• +۳ مورد دیگر

Nadja Petersen is an Instructor at the Department of Computer Science , University of Copenhagen. Her work is affiliated with the Machine Learning Section and the SCIENCE AI Centre, focusing on theoretical foundations and applied research in domains like healthcare, environmental sustainability, and quantum computing. Email: nape@di.ku.dk Research Interests: Nadja contributes to areas including neural network hardware, sustainable AI practices, emotion recognition in conversational systems, and quantum computing applications for biomolecular analysis. Her work intersects machine learning with interdisciplinary challenges such as climate modeling, medical diagnostics, and computational biology. Recent Publications: Her research spans optical neural network processors, sustainable AI frameworks, and quantum-enhanced molecular simulations. Key trends include AI interpretability, cross-cultural recipe generation, and brain-computer interface applications. Labs & Teams: She collaborates with the Machine Learning Section at DIKU, a hub for foundational and applied research connected to the SCIENCE AI Centre. The section explores quantum machine learning, medical imaging, and environmental data analysis.

Martin Siemienski Andersenمشاهده پروفایل

دانشیار

• Cardiac Imaging
• Cardiovascular Disease
• Medical Imaging Systems
• +۳ مورد دیگر

Martin Siemienski Andersen is an Associate Professor at Aalborg University's Department of Medicine and Health Technology within the Faculty of Health Sciences. He works in the CardioTech research group and focuses on advanced cardiac imaging techniques. Education: MSc, PhD Contact: mvan@hst.aau.dk | +4599407867 His research spans multiple disciplines including: Cardiac imaging systems High frame rate echocardiography Myocardial tissue characterization Digital twin-based clinical decision support Coronary artery disease imaging Medical image processing algorithms Recent publications highlight his work on: Automated segmentation of coronary artery bypass images Digital twin applications for mechanical ventilation in ARDS patients Shear wave imaging for myocardial tissue analysis Acoustic window detection in echocardiography Contractile dynamics in left bundle branch block Awards: 2nd Place in Basic Science E-posters category at AIUM 2018 Key collaborations include researchers at: Computing in Cardiology Frontiers in Medicine Journal of Cardiothoracic Surgery Ultrasound in Medicine and Biology

Gilberto Berardinelliمشاهده پروفایل

دانشیار

• Wireless Communication Networks
• AI for Communication
• 6G Networks
• +۷ مورد دیگر

Gilberto Berardinelli is an Associate Professor at the Department of Electronic Systems, Aalborg University (AAU), within The Technical Faculty of IT and Design. His research focuses on 6G networks, wireless communication systems, AI-driven communication protocols, and industrial IoT (IIoT) applications. He leads projects like 6G-SHINE and Cell-free Massive MIMO for Industry 4.0, addressing challenges in interference management, resource allocation, and ultra-reliable low-latency communication. Education details are not explicitly provided in the text, but his academic career is evident through his extensive publications and project leadership roles. His research interests include subnetwork optimization, cooperative communication protocols, and spectrum management for industrial environments. Recent articles highlight advancements in interference coordination for 6G subnetworks, power-efficient protocols, and machine learning for resource allocation. His work emphasizes practical applications in factory automation, smart production, and wireless systems for Industry 4.0. Berardinelli has supervised 6 PhD students and contributed to 170+ research outputs. He collaborates with global institutions and industry partners, focusing on projects like Enabling ultra-reliable low-latency communication and Digital technologies for Industry 4.0. His lab activities include developing novel architectures for 6G in-factory communication and testing prototypes for real-world deployment.

Peter Kochمشاهده پروفایل

دانشیار

• Electronics
• Signal Processing
• Digital Filters
• +۳ مورد دیگر

Peter Koch is an Associate Professor at the Department of Electronic Systems, Aalborg University, affiliated with The Technical Faculty of IT and Design. His expertise spans Electronics, Signal Processing, and Software Defined Radio. He holds a PhD in Electronic Engineering (1996) and an MSc in Electronic Engineering (1989). Research focuses on digital filters, superconducting circuits, and embedded systems. Notable projects include SENSATION (self-sustaining computation systems) and GATOSS (air traffic optimization via satellite surveillance). He received the 2020 Teacher of the Year award at the Faculty of Engineering and Science. Key projects: 19 initiatives since 2001 Publications: 82 works, including 2025 Journal of Instrumentation study on superconducting circuits Active in IEEE and EURASIP conferences Advises 1 PhD student and contributes to interdisciplinary collaborations in superconductivity and AI-driven signal processing.

Mads Rovsing Jochumsenمشاهده پروفایل

دانشیار

• Neurorehabilitation
• Brain-Computer Interfaces
• Signal Processing
• +۳ مورد دیگر

Mads Rovsing Jochumsen is an Associate Professor at Aalborg University's Department of Health Science and Technology, within the Faculty of Medicine. He leads the Laboratory of Brain-Computer Interfaces and EEG, and serves as Semester Coordinator for the 3rd semester in medicine and medicine with industrial specialization. His expertise spans neurorehabilitation, brain-computer interface (BCI) technology, and assistive robotics for individuals with motor impairments. Education: PhD in Biomedical Engineering (2015), Aalborg University MSc in Biomedical Engineering (2012) BSc in Biomedical Engineering (2010) Research Focus: Jochumsen’s work centers on BCI systems for stroke, cerebral palsy, and multiple sclerosis rehabilitation. He explores signal processing, neuroplasticity induction, and gamified rehabilitation tools. His research also addresses telerehabilitation and neurological condition assessment using electrophysiological signals. Awards: Elite Research Travel Scholarship (2014) Annual Meeting Prize at Danish Biomedical Engineering Society (2015) Grants & Projects: He leads initiatives like 'Gamified BCI Training for Cerebral Palsy' (Elsass Foundation) and 'Brain-Controlled Exoskeletons for Stroke Rehabilitation' (VELUX FONDEN). Current collaborations include the Center for Rehabilitation Robotics, advancing hybrid exoskeletons for severely disabled individuals. Labs & Teams: Active in the Neural Engineering and Neurophysiology Center and the Center for Rehabilitation Robotics, focusing on interdisciplinary innovation in assistive technologies.

Ove Kjeld Andersenمشاهده پروفایل

دانشیار

• Speech Synthesis
• Signal Processing
• Digital Signal Processing
• +۷ مورد دیگر

Ove Kjeld Andersen is an Associate Professor at the Department of Electronic Systems within the Technical Faculty of IT and Design at Aalborg University. His research focuses on speech synthesis, signal processing, and digital filter design, with notable contributions to areas like approximate multiplication, phase information, and non-spectral features. He is actively involved in projects such as 'Access for All,' which aims to improve accessibility through technological solutions. Andersen has contributed to over 70 research outputs, including peer-reviewed articles and conference proceedings. His work spans adaptive signal processing, numerical robustness in bi-quad structures, and computational methods for line spectral frequencies. He has also engaged in public discourse on engineering education and IT workforce challenges in Denmark. Andersen holds leadership roles in committees like the Koordineringsudvalg for Aalborg Universitet og Ingeniørhøjskolen i København (IHK) and is a member of the Netværk om Handicap, Dialog og Teknologi. His research has been featured in media outlets discussing topics such as robotic education and synthetic voice technology.

Nicklas Sindlev Andersenمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Data Science
• Computer Science
• +۴ مورد دیگر

Nicklas Sindlev Andersen is a postdoctoral researcher in Data Science at the Department of Mathematics and Computer Science , University of Southern Denmark. His work bridges artificial intelligence with healthcare, mobility analytics, and network protocol design. Research Focus: AI-driven medical risk assessment, underwater communication protocols, mHealth applications for dementia care, and computational heritage storytelling. Collaborations: Active in international research networks spanning 6 countries/regions, with interdisciplinary partnerships in computer science, medicine, and digital ethics. Recent publications highlight his expertise in artificial intelligence (100% overlap), mHealth systems , and data-driven mobility analysis . Key methodologies include predictive modeling, value-sensitive design, and cross-layer network optimization. Collaboration network map shows dominant expertise in Computer Science (100%), with significant contributions to Artificial Intelligence (75%) and Location Data analysis (50%).

Anders Brandtمشاهده پروفایل

استاد

• Mechanical Engineering
• Signal Analysis
• Structural Dynamics
• +۵ مورد دیگر

Anders Brandt is a Professor and Head of the Department of Mechanical and Production Engineering at Aarhus University . His research focuses on mechanical systems, signal analysis, and structural dynamics, with applications in wind turbine technology and vibration analysis. Primary Affiliation: Department of Mechanical and Production Engineering, Aarhus University Research Interests: Operational Modal Analysis (OMA), damping estimation, offshore wind turbines, bearing diagnostics, digital twin technology, and uncertainty bounds in structural dynamics. Contact: abra@mpe.au.dk | Telephone: +45 29 12 58 15 His recent publications include advancements in virtual sensing schemes, periodogram ratio analysis, and non-contact measurement techniques using digital image correlation. Projects like "True Digital Twin technology for wind turbines" (2024-2025) and "Robust Identification of Modal Parameters" (2018-2021) highlight his work in practical and theoretical dynamics.