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Hamzah ZiadehView profile

Research Fellow

• Human-Robot Interaction
• Healthcare Technology
• Brain-Computer Interfaces
• +3 more

Hamzah Ziadeh is a PhD Fellow at Aalborg University's Department of Architecture, Design and Media Technology within The Technical Faculty of IT and Design. His research focuses on Human-Machine Interaction, particularly in Brain-Computer Interfaces, Social Robotics, and Healthcare Technology. He is affiliated with the Human Machine Interaction section and contributes to interdisciplinary projects. His research interests include adaptive learning systems using passive BCI, social robots' impact on user agency, and interventions to reduce food waste via mobile gaming. He explores data-driven healthcare interfaces for chronic disease management, emphasizing dashboards and AI in patient care. Ziadeh has actively participated in projects like RES-Q+ (2022–2026), enhancing stroke care through international registries, and AZV (2022–2024), leveraging mobile apps for patient outcomes. He contributed to IRENE (2018–2023), improving stroke care networks via data visualization. His work bridges technology and human behavior, aiming to improve healthcare outcomes and sustainable practices through innovative interface designs.

• Information Retrieval
• Data Science
• Text Analytics
• +5 more

Christina Lioma is a Full Professor at the Department of Computer Science (DIKU), University of Copenhagen . She has held academic positions including Associate Professor (2014-2018) and Freja Fellow/Assistant Professor (2012-2013) at the same institution. M.Hons (University of Glasgow, 2001) M.Sc. (University of Manchester, 2003) Ph.D (University of Glasgow, 2007) Her research focuses on Information Retrieval , Text Analytics , and Recommender Systems within Applied Machine Learning and Natural Language Processing . Recent work examines fairness-relevance tradeoffs in recommendation systems and neural mechanisms for knowledge conflict tracing. Recent publications in Nature Communications and top conference proceedings (WWW, SIGIR) explore hybrid computation architectures, brain-based language generation, and fairness evaluation metrics. She actively participates in academic conferences as organizer and speaker, including the European Conference on Information Retrieval.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +3 more

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.

• Information Retrieval
• Machine Learning
• Evaluation
• +4 more

Maria Maistro is a Tenure Track Assistant Professor at the Department of Computer Science, Faculty of Science, University of Copenhagen. Her research focuses on Information Retrieval (IR) with emphasis on evaluation, reproducibility, click log analysis, learning to rank, expert search, and machine learning applications to IR. She contributes to the AMAOS project aiming to advance expert search methodologies. PhD in Computer Science (IR) from University of Padua (2017) MSc in Mathematics (probability, stochastic methods) from University of Padua (2014) Maria's research addresses critical challenges in IR evaluation through stochastic modeling and user signal analysis. She investigates fairness-relevance tradeoffs in recommender systems, cross-cultural retrieval frameworks, and explainability techniques for language models and recommendation algorithms. Her work combines theoretical foundations with practical applications in healthcare records, insurance domains, and culinary contexts. Recent publications (2024–2025) demonstrate her expertise in: Retrieval-Augmented Generation (RAG) for cross-cultural adaptation Fairness evaluation in recommender systems EEG-based semantic relevance analysis Session-based recommendation explainability Language model pruning consistency Reproducibility frameworks for IR experiments Maria actively participates in academic activities through conference organization roles, including: Organizer, CENTRE@CLEF 2019 Organizer, CENTRE@NTCIR 2019 Organizer, CENTRE@CLEF 2018 Organizer, LEARNER 2017

Izabella ObálView profile

Associate Clinical Professor

• Amyotrophic Lateral Sclerosis (ALS)
• Neurology
• Rehabilitation Robotics
• +3 more

Izabella Obál is a Clinical Associate Professor at Aalborg University Hospital, affiliated with the Department of Clinical Medicine under The Faculty of Medicine. She is also part of the Center for Rehabilitation Robotics, focusing on interdisciplinary research at the intersection of neurology and robotics. Her work emphasizes neurological disorders like amyotrophic lateral sclerosis (ALS), motor neuron disease, and rehabilitation engineering. She has contributed to projects such as the CRERoB initiative (2024–2027) and the development of adaptive hybrid control frameworks for ALS patients. Her research spans biomarker development, diagnostic aids using transcranial magnetic stimulation, and assistive robotics for severely disabled individuals. Collaborations include experts in neurology, biomedical engineering, and robotics. Obál’s research outputs (17+ publications) highlight clinical-neuroengineering solutions, including studies on Penn upper motor neuron scoring variability and thrombolysis in pregnancy-related strokes. Her work is published in journals like Journal of Neurology and Annals of Clinical and Translational Neurology . She actively participates in multidisciplinary teams advancing rehabilitation robotics, aiming to improve quality of life for individuals with progressive neurological conditions. Current projects focus on tongue-brain interfaces and exoskeleton systems for motor-impaired patients.

Muhammad Ahmed KhanView profile

Researcher

• Brain-Computer Interface
• Biomedical Signal Processing
• Rehabilitation Engineering
• +3 more

Muhammad Ahmed Khan is a Researcher in the Department of Health Technology at the Technical University of Denmark (DTU), specializing in Biomedical Signal Processing and Digital Health. His work addresses critical healthcare challenges through engineering and AI-driven innovations, with a focus on brain-computer interfaces and neurorehabilitation systems. His research spans Brain-Computer Interface, Rehabilitation Engineering, Artificial Intelligence, and Biomedical Signal Processing. Khan develops assistive technologies like the 'Brainy Home' virtual smart home system for wheelchair control via neural signals, conducts systematic reviews on functional electrical stimulation for stroke recovery, and explores AIoT frameworks for pandemic response. His fingerprint analysis highlights expertise in Motor Imagery, Common Spatial Patterns, and Machine Learning applications in healthcare. Analysis of his 2022-2023 publications reveals strong interdisciplinary trends: integrating signal processing with clinical rehabilitation (e.g., post-stroke recovery systems), applying AI/IIoT to healthcare informatics (e.g., COUNTERSAVIOR virus detection), and advancing materials science for biomedical implants. His work appears in high-impact journals including Frontiers in Neurology , IEEE Internet of Things Journal , and ACS Omega , demonstrating convergence of engineering, AI, and clinical medicine. As part of DTU's Health Technology department, Khan contributes to a collaborative research ecosystem focused on digital health solutions, with external partnerships spanning multiple countries as indicated by his international publication collaborations.

• Machine Learning
• Natural Language Processing
• Quantum Computing
• +3 more

Shivam Adarsh is a PhD Fellow in the Machine Learning section at the University of Copenhagen's Department of Computer Science (DIKU), actively contributing to the SCIENCE AI Centre. Based at Universitetsparken 1 in Copenhagen, he engages in interdisciplinary research spanning theoretical foundations and real-world applications of artificial intelligence. His research focuses on Machine Learning, Natural Language Processing, Quantum Computing, Medical Image Analysis, Sustainability Applications, and Remote Sensing. Key projects include cross-cultural recipe adaptation using Retrieval-Augmented Generation, emotion-aware conversational AI, quantum-enhanced biomolecular simulations, and environmentally sustainable AI development. His work bridges computational theory with practical implementations in healthcare, environmental monitoring, and cultural systems. Analysis of his 2025 publications reveals dominant themes in Natural Language Processing (35% of works) and Quantum Computing (27%), with significant emphasis on Explainable AI and Medical Applications. His research consistently integrates multiple disciplines—such as combining quantum algorithms with drug discovery or embedding cultural diversity metrics into recommendation systems—demonstrating a systems-thinking approach to complex problems. As part of DIKU's Machine Learning group led by Professor Yevgeny Seldin, he utilizes the department's powerful compute cluster and participates in the TreeSense Centre for Remote Sensing and Deep Learning of Global Tree Resources. The group's collaborative environment spans medical data analysis, sustainability modeling, and biological data interpretation, with strong ties to Denmark's national AI initiatives.

• Machine Learning
• Artificial Intelligence
• Natural Language Processing
• +4 more

Felix Björklund Osmark is an Lecturer at the Department of Computer Science , University of Copenhagen. His work is affiliated with the Machine Learning section and the SCIENCE AI Centre, focusing on interdisciplinary applications of artificial intelligence. Current research interests include quantum computing , natural language processing , and sustainable AI . Recent publications highlight hybrid quantum-classical systems, biomedical applications of machine learning, and ethical considerations in AI development. His collaborative efforts span domains like structural biology , climate science , and medical diagnostics , leveraging the department's compute cluster and TreeSense research center resources.

• Machine Learning
• Natural Language Processing
• Information Retrieval
• +3 more

David Robert Shannon is an Instructor at the Department of Computer Science, University of Copenhagen. He contributes to teaching and research within the Machine Learning section, which participates in the SCIENCE AI Centre. University: University of Copenhagen Department: Department of Computer Science Section: Machine Learning His research interests span theoretical and applied machine learning, focusing on natural language processing, information retrieval, medical image analysis, computational biology, and quantum computing applications. He utilizes the department's powerful compute cluster for projects involving AI's environmental impact, quantum algorithms, and biomedical data modeling. Recent publications highlight work in quantum-inspired neural networks, sustainable AI, medical diagnostics, and cross-cultural computational frameworks. Key themes include ethical considerations in AI, hybrid quantum-classical systems, and multimodal data analysis. David collaborates with the Machine Learning section and SCIENCE AI Centre, leveraging resources like TreeSense for remote sensing and deep learning of global tree resources. The section's activities range from foundational research to applications in sustainability and biological data modeling.

Boris ModrauView profile

Associate Clinical Professor

• Neurology
• Stroke Research
• Transient Ischemic Attack
• +4 more

Boris Modrau serves as a Clinical Associate Professor at Aalborg University's Faculty of Health Sciences and works as a Senior Consultant at Aalborg University Hospital within the Department of Neurology and Neurophysiology. He is actively involved with the Neuroboost research group and maintains strong clinical and academic ties to the hospital-university interface in Aalborg, Denmark. Dr. Modrau's research interests focus on critical areas of neurology including stroke pathophysiology, transient ischemic attacks (TIAs), brain ischemia, neurorehabilitation techniques, and neuroengineering applications. His work bridges clinical practice with cutting-edge research, particularly in understanding long-term outcomes following cerebrovascular events and developing innovative rehabilitation approaches. A significant portion of his recent work examines fatigue patterns following mini-strokes and the development of patient-centered interventions for TIA patients. Analysis of his recent publications reveals a strong emphasis on large-scale cohort studies examining stroke outcomes, with particular attention to cardiac complications following mechanical thrombectomy, long-term fatigue patterns, healthcare utilization, and socioeconomic impacts of cerebrovascular events. His research increasingly incorporates neuroengineering approaches, especially brain-computer interface technologies for stroke rehabilitation. Dr. Modrau actively supervises research projects including a PhD project focused on developing patient-centered interventions for TIA patients by exploring prognosis and impact on everyday life. He has secured significant research funding including projects from the Novo Nordisk Foundation and has been involved in major collaborative research initiatives such as the Bevica Center for NeuroEngineering Solutions in Stroke Rehabilitation. He is an active member of the Center for Neurotechnology and Rehabilitation (CNR), participating in their organizational structure since 2021 and contributing to both spring and autumn seminars in 2025. His research has garnered substantial media attention, particularly regarding findings about long-term fatigue following transient ischemic attacks, which was covered by over 80 news outlets in May 2025.

• Auditory Working Memory
• Neural Mechanisms of Imagination
• Neuroscience of Music
• +5 more

David Ricardo Quiroga Martinez is a Postdoctoral Researcher in the Department of Psychology at the University of Copenhagen's Faculty of Social Sciences. His research focuses on the neural mechanisms underlying auditory working memory and musical imagination using intracranial EEG and MEG techniques. He investigates how the brain represents and manipulates sounds during perception and imagination, with applications for understanding psychiatric disorders and brain-computer interfaces. His research interests span Auditory Working Memory , Neural Mechanisms of Imagination , Neuroscience of Music , and Reward Coding . He employs invasive and non-invasive neuroimaging to study how musical sequences are processed in the brain, examining temporal hierarchies in predictive processing from pure tones to complex songs. His work bridges cognitive neuroscience, computational modeling, and clinical applications. Key publications reveal trends in neural representation of musical thoughts (2024), asymmetric reward prediction coding (2023), and temporal hierarchies in melody processing (2023). His research consistently explores how musical structure, predictability, and expertise shape neural responses, with implications for understanding disorders involving auditory imagery abnormalities. Reintegration Fellowship from the Carlsberg Foundation: The representation of musical thoughts in neurons of the human brain International Postdoc from DFF: The neural basis of musical imagination His collaborative network spans international institutions, with significant work on congenital amusia, atonal music processing, and neural gain modulation. Current projects involve decoding imagined musical sounds and investigating reward prediction errors in cortical networks, supported by major Danish research foundations.

Sadasivan Puthusserypady KumaranView profile

Associate Professor

• Biomedical Signal Processing
• Brain-Computer Interfaces (BCIs)
• Home Health Care Systems
• +3 more

Sadasivan Puthusserypady Kumaran is an Associate Professor at the Department of Health Technology, Technical University of Denmark. His research focuses on Biomedical Signal Processing, Brain-Computer Interfaces (BCIs), and Home Health Care systems. He holds a PhD from the Indian Institute of Science, with prior roles at National University of Singapore and Raytheon Systems Canada. He leads interdisciplinary projects in digital health and neurorehabilitation. B.Tech: Electrical Engineering, University of Calicut (1986) M.Tech: Instrumentation & Control Systems, University of Calicut (1989) PhD: Electrical Communication Engineering, Indian Institute of Science (1995) Research interests span neurophysiological signal analysis, assistive technologies, and AI-driven healthcare solutions. Recent work emphasizes cardiovascular risk prediction through ECG/sleep data, wheelchair control via EOG signals, and BCI systems for stroke rehabilitation. Notable achievements include Best Paper Awards (2020), Best Poster Award (2015), and recognition for teaching excellence (2017). Advising 5 active PhD projects: Brain-to-Text BCI systems, musculoskeletal digital twins, cardiovascular event prediction, neurology tool development, and wearable health monitoring. Collaborates with clinical and engineering teams across Denmark and internationally. Led the development of Digital Health and Biomedical Signal Processing research groups at DTU, advancing translational healthcare technologies.

Kim DremstrupView profile

Professor

• Biomedical Engineering
• Brain-Computer Interfaces
• EEG Analysis
• +7 more

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.

Søren Bruno ElmgreenView profile

Associate Clinical Professor

• Neurology
• Neuroprosthetics
• Spinal Cord Injury
• +7 more

Søren Bruno Elmgreen is a Clinical Associate Professor at Aalborg University Hospital, affiliated with the Faculty of Medicine and the Department of Neurology at Aalborg University. His clinical and academic work focuses on neurology, neurorehabilitation, and neuroprosthetics, particularly in patients with spinal cord injuries, stroke, and nerve compression syndromes. His research interests span neuroprosthetics , spinal cord injury recovery , nerve entrapment , paraplegia , and chemical warfare agent toxicology . He has contributed significantly to understanding functional restoration via neuromodulation and surgical interventions such as the Possover LION procedure. His work bridges clinical neurology with rehabilitation engineering. The recent publications highlight a strong trend in neuroprosthetic applications for restoring bladder, bowel, and locomotor functions in paraplegic patients, alongside investigations into stroke-related reading disorders and nerve compression in athletes. His interdisciplinary approach integrates neurology, surgery, and biomedical innovation. Scientific contributions and media engagement: Active publication record from 2014–2024 in journals like Spinal Cord , Military Medicine , and Ugeskrift for Læger Media contribution on stroke prevention titled Mindre blodpropper i hjernen (2024) ORCID profile active: 0000-0001-7640-0295 Dr. Elmgreen collaborates with multidisciplinary teams including neurosurgeons, physiatrists, and biomedical engineers. While no formal advising or grant information is listed, his role in clinical trials and peer-reviewed research suggests active supervision and research leadership. His work continues to advance functional recovery in neurological disabilities.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +4 more

Andreas Bjerregaard Jeppesen is a Research Fellow at the Department of Computer Science, University of Copenhagen, specializing in Machine Learning. His work bridges theoretical and applied research across diverse domains including quantum computing, biomedical informatics, environmental monitoring, and AI ethics. The Machine Learning section at DIKU explores foundational algorithms and their applications in Medical data analysis Remote sensing Biological modeling Sustainable AI Information retrieval . Andreas contributes to interdisciplinary projects like the SCIENCE AI Centre, leveraging the department's compute cluster for intensive simulations. His recent publications highlight trends in Quantum-inspired neural architectures Generative models for protein sequences Energy-aware AI development Neurological applications of ML Climate-conscious computing . Collaborations span computational biology, quantum chemistry, and federated learning for precision medicine.