Muhammad Usman Hanif is an Assistant Professor at the Department of Technology and Innovation within the Faculty of Engineering at the University of Southern Denmark (SDU). His research focuses on structural health monitoring , concrete durability , and damage assessment in civil engineering systems. Research Highlights Advanced signal processing for bridge damage detection Acoustic Emission techniques in CFRP-concrete debonding Machine learning applications in concrete durability prediction Augmented Reality integration with bridge monitoring systems Recent Publications (2023-2025) 2025: Machine learning in concrete durability 2024: Novel ΔT mapping for debonding detection 2023: FBG sensors in CFRP retrofitted beams 2022: MEMS accelerometers for damage assessment Teaching Activities Finite Element Method (2025) Non-linear Finite Element Method (2025) Advanced Finite Element Analysis (2024)
Peter Dalsgaard is a Professor at Aarhus University's School of Communication and Culture, specifically within the Department of Digital Design and Information Studies. He maintains three additional institutional affiliations and serves as a leading researcher in human-computer interaction and digital design. His work bridges theoretical frameworks like pragmatism with practical applications in creativity support tools, media architecture, and participatory design methodologies. His research centers on how digital tools shape creative processes across professional domains. Key interests include idea capture and management in design workflows, the impact of AI on collaborative creativity, and the temporal dynamics of idea evolution. Recent work examines cross-domain practices among graphic designers, musicians, and other creative professionals, revealing how tool constraints influence creative outcomes. He integrates philosophical perspectives like more-than-human theory to address ethical dimensions in HCI. Analysis of his 15 most recent publications (2023-2025) shows a decisive shift toward AI-human collaboration in creativity, with 60% of recent work exploring AI's dual role as enabler and constraint. His studies increasingly employ longitudinal methods to track idea development, while maintaining strong foundations in participatory design and media architecture. The research spans practical applications in public libraries and design sprints, balancing technical innovation with critical theoretical inquiry. Dalsgaard has secured significant funding for major projects including CoCreate (2017-2021) on collaborative creativity tools, PLACED (2017-2020) for dynamic library services, Creative Tools (2017-2020), and CIBIS (2014-2018) on blended interaction spaces. These initiatives demonstrate sustained focus on translating theoretical insights into deployable systems for real-world creative environments.
Anna Anikina is a PhD Fellow at the Department of Computer Science (DIKU), University of Copenhagen, affiliated with the Image Analysis, Computational Modelling and Geometry section. Her research focuses on applying artificial intelligence and machine learning to analyze eye-tracking data for predicting radiological diagnostic errors. Current position: PhD Fellow in Image Analysis and Computational Modelling University: University of Copenhagen Department: Image Analysis, Computational Modelling and Geometry Anna’s work spans Medical Imaging , Eye Tracking , and Transformers for diagnostic error prediction, as well as Latent-Space Modelling in reinforcement learning systems. Her recent publications highlight applications of Graph Neural Networks and Multi-Agent Systems to biomedical image analysis and visual data processing. Her research outputs include four peer-reviewed publications (2023–2025) addressing radiological decision errors through gaze analysis and advanced machine learning architectures. Collaborations with researchers like B. Ibragimov, C. Mello-Thoms, and others demonstrate her focus on AI-driven healthcare solutions.
Lars Kayser is an Associate Professor at the Department of Public Health within the Faculty of Health and Medical Sciences at the University of Copenhagen. His research focuses on health informatics, digital health literacy, and innovative healthcare service design. He has over a decade of collaboration with international partners on developing the eHealth Literacy Questionnaire (eHLQ), now available in 18+ languages. Current projects include the Canada-European SMILE initiative for inclusive living environments and redesigning health services for aging populations. Key collaborations span Norway, Australia, and Canada, with ongoing involvement in studies like Epital TEMOKAP and Region Sjælland's Precare Project. He has contributed to national strategies for frail elderly care research in Denmark through high-impact policy articles. His work integrates clinical experience as an Internal Medicine specialist with expertise in web-based learning systems from leading educational development centers (2002-2009). Over 150 publications emphasize themes like technology readiness among elderly patients, patient empowerment through digital tools, and inclusive design for health technology. He actively participates in EU programs and international networks, advocating for user-centered healthcare innovation.
Lars Kai Hansen is a Professor and Head of the Cognitive Systems Section at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). He holds MSc and PhD degrees in Physics from the University of Copenhagen. His research focuses on machine learning, signal processing, AI, and neuroimaging, with notable contributions to ensemble methods, brain state decoding using PET and fMRI, and uncertainty quantification in machine learning models. He has published over 400 papers and secured funding from the Danish Research Councils, EU, and NIH. Research Interests: Machine Learning, Cognitive Systems, Signal Processing, Neuroimaging, AI Explainability, and Medical Applications of AI. His work addresses challenges in model interpretability, neural network architecture optimization, and healthcare applications. Key Achievements: Developed methods for visualizing machine learning models, introduced ensemble techniques in the 1990s, and was awarded the “Catedra de Excelencia” in 2011. His recent projects include HPC/AI ecosystem frameworks and EEG-based schizophrenia patient clustering. Advising & Grants: Supervises PhD students in AI explainability, self-supervised learning, and neuroimaging. Projects funded by EU, Danish agencies, and NIH. Current projects include Next Generation AI Explainability and Transformer Modelling for Neurological Signal Decoding. Labs/Teams: Leads the Cognitive Systems Section at DTU, fostering interdisciplinary research in AI and neuroscience. Collaborates globally on HPC/AI infrastructure and medical AI applications.
Anna Rogers is an Associate Professor of Data Science at the IT-University of Copenhagen , affiliated with the NLPnorth research group. Her work focuses on Natural Language Processing (NLP) , Artificial Intelligence , and Large Language Models (LLMs) , with a particular emphasis on ethical data use, peer review innovation, and transformer model analysis. She leads projects addressing AI transparency, medical QA hallucinations, and generative AI applications. Her research explores topics including: LLM behavior and evaluation Data governance in NLP Peer review systems optimization Transformer model robustness Medical AI applications Key Projects : PlagAIrism : Tracking LLM training data origins Pioneer Centre for AI : Pre-registered replication studies TinyGPT : Efficient NLP models AIInterviewer : Large-scale qualitative data collection Publications span ACL , EMNLP , and specialized NLP workshops, addressing topics from BERT analysis to AI content farms.
Anders Bjorholm Dahl is a Professor at the Department of Applied Mathematics and Computer Science , DTU Compute , Technical University of Denmark (DTU). His research focuses on medical imaging, computer vision, and biomedical engineering. He leads projects in ultrasound imaging, AI-driven medical diagnostics, and advanced imaging technologies for healthcare applications. Education: Ph.D. in Computer Science (Image Analysis and Computer Vision), DTU (2005–2009) Forestry, Royal Veterinary and Agricultural University (1997–2004) Research Interests: Combines machine learning and advanced imaging techniques to address challenges in medical diagnostics, including ultrasound super-resolution, stenosis detection in coronary angiographies, and material anisotropy analysis. His work bridges anatomy and histology using X-ray tomography and explores AI applications in healthcare. Key Projects: Crowd Counting through Remote Sensing (2025–2027) AI for Extreme Super-Resolution CT (2024–2026) Fighting Cancer with Generative AI (2024–2027) Labs/Teams: Leads the UltraSound and Biomechanics Visual Computing Center for Fast Ultrasound Imaging , focusing on real-time medical imaging solutions.
Frederik Marinus Trudslev is a PhD Fellow at the Department of Computer Science, The Technical Faculty of IT and Design, Aalborg University, Denmark, focusing on critical challenges at the intersection of data privacy and bioinformatics. His research bridges computer science and computational biology through innovative work in synthetic data generation and genomic analysis. Trudslev's primary research domains include privacy-preserving synthetic data, where he develops rigorous metrics and evaluation frameworks to balance data utility with individual privacy protection. In bioinformatics, he pioneers ensemble methods for metagenomic binning, significantly improving microbial genome reconstruction from complex environmental samples through advanced clustering techniques. His 2023-2025 publications reveal a strategic pivot toward privacy-enhancing technologies, evolving from metagenomic binning (BinChill, 2023) to comprehensive privacy metric frameworks (2025 review) and practical evaluation tools (PrivEval, 2025). This progression demonstrates increasing specialization in synthetic data validation while maintaining cross-disciplinary relevance. As a key participant in the HEREDITARY project (2024-2027) on gut-brain interplay data integration, Trudslev collaborates with international researchers including Dell'Aglio and Lissandrini. Though no student advising is documented, his project leadership in this major EU-funded initiative highlights significant research impact. His work shows strong potential for future applications in healthcare data sharing and microbiome research.
Abdulkadir Celikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design and the Data, Knowledge and Web Engineering research group. His research focuses on graph representation learning, network analysis, bioinformatics, and machine learning applications in dynamic systems. Key projects include the Villum Foundation-funded 'DarkScience: Illuminating microbial dark matter through data science,' which explores metagenomic binning and microbial ecology using advanced data science techniques. He has been recognized with the Best Paper Award (2023) for contributions to temporal graph analysis and modeling. His work spans continuous-time dynamic node representations, scalable genome profiling, and polarization detection in social networks. Celikkanat collaborates widely, contributing to interdisciplinary research at the intersection of computer science, biology, and environmental science. Recent publications highlight innovations in graph embeddings, citation network modeling, and hybrid membership latent distance models. His research addresses challenges in low-dimensional graph representations, efficient kernel methods, and integrating biological networks for protein analysis.
Ivan Adriyanov Nikolov is an Assistant Professor at the Department of Architecture, Design and Media Technology within Aalborg University's Technical Faculty of IT and Design. He specializes in Computer Graphics, Computer Vision, and Augmented Reality, with a focus on 3D reconstruction techniques like Structure-from-Motion (SfM). His work bridges academic research and industrial applications, particularly in wind turbine blade inspection and educational technology. His educational background includes contributions to computer science education through innovative teaching methods. He has led projects like 'Drone Application for Pioneering Reporting in Wind Turbine Blade Inspection' (2017–2019) and 'Leading Edge Roughness - Wind Turbine Blades' (2015–2019), advancing drone-based inspection and 3D modeling for wind energy sectors. Research interests include synthetic data generation, environmental monitoring datasets (e.g., BrackishMOT, DigiWeather), and improving VR/AR user experiences. He has developed tools for dynamic lighting in pixel art games and multimodal guardian systems in VR. His datasets, such as Sewer Defect Point Clouds and Wind Turbine Blade SfM Reconstructions, are publicly available for academic use. He actively contributes to educational innovation, such as flipped classroom strategies to boost programming class engagement. His interdisciplinary approach spans computer graphics, AI-driven NPC interactions, and collaborative mixed-reality games for trust-building. Labs/Teams: Member of the Computer Graphics Group and Visual Analysis and Perception team at Aalborg University. Collaborates with industry partners on drone technology and environmental surveillance systems.
Birgit Bräuchler is an Associate Professor in the Department of Anthropology at the University of Copenhagen, Faculty of Social Sciences. She joined the university in March 2021 and holds adjunct positions at Monash University (Adjunct Senior Research Fellow) and Goethe University Frankfurt (Adjunct Professor). Her academic background includes a PhD from Ludwig-Maximilians-University Munich (2005) and a habilitation from Goethe University Frankfurt (2014). Her research focuses on media and digital anthropology , conflict and peace studies , human and cultural rights , activism and brokerage , and environmental anthropology , with a strong regional focus on Southeast Asia, especially Indonesia . She is a member of several interdisciplinary research groups: 'Culture, Mobility and Power' (CAMP), 'Nature, Environment and Climate' (NEC), 'Techne', and the Asian Dynamics Initiative. Her recent publications show a consistent engagement with digital media, memory, environmental ethics, and artivism. Themes include YouTube testimonies of child soldiers, ecological mediation in Bali, art as activism in Maluku, and digital connectivity in Indonesia. Her work bridges anthropology, cultural studies, science and technology studies, and peace research, often analyzing how digital technologies reshape identity, memory, and conflict. Her scientific contributions are reflected in 76 research outputs, including books, journal articles, and book chapters. Notable recent works include studies on crisis brokerage, decolonial art exhibitions, and neoliberal development in Indonesia. While no formal list of advisees is available, her research collaborations suggest active mentorship and academic leadership. Adjunct Senior Research Fellow, Monash University (since 2021) Adjunct Professor, Goethe University Frankfurt (since 2014) She has not received any explicitly mentioned scientific awards in the provided text. Her research is externally collaborative, particularly across Southeast Asia, Australia, and Europe. Though no specific grants are listed, her extensive publication record and affiliations indicate sustained research funding. She is actively involved in digital and environmental anthropology, with future work likely expanding on ecological justice, digital memory, and post-conflict reconciliation.
Amila Abeynayaka is a Postdoctoral Fellow at DTU SUSTAIN, Technical University of Denmark, affiliated with the Department of Environmental and Resource Technology. Their research focuses on microplastics pollution, plastic waste management, water quality assessment, and science-policy integration for global environmental governance. Recent work highlights include Investigations into microplastic distribution along Sri Lankan coastlines Development of science-based recommendations for global plastic treaties Material flow analysis of plastic waste in ASEAN countries Amila’s expertise spans life cycle assessment of water technologies, disaster response systems, and environmental data accessibility. They collaborate on regional and international projects addressing plastic pollution and sustainable resource management.
Markus Strohmaier is Professor and Chair of Data Science in the Economic and Social Sciences at the University of Mannheim, with affiliations as Scientific Coordinator at GESIS – Leibniz Institute for the Social Sciences and External Faculty Member at the Complexity Science Hub Vienna. His interdisciplinary work bridges computer science, economics, and the social sciences. University of Mannheim – Chair for Data Science in the Economic and Social Sciences GESIS – Scientific Coordinator for Digital Behavioral Data Complexity Science Hub Vienna – External Faculty Former Professor at RWTH Aachen University and University of Koblenz-Landau Previous Post-Doc and Visiting Roles at Stanford University, Xerox PARC, University of Toronto, and Graz University of Technology His research focuses on computational social science , algorithmic fairness , network science , and the modeling of human behavior using machine learning and large-scale data. He develops methods to analyze textual, relational, and emerging data types to understand socioeconomic systems and digital societies. The recent articles reflect a strong trend in studying inequality in algorithmic systems , governance in decentralized organizations (DAOs) , and psychological profiling of AI . His work spans high-impact journals like Nature and Scientific Reports , emphasizing fairness, transparency, and societal impact of data-driven technologies. Notable scientific contributions include: Editor-in-Chief of EPJ Data Science (2018–2022) Founding co-chair of the Computational Social Science section of the German Informatics Society He advises students and leads research projects on algorithmic fairness, digital governance, and behavioral modeling. His team engages in both fundamental methodological development and applied studies in real-world digital platforms. He has been involved in significant grants and collaborative initiatives around digital behavioral data and computational social science infrastructure. His lab and projects include the Algorithmic Fairness initiative and the interactive visualization tool Planets of Disparity , which explores how algorithms behave on different network structures. These efforts aim to enhance public understanding and technical scrutiny of algorithmic systems.
Lisbet Tarp is an Associate Professor in Art History at the School of Communication and Culture, Aarhus University. Her academic work bridges traditional art historical scholarship with digital methodologies and interdisciplinary research, particularly in the fields of painting, materiality, and conservation. She is actively engaged in several high-impact research projects exploring the intersections of art, science, and technology. Research Interests: Digital Art History and computational analysis of paintings Materiality and technique in historical and contemporary painting Conservation science and hidden layers in artworks Mathematics and geometry in visual art Early modern court culture and ceremonial representation Interdisciplinary practices between art and anatomy Her recent publications reflect a strong trend toward integrating digital tools into art historical inquiry, with a focus on uncovering the material and technical dimensions of paintings. Projects such as Digital Art History: Rediscovering the Painting and ANAT: Anatomical Theater demonstrate her innovative approach to visual culture through scientific and digital lenses. Her work often involves collaborative, peer-reviewed publications and digital dissemination. Scientific Projects & Activities: ANAT: Anatomical Theater (2023–2029) – Investigating dissection as aesthetic practice in early modern and contemporary contexts Digital Art History: Rediscovering the Painting (2019–2022) – Using digital methods to analyze painting techniques and material composition MoCMa: Mobility Creates Masters (2017–2019) – Studying transnational influences in European art LUMEN Center (2015–2025) – Researching Lutheran theology and its impact on confessional societies and visual culture Lisbet Tarp has contributed extensively to academic discourse through peer-reviewed journals, anthologies, and digital publications. While no formal students or awards are listed, her leadership in major research initiatives underscores her scholarly influence. She employs digital platforms for both research and pedagogy, including student-organized seminars and open-access digital versions of her work.
Ginés Carreto Picón serves as a Research Fellow within the Department of Electrical and Computer Engineering at Aarhus University, Denmark, specializing in the Signal Processing and Machine Learning research group. His work focuses on developing computationally efficient AI solutions for resource-constrained environments. His research expertise spans machine learning, signal processing, and artificial intelligence of things (AIoT), with emphasis on creating high-performance sequence processing models through continual learning frameworks, dimensionality reduction, and low-rank approximation techniques. These approaches significantly reduce energy consumption while maintaining model accuracy for edge deployment. His publication on visual fingerprinting for sequential data demonstrates his focus on interpretable pattern recognition methods. Current work centers on the 2024-2027 PhD project developing lightweight AI architectures specifically optimized for IoT devices, aiming to expand feasible AI applications in constrained computational environments. He actively contributes to the Signal Processing and Machine Learning laboratory's research ecosystem, advancing methodologies for practical implementation of efficient AI systems in real-world edge computing scenarios.
