Sebastian Weichwald is an Associate Professor at the Department of Mathematical Sciences , University of Copenhagen. He leads the Copenhagen Causality Lab (CoCaLa) and co-leads the Causality and Explainability (CX) collaboratory at the Pioneer Centre for AI. His academic journey includes a PhD at ETH Zurich (2019) and postdoctoral work at the University of Copenhagen. Research Interests: Causal Modelling, Causal Discovery, Structural Equation Models, Time Series Analysis, Neuroimaging, Biomedical Data Analysis. Awards: Best student paper at CIP 2014, Winner of Causality 4 Climate NeurIPS 2019 competition. Collaborations: Active in interdisciplinary projects with applications in cardiology, neuroscience, and biomedical imaging. Software Tools: Developed open-source libraries including CausalDisco , coroICA , tidybench , and Pymanopt for causal discovery, signal processing, and manifold optimization.
Ana Valeria Gonzalez is a researcher in the Department of Computer Science at the University of Copenhagen's Faculty of Science, specializing in Machine Learning with a focus on human-centered natural language processing. Her work bridges computational linguistics and cognitive science within the department's Machine Learning research section. Her research investigates bias mitigation in multilingual systems, interpretable AI evaluation frameworks, and affect-aware dialogue generation. Key contributions include developing testbeds for gender bias analysis in coreference resolution, novel methodologies for evaluating model interpretability through reverse Turing tests, and retrieval-based approaches for goal-oriented conversational agents. She employs techniques spanning attention mechanisms, BiLSTMs, and domain adaptation to address challenges in low-resource NLP settings. Analysis of her 2019-2021 publications reveals a cohesive research trajectory toward building transparent, equitable NLP systems that account for human cognitive factors. Her work consistently addresses real-world deployment challenges, particularly in dialogue systems requiring emotional intelligence and cross-lingual fairness. Collaborations with Anders Søgaard, Isabelle Augenstein, and other Copenhagen researchers demonstrate strong integration within the university's AI ecosystem. Gonzalez completed her Ph.D. in 2021 with the dissertation "Towards Human-Centered Natural Language Processing". She maintains active research in the Machine Learning group, though specific advising roles and grant details aren't documented in the available materials. Her publication record shows significant impact with multiple Scopus-cited works and substantial reader engagement across platforms.
Henrik Anders Lindén serves as a Guest researcher in the Department of Neuroscience at the University of Copenhagen's Faculty of Health and Medical Sciences, where he is part of the Neuronal Signalling research group. Holding a Ph.D. in Physics (Computational Neuroscience) from the Norwegian University of Life Sciences, Lindén applies computational methods to study neural dynamics in motor and sensory systems, with particular focus on spinal cord networks and auditory processing mechanisms. His educational background includes: Ph.D. in Physics (Computational Neuroscience), Norwegian University of Life Sciences Lindén's research centers on computational neuroscience with emphasis on neural circuit dynamics. He investigates rotational dynamics governing movement in spinal motor networks, firing rate distributions in central pattern generators, and critical periods in auditory processing. His work bridges theoretical modeling and experimental neuroscience to uncover fundamental brain mechanisms across multiple scales, integrating mathematical approaches with biological data to explain emergent properties of neural systems. Analysis of his 2016-2022 publications reveals strong concentration on motor control and sensory neuroscience. Key contributions include demonstrating rotational dynamics in spinal motor networks, elucidating excitation-inhibition balance in motor circuits, and identifying late critical periods in auditory processing. Lindén consistently employs computational modeling to interpret neural data, with increasing focus on how neural dynamics translate to behavioral outputs through interdisciplinary approaches combining physics, neuroscience, and data science. No scientific awards or major grants are documented in available sources. Similarly, student advising activities are not specified in current institutional records. Lindén operates within the Neuronal Signalling group, which investigates cellular and network mechanisms of neuronal communication using electrophysiology, imaging, and computational techniques to understand information processing in neural circuits related to movement and sensation.
David Alejandro Duchene Garzon is an Associate Professor in the Department of Public Health, Section of Epidemiology at the University of Copenhagen's Faculty of Health and Medical Sciences. His work bridges evolutionary biology with public health, focusing on the application of genomic and computational approaches to understand disease dynamics. Education: PhD in Molecular Evolution, Australian National University (2017) Bachelor of Science with Honours in Marine Biology, James Cook University Queensland (2012) Duchene's research traverses multiple fields including phylogenetic modeling, comparative analysis, biogeography, epidemiology (phylodynamics), and machine learning in public health. His past work has focused on explaining molecular evolution in both animal and pathogen genes using diverse statistical approaches. He connects processes at macro scales (macroevolution, macroecology, phylogeography) with those at micro scales (molecular evolution), addressing fundamental questions about how and why novel living beings and pathogens emerge. Currently, he is exploring the intersection of animal movement (neuroscience), infection (epidemiology), and genomics, particularly in identifying infections in livestock and wildlife using computer vision techniques. His recent publications (2024-2025) demonstrate a strong focus on applying genomic approaches to epidemiology and evolutionary biology. These works span diverse topics including infectious disease modeling, molecular clock hypothesis testing, avian genomic evolution, and SARS-CoV-2 genomic surveillance. His research combines computational methods with biological insights to address complex questions in evolution and public health. Scientific Collaborations: Collaboration with Danish COVID-19 Genome Consortium (DCGC) International collaborations across multiple countries in genomic epidemiology Work with multiple genome-sequencing consortia Duchene has established himself as a key researcher bridging evolutionary biology and public health. His work on molecular evolution provides foundational insights that inform his current epidemiological research. He has developed strong collaborative networks across institutions and countries, particularly in genomic surveillance and evolutionary analysis. His current research direction toward computer vision applications for infection identification in wildlife represents an innovative interdisciplinary approach. Duchene maintains an active research program with significant impact, as evidenced by the broad dissemination of his work across news outlets, social media, and academic platforms. His research has been featured in major scientific journals including Nature and has influenced both academic discourse and public health practice.
Jonas Geldmann is an Associate Professor at the Globe Institute, University of Copenhagen, where he leads research in the Section for Biodiversity. His work focuses on understanding and assessing the impact of conservation interventions, particularly how resources, management, governance, and socio-economic context influence the effectiveness of protected areas. Dr. Geldmann's research spans three main areas: global patterns of anthropogenic threats to biodiversity, understanding the impact of protected areas, and improving the effectiveness of protected areas. He utilizes large global datasets from conservation organizations, research institutions, and remote-sensing sources to develop correlative models of conservation success through quasi-experimental methods. His work on global threat patterns involves mapping the distribution of threats to biodiversity using IUCN Red List data for amphibians, birds, and mammals. He investigates how socio-economic, biological, and geological factors explain geographical threat patterns and how they relate to conservation interventions. In protected area research, he examines whether protected areas safeguard biodiversity and local livelihoods using data on management effectiveness and socio-economic context. Dr. Geldmann contributes to global conservation efforts through his involvement with the IUCN World Commission on Protected Areas' Management Effectiveness Specialist Group, working with managers worldwide to improve evidence-based conservation practices. He has published extensively in top journals including Nature, PNAS, and Conservation Biology, with his research receiving significant media attention and social media engagement. Current Research Group: Antonella Gorosabel (Marie Skłodowska-Curie fellow, 2023) Harith Farooq (postdoc, 2022) Helena Neri Alves Pinto (Marie Skłodowska-Curie fellow, 2023) Katherine Pulido Chadid (PhD student, 2022) Dr. Geldmann organizes the MSc course 'International Nature Conservation' and contributes to summer courses 'Herpetology' and 'Area-based Management'. He has successfully supervised numerous MSc and BSc students, with research topics spanning protected area effectiveness, threat mitigation, and conservation planning across multiple continents, including work in Africa, South America, and Asia.
Martin Gustaf Ehrensvärd is an Associate Professor at the University of Copenhagen, Faculty of Theology, specializing in Biblical Exegesis. With a PhD in Semitic Philology and professional certification as a coach (PCC), he brings a unique interdisciplinary perspective to his academic work. Coordinator of the master's programme in Religious Roots of Europe Teaching biblical and rabbinic Hebrew, classical Arabic, classical Syriac, and other Aramaic dialects Principal Investigator on the AI and Ancient Hebrew Texts project with computer scientist Anders Søgaard Co-author of the influential two-volume work "Linguistic Dating of Biblical Texts" (2008) Translator and editor of "Bibelen 2020" (The Bible 2020) Ehrensvärd's research focuses on linguistic dating of biblical texts, challenging traditional views that linguistic evidence can reliably date biblical compositions. He argues that stylistic choices rather than chronological development explain linguistic variations in biblical texts. His work has significantly contributed to debates about early Hebrew language history through analysis of the Dead Sea Scrolls and other ancient texts. His methodology has evolved to incorporate modern computational techniques, as evidenced by his current project applying machine learning to study ancient Hebrew texts. This represents a significant shift in biblical scholarship, bringing cutting-edge technology to longstanding questions. Ehrensvärd has served as editor for several academic journals including Scandinavian Journal of the Old Testament, Pennsylvania State University Press publications, and Hiphil Novum, demonstrating his standing in the academic community. His academic journey began with his dissertation at Aarhus University in 2002 titled "Studier i bibelsk hebraisk syntaks og datering" (Studies in Biblical Hebrew Syntax and Dating), which laid the foundation for his subsequent influential work in the field.
Jichen Zhu is a Full Professor in Human-Computer Interaction and Design at IT University of Copenhagen, where he leads the Procedural eXpression Lab. His research focuses on the intersection of artificial intelligence, human-computer interaction, and design, with particular emphasis on explainable AI, user-centered design, and AI in creative processes. His research interests span multiple domains within human-AI interaction: Explainable Artificial Intelligence (XAI) and transparent AI systems AI in design and creative processes Human-centered AI and user experience Game design with AI elements Player modeling and personalization in games Well-being technologies and health applications of AI Professor Zhu's recent publications demonstrate a strong focus on bridging the gap between AI capabilities and human understanding. His work combines theoretical insights with practical applications in gaming, design, and health contexts, often exploring how AI can enhance human creativity while maintaining transparency and user control. Professor Zhu has received several prestigious awards for his research: Best Paper Award at the 2021 Conference of Foundation of Digital Games (FDG'21) Best Paper Award Finalist in 2022 Honorable Mention Paper Award at the 2021 Conversational User Interface Conference (CUI'21) He serves as Principal Investigator for the ExPAI (Explainable Personalization AI for Health and Well-Being) project funded by the Novo Nordisk Foundation (2021-2027), exploring how explainable AI can personalize health interventions while maintaining transparency. Professor Zhu also contributes significantly to the academic community through editorial roles at IEEE Transactions on Computational Intelligence and AI in Games and as Conference Chair for the 2021 CHI Conference on Human Factors in Computing Systems. As Head of the Procedural eXpression Lab, Professor Zhu leads research that develops innovative approaches to human-AI collaboration in creative domains, focusing on how AI can support human creativity while preserving user agency and understanding.
Peter Dolog is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design. His research focuses on Recommender Systems, Web Engineering, Machine Learning, and Health Informatics. He leads projects such as the MEco Medical Ecosystem and the IWIS Intelligent Web and Information Systems initiative. His work emphasizes explainable AI, knowledge graphs, and social media analytics for public health monitoring. Notable contributions include hypergraph-based recommendation models and sensitive information detection in legal documents. Projects include MEco (medical event surveillance) and IWIS (web systems personalization), funded by EU and national grants. His research spans 148 publications, with recent work on explainable recommendation systems and neural networks for medical data analysis. Collaborations include the University of Vienna and international conferences like ECIR and RecSys. Media coverage highlights his work on using social media for epidemic detection. Key research areas include recommendation systems with attention mechanisms, knowledge graph integration, and ethical AI applications in healthcare. His methodologies bridge machine learning with user-centric design, emphasizing transparency and explainability in AI-driven systems.
Sergio Escalera is Full Professor at Aalborg University's Department of Architecture, Design and Media Technology. He is Distinguished Professor at Universitat de Barcelona, adjunct professor at Universitat Oberta de Catalunya and Dalhousie University, and ELLIS Fellow. He leads the Human Pose Recovery and Behavior Analysis Group and serves as vice-president of ChaLearn. Research focuses on human-centric computer vision including pose estimation, behavior analysis, and ethical AI. Current projects include REPAI (Responsible AI for Value Creation) and trustworthy computer vision systems. Scientific Awards: Amazon Research Award 2020 Fellow of European Laboratory for Learning and Intelligent Systems (2019) Recent publications explore machine unlearning, power system stability, object tracking, and 3D cloth simulation, reflecting interdisciplinary applications of deep learning.
Richard Röttger is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), part of the Faculty of Science. His research focuses on developing efficient algorithms for analyzing biological networks and large-scale biomedical datasets, particularly emphasizing unsupervised machine learning and integration of heterogeneous datasets. He aims to understand biological processes such as cell responses and environmental adaptation through computational methods. His work spans federated learning applications in healthcare, privacy-preserving analytics, and algorithm development for genomic and proteomic data analysis. Education: Not explicitly detailed in the provided text. Research Interests: Bioinformatics, machine learning, biomedical data integration, federated learning, and algorithm development for healthcare applications. His projects include optimizing AI-driven risk assessment systems, analyzing genomic instability in aging populations, and developing tools for rare disease diagnostics. Key Contributions: Over 15 recent publications (2023–2025) highlight his work in federated learning frameworks (e.g., FeatureCloud), clinical AI performance evaluation, and algorithmic solutions for spatial transcriptomics and proteomics. He also contributes to open-source tools like DiMmeR for differential methylated region analysis and ClustEval for clustering evaluation. Awards: None explicitly mentioned in the provided texts. Grants/Advising: No specific grants or advising roles listed, though his research aligns with EU projects like screen4Care for accelerating rare disease diagnosis through digital technologies. Labs/Teams: Part of the Bioinformatics at SDU group, focusing on collaborative, privacy-preserving biomedical data science initiatives.
Anna Murphy Høgenhaug is a Postdoctoral Researcher at Aalborg University's Centre for AI Ethics, Law and Policy within the Department of Communication and Psychology (Faculty of Social Sciences and Humanities). Her interdisciplinary work bridges legal frameworks and AI systems, focusing on ethical implications in refugee status determination and administrative decision-making. Her educational background includes: PhD in Refugee Law and Data Science (2020-2024), Center of Excellence, Global Mobility Law, University of Copenhagen MA in Law (2020), University of Copenhagen BA in Law (2017), University of Copenhagen Her research critically examines AI regulation's impact on citizens' rights , algorithmic transparency in asylum procedures , and data-driven refugee law . She employs mixed-methods approaches to analyze Scandinavian asylum systems, highlighting how AI implementations affect vulnerable populations while advocating for explainable AI solutions that uphold legal principles like the right to information and good administration. Recent publications reveal a cohesive trajectory analyzing AI's role in legal contexts: Scandinavian asylum system comparisons, AI Act compliance mechanisms, and XAI development for NLP. Her work consistently connects technical AI capabilities with fundamental rights protection, particularly in EU regulatory frameworks. Dr. Høgenhaug actively contributes to the REPAI project (2023-2027) on Responsible AI for Value Creation, collaborating with multidisciplinary teams across Aalborg University and York University to develop ethical speech recognition technologies. This involves close coordination with computer scientists, legal scholars, and policymakers. She operates within Aalborg University's AI for the People initiative and Centre for AI Ethics, Law and Policy, which provide critical interdisciplinary platforms for examining AI's societal impact through legal, ethical, and technical lenses.
Samuel Rhys Cox is a Postdoctoral Researcher in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. He is an active member of the Human-Centered Computing research group based in Aalborg, Denmark, with a verified ORCID profile (0000-0002-4558-6610). His research spans multiple domains within human-computer interaction and artificial intelligence, with significant publication activity from 2018 through 2025. Dr. Cox's research interests focus on human-computer interaction (HCI), human-centered AI, conversational AI, and social computing. His work particularly examines how artificial intelligence systems can be designed to better understand and respond to human needs, with emphasis on Large Language Models, conversational agents, self-disclosure mechanisms, and semistructured interview techniques. He investigates how motivational messages and volumetric video can enhance user experiences with AI systems. Analysis of his recent publications reveals a strong focus on practical applications of conversational AI across multiple domains. His 2025 work shows particular emphasis on creativity support tools, health self-examination applications, and self-disclosure in chatbot interactions. The research demonstrates interdisciplinary approaches combining computer science with psychology, health informatics, and social sciences to create more effective and human-centered AI systems. Dr. Cox collaborates extensively with researchers including van Berkel, N., Djernæs, H. B., Jacobsen, R. M., and others across multiple projects. His work appears in prestigious venues including CHI Conference on Human Factors in Computing Systems, CUI (Conversational User Interfaces), and Creativity and Cognition conferences. The research has garnered attention across academic platforms with readership on Mendeley and references on social media platforms including X and Bluesky.
Ole Lund is a Professor in the Department of Health Technology at the Technical University of Denmark (DTU), affiliated with the Health Bioinformatics and Personal Medicine group and DTU Microbes Initiative. His research focuses on developing computational methods for biological and medical applications. Professor Lund's research interests span bioinformatics, genomics, and microbiology, with particular expertise in: Metagenomic analysis and microbiome studies Pathogen detection and antimicrobial resistance Machine learning applications in genomics Immunoinformatics and epitope prediction Large-scale genomic data integration His recent publications demonstrate a strong focus on developing novel bioinformatics tools for microbial genomics, pathogen detection, and machine learning applications in genomics. The research shows consistent emphasis on translational bioinformatics approaches that bridge computational methods and biomedical applications. Professor Lund currently supervises multiple PhD students in diverse research areas: Explainable AI and Bioinformatics for Health (K. A. Papagoras) Causal language models for disease landscapes (F. S. Gade) Patient trajectory prediction in oncology (P. Quarles van Ufford) T cell specificity prediction methods (S. N. Deleuran) Immune receptor immunoinformatics (J. N. Clifford)
Tina Blegind Jensen is Professor at the Department of Digitalization , Copenhagen Business School (CBS), Denmark. She investigates how digital technologies reshape work, organizations, and society, with a particular focus on human dignity, algorithmic management, and the future of digital labor. Research Interests Digitalization & Organizational Transformation: longitudinal studies of how enterprise systems, platforms, and AI reconfigure work practices and power relations. Algorithmic Management & Labour Organizing: trade-union perspectives on workplace datafication and collective responses to algorithmic control. Human Flourishing & Ethics in Digital Futures: conceptual and empirical work on preserving human dignity, agency, and well-being in increasingly data-driven environments. Healthcare Information Infrastructures: sociotechnical analyses of large-scale e-health implementations, comparing national strategies and clinical outcomes. Sociomateriality & Sensemaking: theoretical development of sociomaterial practice theory and sensemaking lenses to understand technology-in-use. Across 88 scholarly outputs (2018-2025) she has traced a trajectory from early critical studies of electronic patient records to cutting-edge debates on generative AI and the governance of digital labour platforms. A recurring theme is the tension between technological promise and lived experience of workers, patients, and citizens. Scientific Awards & Recognition While no specific awards are listed in the provided text, Professor Jensen’s sustained visibility in premier IS outlets ( Communications of the AIS, Scandinavian Journal of Information Systems, HICSS, Academy of Management ) and frequent keynote invitations testify to her scholarly impact. Doctoral Advising & Research Leadership Supervised at least two doctoral or master’s theses (recorded under “Supervised Work”). Active in doctoral consortia and serves on review committees (e.g., Aalborg University). Leads or co-leads multiple research projects on digital work futures funded by Danish and EU sources (exact grants not detailed in text). Labs, Teams & Collaboration She heads the Digitalization Research Group at CBS, coordinates the Copenhagen Digital Futures research network, and collaborates internationally with scholars in the US, UK, Germany, and Scandinavia. The group’s lab facilities support ethnographic field studies, digital trace data analytics, and participatory design workshops.
Line H. Clemmensen is a Professor in the Department of Mathematical Sciences at the University of Copenhagen's Faculty of Science, with additional roles as Co-founder and Chief Scientific Officer at Interhuman AI. Her work bridges academic research and industry applications in statistical modeling and machine learning. Her research centers on statistical modeling and machine learning with emphases on low-resource domains, explainable AI, and fair modeling—particularly within health and life science contexts. Key focus areas include computational statistics, machine learning fairness, and rigorous statistical evaluation of AI systems, addressing critical challenges in data-scarce and high-stakes environments. Analysis of her 2024-2025 publications reveals strong interdisciplinary trends: bioinformatics applications (gene co-expression networks in single-cell genomics), computational psychiatry (OCD and oxytocin interactions), agricultural science (crop health via remote sensing), and critical AI evaluation (facial recognition fairness, LLM biases in STEM education). Her work consistently integrates statistical rigor with machine learning to solve domain-specific problems while prioritizing ethical considerations like fairness and explainability.
