Jan Østergaard is a Full Professor in Information Theory and Signal Processing at Aalborg University's Department of Electronic Systems. He leads the AI and Sound research section and directs the CASPR center. His expertise spans AI-driven acoustic signal processing, information theory, and EEG signal analysis. Østergaard holds a M.Sc. from Aalborg University and a PhD (cum laude) from Delft University of Technology. Major awards include the Danish Young Researcher’s Award and a EURASIP Best Thesis honor. His work focuses on speech enhancement, sound zone technologies, and neural tracking of auditory attention. Recent research emphasizes low-latency speech transmission, deep learning for sound field control, and robust voice activity detection. He serves on editorial boards and national committees, advancing Denmark’s sound technology initiatives. Education: M.Sc. (Aalborg, 1999), PhD (Delft, 2007) Research interests emphasize practical AI applications in sound systems, including hearing aid improvements, data-efficient acoustic modeling, and feedback control in networked systems. Over 210 publications and 17 active projects reflect his interdisciplinary impact across academia and industry.
Jesper Rindom Jensen is an Associate Professor in the Department of Electronic Systems at Aalborg University, Denmark, under the Technical Faculty of IT and Design. He is the Head of the Audio Analysis Lab, a leading research group in audio signal processing, since 2023. His work bridges theoretical signal processing and practical applications in artificial intelligence and audio systems. Full Name: Jesper Rindom Jensen Institution: Aalborg University School: The Technical Faculty of IT and Design Department: Department of Electronic Systems Research Lab: Audio Analysis Lab Email: jrj@es.aau.dk Office: Fredrik Bajers Vej 7B, B5-206, 9220 Aalborg Øst, Denmark Education: M.Sc. in Electronic Systems, Aalborg University (cum laude, 2009) Ph.D. in Signal Processing, Aalborg University (2012) Research Interests: Jesper Rindom Jensen's research centers on audio signal processing, with a strong emphasis on artificial intelligence, speech enhancement, noise reduction, beamforming, and multichannel systems. His work applies to diverse domains including robot and drone audition, spatial audio, and active noise control. He develops novel filtering techniques, including variable span linear filters and harmonic beamformers, to improve speech quality and intelligibility in noisy and reverberant environments. Publication Trends: His recent publications (2023–2025) show a strong trend toward integrating deep learning with classical signal processing, particularly in direction-of-arrival estimation, underwater acoustics, and robust multichannel systems. There is a clear focus on real-world applications, including sound zone control, active noise control, and limited-data scenarios using knowledge distillation. His work consistently emphasizes robustness, efficiency, and practical deployment. Scientific Awards and Recognition: AAU Talent for emerging research leaders Recipient of a competitive postdoc grant from the Danish Independent Research Council Advising and Grants: Jesper has supervised multiple PhD and master’s students, including Nørholm, Karimian-Azari, Zhang, and Wang. He has led significant research projects such as 'Sound Processing for Robots and Drones' (2018–2020) and participated in others related to joint audio-visual tracking and speech enhancement. His research has been supported by national funding bodies, reflecting its innovation and impact. Labs and Teams: He is a founding and core member of the Audio Analysis Lab at Aalborg University, which focuses on cutting-edge audio signal processing and AI-driven solutions. The lab fosters interdisciplinary collaboration and has produced numerous publications, datasets, and real-world applications. Jensen’s leadership since 2023 underscores his pivotal role in shaping the lab’s research direction.
Hanne Leth Andersen is the Rector of Roskilde University and a Professor of University Pedagogy. She holds a PhD and has extensive experience in higher education leadership, including roles as director of the Centre for Teaching Development at Aarhus University and director of the Learning Lab at Copenhagen Business School. Her research focuses on foreign language didactics, university pedagogy, educational quality, and innovative teaching methods. Education: PhD in University Pedagogy. Previous academic positions include Professor of University Pedagogy at Aarhus University and Copenhagen Business School. Research interests emphasize exam form innovations, teaching development, language learning methodologies, and the role of foreign languages in education. She advocates for educational quality and pedagogical strategies to enhance student learning environments. Key awards include Chevalier de l'Ordre de la Légion d'Honneur (France), Commandant of the Ordre des Palmes Académiques (France), and Dannebrog Order (Denmark). Notable contributions include developing teacher training programs and advising on educational policies in Norway, Sweden, Finland, and France. Advising and grants: Pioneered collegial supervision methods for teacher competence development at Aarhus University, contributed to Norway’s university quality systems evaluations, and advised on French bachelor’s program reforms. Engaged in strategic board roles within research, education, and cultural institutions. Labs/teams: Active in Roskilde University’s Rectorate leadership, previously directed Learning Lab at CBS, and collaborates internationally on educational strategy initiatives.
Per Bækgaard is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), specializing in Cognitive Systems. He serves as Head of Study for Human-Centered Artificial Intelligence, leading research in human-computer interaction, user experience, eye tracking, and cognitive neuroscience. His work bridges AI and human cognition to create systems that enhance daily life and support meaningful tasks. PhD, MSc EE, Technical University of Denmark His research interests center on Human-Computer Interaction (HCI) , User Experience , and Human-Centered Artificial Intelligence , with strong emphasis on Eye Tracking , Cognitive Neuroscience , and Digital Media . He explores how digital systems can adapt to users’ cognitive states using physiological signals like pupil dilation and gaze patterns, aiming to improve learning, health, and decision-making. His work aligns with UN Sustainable Development Goals in health and education. The recent publications reflect a strong trend in integrating eye tracking and pupillometry with AI-driven adaptive systems , particularly in education and healthcare. Themes include generative AI in learning , trustworthy AI in supply chains , and digital micro-interventions for mental health . The interdisciplinary nature spans computer science, psychology, and biomedical engineering, showcasing a cohesive focus on human-centered technology evaluation. Scientific Awards: Best Paper Award, 26 Jun 2020 – for contributions to gaze interaction research Per Bækgaard actively supervises PhD students and leads multiple research projects, including those involving generative AI in education , digital phenotyping , and AI in nursing and mental health . He is the main supervisor for several PhD projects and a co-supervisor or examiner in others, demonstrating a strong commitment to academic mentoring. His grant involvement includes projects funded by DTU and collaborative research initiatives in digital health and AI. He is part of the Cognitive Systems group at DTU, contributing to interdisciplinary research in AI, neuroscience, and human factors. His team collaborates on projects involving real-time physiological monitoring, adaptive interfaces, and AI-mediated learning systems, positioning him at the forefront of human-centered AI research in Scandinavia.
Daniel Spikol is an Associate Professor at the Department of Computer Science , University of Copenhagen , affiliated with the Center for Digital Education and Human-Centred Computing section. His research focuses on multimodal learning analytics, computational thinking, and physical computing technologies that enhance learning, play, and reflection. Keywords: Learning Analytics, Human-Computer Interaction, Computational Thinking His recent work examines: Collaborative task design impacts on knowledge construction AI trust dynamics in educational contexts across six countries Smart learning environment integration with MMLA Design frameworks for multimodal analytics systems Key publications (2023-2025) analyze: Cultural factors in AI adoption Collaborative learning metrics Speech analytics for language acquisition He leads research bridging ambient computing, social signal processing, and educational innovation through physical computing toolkits like Talkoo (2016) and mBox (2024).
Henriette Skovgaard Andersen is a researcher at the Department of Biochemistry and Molecular Biology, University of Southern Denmark. Her work focuses on molecular biology, genetics, and RNA splicing mechanisms, particularly in neurodegenerative diseases like spinal muscular atrophy and Costello syndrome. She utilizes advanced sequencing technologies and oligonucleotide-based therapies to explore gene regulation and splicing defects. Her research spans translational medicine, with a focus on: RNA splicing regulation in genetic disorders Development of splicing-correcting therapeutics Neurodegenerative disease mechanisms Recent publications highlight her expertise in SMN2/SMN1 gene interactions, HRAS mutations, and resveratrol's role in metabolic disorders. Collaborations include international conferences and workshops on sequencing technologies and presentation techniques. She contributes to peer-reviewed journals and is involved in academic workshops, demonstrating active participation in research networks and educational events.
Merete Birkelund is an Associate Professor at Aarhus University's School of Communication and Culture, specializing in French Language, Literature, and Culture. She holds a PhD and focuses on political discourse analysis, semantics, argumentation, and language acquisition. Her research explores topics such as political rhetoric, irony in communication, and multilingualism. Key projects include INTERLING (2025-2026), investigating interlinguistic and intercultural approaches to language comparison, and EDU-it (2021-2023), enhancing theoretical linguistics education for French students. Other initiatives address language pedagogy, digital learning tools, and Scandinavian Romance studies. Her publications span political discourse analysis, language transfer mechanisms, and textbook evaluations. She has contributed to journals like Mémoires de la Société Néophilologique de Helsinki and anthologies such as Synergies Pays Scandinaves . Birkelund has no listed scientific awards but actively engages in editorial work for academic publications and collaborates with institutions like Aarhus Katedralskole. She oversees research labs focused on political semantics and language education, emphasizing interdisciplinary collaboration.
Zaibei Li is a PhD Fellow at the Department of Computer Science (DIKU) of the University of Copenhagen , affiliated with the Human-Centred Computing section. Their research intersects Educational Technology , Multimodal Learning Analytics (MMLA) , and Human-Computer Interaction . Key Research Areas: Multimodal Learning Analytics for spoken language acquisition Collaboration analytics in hackathons and educational settings Design of smart, inclusive, and equitable learning systems Recent Work: 2024 contributions to EC-TEL , ICALT , and LAK24 conferences Development of open MMLA platforms and frameworks for human-centric analytics Contact: Email: zali@di.ku.dk Address: Sigurdsgade 41, 2200 København N.
Cumhur Erkut is an Associate Professor at Aalborg University's Department of Architecture, Design and Media Technology, part of The Technical Faculty of IT and Design. His work focuses on Embodied Interaction, Virtual Reality, and Sound and Music Computing. He leads the Multisensory Experience Laboratory and has contributed to projects like MAT-DYN-NET (mathematical modeling for network dynamics) and SooC (sound in urban environments). Research interests include sonic interactions in virtual environments, movement-sound relationships, and audio-based human-machine interaction. He has published over 100 articles, with recent work on AI-generated speech in VR, voice conversion algorithms, and multimodal improvisation systems. Awards include the 2018 Best Student Paper Award and a 2015 3D User Interface contest win. His editorial roles include co-editing the Journal of Somaesthetics and serving on the Audio Engineering Society board. Projects emphasize applied research in wearable tech, urban soundscapes, and interactive systems for health and creativity.
Zheng-Hua Tan is a Full Professor of Machine Learning and Speech Processing at the Department of Electronic Systems, Aalborg University, Denmark. His research focuses on advanced signal processing techniques, including speech enhancement, audio representation learning, and applications in hearing aid technology. He leads the Artificial Intelligence and Sound group and has authored over 284 publications. His work spans machine learning, state-space models, and deep learning applications in audio-visual speech processing. Notable contributions include pioneering methods for noise-robust keyword spotting, diffusion-based speech enhancement, and adversarial attack defense in ASR systems. He also explores cross-modal audio captioning and generative models. His research extends to 6G radio sensing and energy optimization in CubeSats. Tan holds an ORCID identifier (0000-0001-6856-8928) and maintains active collaborations through international conferences like ICASSP and INTERSPEECH. His lab develops practical solutions for real-world audio challenges, including hearing assistance systems and low-latency voice activity detection. He supervised 15+ PhD students and has secured multiple grants for projects in AI-driven audio innovation. Current work emphasizes self-supervised learning, PAC-Bayesian theory for dynamical systems, and bi-level optimization in pretraining frameworks.
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.
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.
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.
Laila Kjærbæk is an Associate Professor at the Department of Culture and Language , University of Southern Denmark. She is also an external lecturer at the University of Copenhagen . Her work spans research, teaching, and public engagement in child language acquisition and education. PhD in Danish Linguistics (University of Southern Denmark, 2011-2013) Masters in Danish/Nordic Studies (University of Southern Denmark, 2004-2007) Bachelor in Danish/Nordic Studies (University of Southern Denmark, 1999-2004) Laila focuses on child language acquisition , early childhood education , and teacher training . Her research examines grammatical development, language disorders (e.g., Developmental Language Disorder, DLD), and plurilingual approaches in education, particularly in Danish primary schools and teacher training programs. Her scholarly work includes studies on noun plural inflection, phonological complexity, and the impact of input frequency on language learning. She has contributed to national language screening programs and debated language education policies, emphasizing the importance of early language intervention and plurilingual pedagogy. Recipient of NetWords Short Visit Grant (2012) Active in public media as a language expert (2025 coverage in lex.dk and Liv i Skolen ) Laila leads projects like TiG: Plurilingualism in Primary School and Sprogstimulering af flersprogede børn , aiming to enhance language education and support for multilingual children. She supervises courses on linguistic diversity in audiological practice and theoretical linguistics.
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.
