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.
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.
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.
Peter Bank Mariager is a Part-time Lecturer at the Department of Materials and Production, affiliated with the Faculty of Engineering and Science at Aalborg University. His research focuses on signal processing, speech enhancement, and acoustic engineering. He contributed to a notable 2022 publication on speech intelligibility enhancement at the IEEE ICASSP conference. Education details are not explicitly provided in the source text. His work emphasizes practical applications of signal processing in audio systems, with a recent focus on multichannel speech enhancement techniques. Collaborations include researchers like A. J. Fuglsig and J. Østergaard. While no formal grants or advising roles are mentioned, his research output reflects active engagement in acoustic and audio signal processing domains.
Bjarke Lundgaard Gårdbæk is a Research Fellow at the Department of Electrical and Computer Engineering, Aarhus University. His work bridges biomedical engineering and audio signal processing. Research Interests Broad: Biomedical Engineering, Signal Processing Specific: Cardiovascular sound analysis, audio signal processing, machine learning applications in medicine Publications His recent research focuses on cardiovascular sound analysis, particularly exploring the origin and processing of audio signals recorded from the ear.
Torben Gregersen serves as a Temporary Lecturer in the Department of Electrical and Computer Engineering at Aarhus University. His academic role centers on applying engineering principles to agricultural challenges, specifically developing monitoring systems for pigs and sows to enhance animal welfare and farm efficiency through technological innovation. His research spans precision livestock farming, computer vision, sensor networks, RFID technology, and animal behavior monitoring. He designs systems using wireless sensors, depth cameras, and RFID for real-time health assessment and behavioral tracking of livestock, enabling early disease detection and performance optimization at the individual animal level. This work bridges engineering and agricultural sciences to create practical solutions for modern farming operations. Analysis of his 2013 publications reveals a cohesive focus on pig monitoring systems, with consistent integration of computer vision for behavioral analysis, RFID for feeding pattern tracking, and wireless sensor networks for physiological parameter monitoring. These interdisciplinary efforts demonstrate a systematic approach to solving livestock management challenges through technological synergy between hardware and software systems. Scientific awards: No awards or honors were mentioned in the provided information. Advising and grants: The available documentation contains no details regarding graduate student supervision or research funding sources. Labs and teams: Research activities appear to be conducted within the Department of Electrical and Computer Engineering infrastructure at Aarhus University, likely involving collaboration with agricultural science researchers. Specific laboratory facilities or dedicated research teams were not detailed in the source material.
Harrison Bo Hua Zhu is an Assistant Professor at the Section for Health Data Science and AI within the Department of Public Health at the University of Copenhagen. He joined the university in November 2024 and is also a member of the Machine Learning and Global Health Network and part of the research group led by Samir Bhatt and David Duchêne. His educational background includes: PhD in Modern Statistics and Statistical Machine Learning from Imperial College London (2019-2023), supervised by Seth Flaxman and Yingzhen Li MSci in Mathematics from Imperial College London (2015-2019), with an exchange year at École Polytechnique Fédérale de Lausanne Dr. Zhu's research focuses on developing probabilistic machine learning methods for public health applications, particularly in infectious disease modeling and phylogenetics. His work bridges theoretical advances in Gaussian processes and Bayesian deep learning with practical epidemiological challenges. He specializes in creating scalable models that can handle multimodal data sources including time series, satellite imagery, and genomic sequences. His publication record shows a clear progression from foundational machine learning methodology to impactful public health applications, with significant contributions during the COVID-19 pandemic as part of the Imperial College London Response Team. Recent work demonstrates expertise in Markovian Gaussian Process Variational Autoencoders and multimodal learning approaches for health data science. Dr. Zhu actively supervises students at multiple levels: Current PhD students: Mathilde Marie Brünnich Sloth (life-course epidemiology) and Mariya Pavlova (Climate Change AI) Previous students: Alexander Pondaven (MEng thesis on diffusion models), Qing Pan (MSc on meta-learning), and Gengjian Hu (MSc on climate data compression) He maintains strong industry connections through past work at Fano (2023-2024) and an internship at Amazon, demonstrating the practical applicability of his research. His GitHub activity shows active development of machine learning tools, including implementations of neural processes and Gaussian process models.
Jesper Jensen is an Assistant Professor at the University of Southern Denmark (SDU), affiliated with the Department of Design and Communication and the Centre for Learning Computational Thinking (CLCT). His primary research focuses on computational thinking, educational technology, and interdisciplinary design, particularly in how technical skills are transferred across academic and professional contexts. His recent work examines the application of computational literacy in project-based learning environments, emphasizing the interplay between theoretical frameworks and practical implementation. Notable studies include an empirical analysis of how students transfer computational competences from coding courses to broader design projects involving humanities and social sciences. Earlier research spans supply chain innovation, sustainability practices, and global manufacturing strategies, reflecting a dual focus on technological pedagogy and industrial systems. His publications address topics ranging from big data applications in supply chains to the socio-economic implications of manufacturing relocation. He holds a strong record of collaborative projects, including design for interactive web-based educational tools and cross-disciplinary partnerships. Contact: jesjen@sdu.dk .
Søren Bech is a Professor at the Department of Electronic Systems within The Technical Faculty of IT and Design at Aalborg University. His academic qualifications include a PhD from the Technical University of Denmark (1988) and an MSc in Electrical Engineering (1982). His primary research focuses on audio quality, acoustics, and sound zone technologies, with emphasis on perceptual audio evaluation, spatial audio systems, and cortical auditory attention decoding. He leads projects such as ISOBEL (Interactive Sound Zones) and SOUNDS (Service-Oriented, Ubiquitous, Network-Driven Sound), addressing challenges in dynamic audio environments and networked audio systems. Key research areas include automotive audio systems, headphone technology, and the integration of EEG-based attention decoding for personalized sound experiences. He has received recognition, including the Best Student Paper award in 2024. Collaborations span academia and industry, with involvement in organizations like the Danish Sound Cluster and Danmarks Forsknings- og Innovationspolitiske Råd. His work bridges theoretical acoustics with practical applications in consumer electronics and immersive media.
Jesper Jensen is a Professor at the Department of Electronic Systems, part of The Technical Faculty of IT and Design at Aalborg University. His research focuses on acoustic signal processing, machine learning, and speech enhancement, with particular expertise in applications like hearing aids, noise reduction algorithms, and deep learning architectures for audio systems. He serves as a project supervisor at institutions including Oticon A/S since 2007, and co-leads the CASPR (Centre for Acoustic Signal Processing Research) center. Key research interests include multichannel signal processing, robust speech enhancement in reverberant environments, and adaptive filtering techniques. His work integrates Bayesian methods, deep neural networks (DNNs), and sparse modeling to address challenges in audio localization, speech presence probability estimation, and sound zone control systems. Notable Achievements: Recipient of the prestigious 'Stor international pris' award in 2017 Over 135 publications in journals like IEEE Signal Processing Letters and conference proceedings Active collaborations with industry partners such as Oticon A/S His research outputs emphasize practical applications, including voice control systems for hearing aids, binaural speech enhancement in noisy environments, and acoustic reflector localization for robotics. Recent work explores transformer networks and learning-based frameworks for real-time audio processing.
Ilhan Aslan is an Associate Professor in the Department of Computer Science at Aalborg University (Denmark), affiliated with the Technical Faculty of IT and Design. His work focuses on Human-Centered Computing, with a strong emphasis on AI-driven interaction design, emotion-aware systems, and tangible/somaesthetic interfaces. He leads research in areas such as conversational AI, mobile computing, and smart environments, integrating machine learning with user-centered methodologies. Aslan's recent projects explore emotional speech processing, proactive AI agents, and creativity support tools, often bridging technical innovation with human well-being applications. His research spans over two decades, with notable contributions to mobile navigation systems (e.g., Bum Bag Navigator), gesture-based interaction, and ambient intelligence. Media coverage highlights his work on AI solutions for COPD patients and stress management applications. Aslan's approach combines rigorous technical development with ethnographic and participatory design practices, ensuring ethical and user-centric outcomes. His lab collaborates across disciplines, addressing challenges in HCI, AI ethics, and sustainable computing. Key Research Themes : Emotion-Aware AI, Human-AI Collaboration, Tangible Interaction, Mobile Health, Interactive Machine Learning Notable Projects : 'Feel my Speech' (haptic emotion conversion), 'BEHAVE AI' (ethical agent design), and somaesthetic smarthome systems Media Impact : Featured in 400,000+ Danes-relevant health tech stories and COPD patient support innovations Aslan's publications reflect a prolific trajectory from early mobile learning platforms (2000s) to cutting-edge generative AI and multimodal systems. His work consistently prioritizes real-world applicability through iterative prototyping and user studies, making him a key figure in both academic and applied HCI domains.
Vicente Cutanda Henriquez is an Associate Professor in the Department of Electrical and Photonics Engineering at the Technical University of Denmark (DTU), specializing in Acoustic Technology. His research integrates computational modeling and experimental validation in acoustics, with a strong focus on numerical methods such as the Boundary Element Method (BEM) and Finite Element Method (FEM). His research interests include acoustics, electroacoustics, microacoustics, transducer modeling, vibroacoustics, and numerical simulation techniques . He actively explores applications in hearing aid technology, underwater noise, broadband absorption, and personalized audio devices. His work often involves model order reduction, hybrid FEM-BEM approaches, and visco-thermal losses in acoustic systems. The recent publications reflect a strong trend in computational efficiency, acoustic metamaterials, and personalized hearing solutions , with increasing emphasis on real-world applications in medical devices and environmental acoustics. His work bridges theoretical acoustics with practical engineering challenges in audio and biomedical technologies. Computationally efficient Prediction of Underwater Noise from Offshore Pile Driving Broadband acoustic absorption using cylindrical rods Vibroacoustic modeling of balanced armature receivers Sound propagation in curved ear canals Reduced-order BEM with boundary layer impedance He has been involved in multiple scientific activities, including organizing the 24th International Congress on Acoustics and chairing Dansk Akustisk Selskab. Though no formal awards are listed, his leadership in national and international acoustics communities underscores his recognition. Vicente Cutanda Henriquez supervises several PhD students, including Kulakauskas, Pedersen, Bække, and Cai, and is involved in major research projects such as Efficient Numerical Modelling of Smart Hearables and Broadband Vibroacoustic Shape Optimization . His work is supported by institutional and collaborative grants, focusing on innovation in hearing technologies and sustainable acoustic solutions. He is affiliated with the Acoustic Technology group at DTU, a leading research unit in electroacoustics and numerical acoustics, contributing to both fundamental research and industrial applications in sound engineering.
Tommy Sonne Alstrøm is an Associate Professor at the Department of Applied Mathematics and Computer Science, DTU Compute, Danmarks Tekniske Universitet (DTU). His research focuses on machine learning, signal processing, and nanotechnology-based sensor systems. Key research areas include: Explainable AI for time series and spectroscopy Diffusion models for speech enhancement Federated learning optimization Probabilistic modeling of biosignals His recent publications demonstrate expertise in neural network geometry, federated learning privacy, and spectroscopic data analysis. Current work involves self-supervised learning for variable-channel time series and road condition modeling using LiRA-CD dataset. Contact: tsal@dtu.dk
Bjørn Sand Jensen is an Associate Professor in the Department of Applied Mathematics and Computer Science (Cognitive Systems section) at the Technical University of Denmark (DTU), with research contributing to UN Sustainable Development Goals through interdisciplinary machine learning applications. His work bridges computer science, engineering, and biomedical domains with 21 publications to date. His research specializes in machine learning methodologies including active learning, preference learning, and Gaussian processes, applied to cognitive systems development. Key focus areas involve modeling cognitive aspects in information processing, predictive performance optimization, and mobile sensor applications. Current projects emphasize real-world implementations in hearing aid technology and biomedical imaging. Recent publications demonstrate a strong trend toward interdisciplinary deep learning integration, combining state-space models (Mamba) for audio processing and Gaussian processes for biomechanical analysis. This reflects his commitment to solving complex problems in neural processing systems and single-cell image reconstruction through novel statistical-computational hybrids. Advising: PhD student K. F. Olsen on "Towards Real-time Neural Processing in Hearing Aids" (2023-2026) Research Projects: Active: "Towards Real-time Neural Processing in Hearing Aids" (2023-2026) Completed: "Active learning in cognitive information processing systems" (2009-2013) Dr. Jensen collaborates across biomedical engineering and signal processing domains, particularly within DTU's Cognitive Systems research group, focusing on neural processing applications for hearing assistance and cognitive information systems development.
Luca Pezzarossa is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on real-time systems, embedded computer systems, digital microfluidics, compiler optimizations, and hardware accelerators. He leads projects such as the Edu4Chip: Joint Education for Advanced Chip Design in Europe initiative and supervises PhD students in areas like compiler optimizations for neural networks and speech enhancement algorithms. His academic journey includes contributions to interdisciplinary fields, combining computer engineering with biomedical applications such as biochip design and PCR optimization. He actively engages in open-source tool development, particularly using the Chisel framework for hardware design education and research. Key research themes include: Real-time systems and time-predictable architectures Compiler-driven optimizations for constrained devices Digital microfluidics for lab-on-a-chip systems Edge computing and TinyML applications Recent publications highlight innovations in microplastic detection on edge devices, dynamic channel pruning for speech enhancement, and parallel execution engines for digital microfluidics. His work aligns with sustainable development goals through environmental applications and energy-efficient technologies. Current projects involve: PhD Supervision: Andrea Cerioli (Compiler Optimizations), Riccardo Miccini (AI-to-Neural Network Mapping), Ehsan Khodadad (Time-predictable Systems) Research Grants: EU-funded Edu4Chip (2023–2025), multiple industry-academia collaborations Labs and teams: Leads the Embedded Systems Engineering group at DTU, focusing on interdisciplinary hardware-software co-design for real-world applications. Active in developing open-source frameworks for education and research.
