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Jonathan Voersaa Wenshøj is an academic researcher at the Department of Computer Science, University of Copenhagen. He contributes to the Machine Learning section's activities spanning theoretical foundations and applications in diverse domains like information retrieval, medical data analysis, remote sensing, sustainability, and biological modeling. The section participates in the SCIENCE AI Centre and collaborates with initiatives like TreeSense for global tree resource analysis. His research intersects machine learning with quantum computing, medical informatics, and sustainability. Recent publications highlight applications in environmental monitoring, healthcare diagnostics, and energy-efficient AI systems. The department provides advanced compute resources including a powerful cluster for intensive machine learning tasks. This researcher's work appears in diverse machine learning domains, with recent publications addressing quantum-inspired architectures, explainable AI in medical imaging, and sustainable computing practices. The section actively hosts events including seminars, conferences, and PhD defences related to machine learning advancements.

• Machine Learning
• Artificial Intelligence
• Data Analysis
• +7 more

Sophia Natasha Wilson is a Research Fellow in the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in machine learning applications across interdisciplinary domains. She is affiliated with the SCIENCE AI Centre and holds a cross-departmental position at the Niels Bohr Institute . Her research bridges theoretical machine learning with practical implementations in healthcare, quantum computing, and environmental sustainability. University of Copenhagen Department of Computer Science (DIKU) Niels Bohr Institute SCIENCE AI Centre Her research focuses include: Quantum-enhanced machine learning algorithms Explainable AI for healthcare applications Environmental sustainability in computing Emotion-aware language models Quantum computing hardware optimization Public health risk modeling Her recent publications demonstrate cross-disciplinary work in quantum machine learning (hybrid optical processors, qubit stabilization), health informatics (hypothyroidism analysis, nursing values evaluation), and ethical AI (sustainable AI, fairness in recommender systems). Technical work also appears in non-Euclidean generative models and real-time adaptive systems . Current projects include quantum dot array simulation (QDarts platform) and federated learning for personalized medicine . She contributes to the TreeSense center for remote sensing of global tree resources and works on climate-aware AI frameworks.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +5 more

Stefan Oehmcke is an Assistant Professor at the Machine Learning Section of the Department of Computer Science , University of Copenhagen. His research focuses on applying machine learning techniques to environmental and geospatial analysis, particularly in forest ecology, tree monitoring, and climate impact studies. Research Trends: His recent publications emphasize deep learning for LiDAR data processing, multi-modal geospatial representation, and sustainable AI practices. Key Collaborations: Frequently collaborates with researchers in environmental science, remote sensing, and climate change (e.g., Martin Brandt, Christian Igel). Applications: Develops tools for forest biomass estimation, tree mortality mapping, and urban safety analysis using satellite imagery. While no specific educational background or scientific awards are mentioned in the provided texts, Oehmcke's work demonstrates technical innovation in AI explainability and environmental monitoring, with significant contributions to journals like Remote Sensing of Environment and Nature Communications .

• Machine Learning
• Remote Sensing
• Geospatial Representation Learning
• +3 more

Ilias Chalkidis is an Assistant Professor specializing in Natural Language Processing at the Department of Computer Science, University of Copenhagen. He is actively affiliated with the Natural Language Processing research section, contributing to both theoretical and applied advancements in the field. His research spans multiple high-impact domains with particular emphasis on: Legal natural language processing and multilingual legal reasoning Large language model applications in political and social contexts Fairness-explainability trade-offs in AI systems Innovative representation learning techniques for textual data Analysis of his recent publications reveals a strong focus on bridging legal informatics with cutting-edge NLP methodologies. His work on multilingual legal corpora (including the 689GB MultiLegalPile dataset) and legal decision influence prediction demonstrates practical applications for judicial systems. Simultaneously, his investigations into LLMs as voting assistants and European political spectrum analysis showcase innovative intersections between computational social science and language technology. His technical contributions to contrastive learning and hyperbolic embeddings provide foundational advances for document representation. Chalkidis actively participates in the research community through workshop organization (Natural Legal Language Processing Workshop 2023-2024) and conference presentations. His research has been published in top-tier venues including ACL, EMNLP, and ECAI, with significant citations reflecting community impact. While specific advising relationships aren't documented in the provided materials, his collaborative work patterns suggest active mentorship within the NLP research ecosystem.

• Natural Language Processing
• Legal Informatics
• Multilingual NLP
• +3 more

Allan Hanbury is a Full Professor for Data Intelligence at the Faculty of Informatics, TU Wien, and a faculty member at the Complexity Science Hub Vienna. He leads the Data Science Research Unit and serves as the Faculty Representative for financial affairs and internationalization. He holds a PhD in Applied Mathematics from Mines ParisTech and a Habilitation in Practical Informatics from TU Wien. PhD in Applied Mathematics, Mines ParisTech, 2002 Habilitation in Practical Informatics, TU Wien, 2008 Bachelor’s and Master’s in Physics and Applied Mathematics, University of Cape Town His research focuses on information retrieval, data mining, natural language processing, and information extraction, with applications in healthcare, legal, and patent domains. He has coordinated major EU projects including Khresmoi, VISCERAL, KConnect, and DoSSIER, the latter training 15 PhD students. He is co-founder of contextflow, a spin-off commercializing radiology search technology. His recent publications (2024–2022) highlight a strong trend in systematic literature review automation, neural re-ranking, large language models, and domain-specific information extraction. Key themes include improving citation screening, patient-trial matching, evaluation metrics, and dataset creation for offensive language and legal text. His work combines technical innovation with real-world impact in medical, legal, and scientific communication contexts. Allan Hanbury has received no explicitly mentioned scientific awards in the provided text. He actively supervises numerous PhD and master’s students and leads large research projects such as DoSSIER, Transparent Automated Content Moderation, and PLFDoc, funded by FWF, WWTF, and EU. His group develops tools for evidence synthesis, clinical data extraction, and legal document analysis. He also contributes to AI and data strategy in Austria and Europe. He leads the Data Science Research Unit at TU Wien and is involved in multiple interdisciplinary projects including BRISE (building regulation analysis), CDL-RecSys, and TACo, focusing on legal and scientific document processing. His work bridges academia and industry through spin-offs like contextflow and collaborations with Deutsche Telekom, Siemens, and FMA.

• Information Retrieval
• Data Mining
• Natural Language Processing
• +13 more

Thomas Niederkrotenthaler is an Associate Professor and Head of the Unit for Suicide Research & Mental Health Promotion at the Medical University of Vienna, Austria. He is also an Associate Faculty member at the Complexity Science Hub (CSH) since September 2019. His research centers on suicide prevention, mental health promotion, and the epidemiological impact of media on suicidal behavior. He is best known for coining the Papageno effect , which describes how responsible media reporting can have protective effects against suicide. His work integrates public health, data science, and complex systems approaches. The recent publications reflect a strong trend in using data science and machine learning to analyze media content, evaluate public health campaigns, and understand mental health trajectories during crises such as the COVID-19 pandemic. His research spans refugee mental health, antidepressant use, and the societal impact of celebrity suicides. He is a board member of the International Association for Suicide Prevention and chairman of the Wiener Werkstaette for Suicide Research. He has received multiple research grants, including funding for COVID-19-related projects from WWTF. Board Member, International Association for Suicide Prevention Chairman, Wiener Werkstaette for Suicide Research Associate Faculty, Complexity Science Hub (CSH) Thomas Niederkrotenthaler has advised numerous research projects and secured competitive grants. His team leverages large datasets, including administrative and social media data, to inform suicide prevention strategies. He leads a multidisciplinary research unit focused on translating epidemiological findings into public health action. His lab collaborates closely with the Complexity Science Hub and engages in international research networks focused on mental health, media effects, and pandemic response. The team emphasizes open science and data sharing, as demonstrated by contributions to large-scale datasets like the CSH COVID-19 Control Strategies List.

• Suicide Prevention
• Mental Health Promotion
• Epidemiology
• +5 more

Rob van der Goot is an Associate Professor at the IT University of Copenhagen , specializing in robustness in natural language processing (NLP). His work focuses on non-standard language varieties, low-resource languages, and scenarios with limited training data, particularly in syntactic tasks. He is involved in projects like MaChAmp and MoNoise , which address multi-task learning and lexical normalization. Supervises PhD student Arzu Burcu Güven and mentors postdoc Elisa Bassignana Member of the HPC committee at ITU and the AI Pioneer Center Active in research collaborations including the COST Action Multi3Generation His research has earned accolades, including a Best Paper Award at WNUT 2022, an Outstanding Paper Award at EACL2021, and the Most Reviews Award at NAACL 2019. He frequently presents at international venues, such as LMU, DeiC Konference, and the University of Helsinki. His recent publications explore cross-domain dialogue act classification, code-switched lexical normalization, and multi-task learning frameworks. His work has been featured in media outlets like Omrop Fryslan and Linear Digressions. 2022 : Best Paper Award for Increasing Robustness for Cross-domain Dialogue Act Classification on Social Media Data (WNUT) 2021 : Outstanding Paper Award for MaChAmp (EACL) 2019 : Most Reviews Award (NAACL) As part of the HPC committee and DeIC Science Forum, he contributes to computational infrastructure and data management initiatives. His supervision history includes advising Anders Giovanni Møller (shared first place in WNUT 2021 shared task) and collaborations with researchers like Barbara Plank and Malvina Nissim.

• Natural Language Processing
• Robustness in NLP
• Low-Resource Languages
• +7 more

Giovanni Colavizza holds a dual academic appointment as Professor in the Department of Communication at the University of Copenhagen and Associate Professor at the University of Bologna (since November 2023). His research bridges information science, digital humanities, and open science practices. His primary research areas include: Quantitative analysis of open research practices (data/code/preprint sharing) and citation impact Wikipedia's role in scientific knowledge dissemination and news source reliability assessment Digital tools for humanities including knowledge graphs and NLP applications for historical archives Recent publications (2024-2025) reveal strong interdisciplinary trends, combining scientometrics with natural language processing to analyze scientific communication patterns and colonial archival materials. His work appears in leading venues including PLoS ONE, Scientometrics, and IEEE Journal of Biomedical and Health Informatics, demonstrating methodological innovation across information science and humanities domains.

• Open Science
• Scientometrics
• Digital Humanities
• +4 more

Freja Stær Hincheli serves as a Lecturer at the Department of Computer Science , University of Copenhagen. Her work intersects multiple domains within machine learning, with a particular emphasis on quantum-inspired algorithms, medical imaging, and sustainable AI development. Keywords : Machine Learning, Quantum Computing, Medical Imaging, Natural Language Processing, Computational Biology Key Collaborations : SCIENCE AI Centre Her research spans quantum-enhanced neural networks, explainable AI for medical diagnostics, and energy-aware model design. Recent publications highlight applications in cross-cultural recipe adaptation, emotion-aware dialogue systems, and climate-conscious AI strategies. The Machine Learning Section at DIKU focuses on theoretical foundations and applications including medical image analysis , biological data modeling , and quantum computing , aligning with her contributions.

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

Professor Sven Tarp is affiliated with Aarhus University as a faculty member at the School of Communication and Culture , specializing in Spanish Business Communication . His research focuses on lexicography, artificial intelligence, and digital tools for language learning. Education: Not explicitly detailed in the scraped text. Research Interests: Sven Tarp explores the intersection of lexicography and generative AI , with a focus on creating context-aware dictionaries and writing assistants for foreign-language learners. His work emphasizes human-AI collaboration and user-centered design in digital lexicography. Scientific Trends: His recent publications highlight the integration of AI technologies into lexicographic practices , particularly for Spanish and Chinese learners of English . Themes include grammar development , digital dictionaries , and pedagogical tools that combine machine learning with human expertise. Projects: He contributes to initiatives like the LACUA (Latin American Center Aarhus University) and leads research on disruptive technologies in lexicography , spanning 2018–2023.

• Lexicography
• Artificial Intelligence
• Spanish Language
• +3 more

Chenjuan Guo is an Associate Professor at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. She is affiliated with the Data Engineering, Science and Systems group and the AI for the People initiative, and is part of the Daisy - Center for Data-intensive Systems. Her research focuses on machine learning, data engineering, spatio-temporal data analysis, and time series forecasting. Key projects include the Villum Foundation-funded 'Explainable AI for Complex Microbial Community Interactions and Predictions' (2021-2024) and the Astra project on time series analytics in spatial networks (2018-2021). Her research interests span representation learning, autoencoders, path representation, outlier detection, trajectory data analysis, and time series modeling. She has supervised 3 PhD students and contributed to over 60 publications, with a recent emphasis on transformer-based forecasting, neural architecture search, and continuous learning frameworks for spatio-temporal data. Her work bridges theoretical advancements with practical applications in environmental science, cloud computing, and urban mobility systems. Key achievements include developing frameworks like AutoCTS++ for automated time series forecasting and LightGTS for lightweight models. She actively collaborates internationally, contributing to conferences like ECML PKDD and CVPR. Her research is supported by grants from the Villum Foundation and other institutions.

• Machine Learning
• Data Engineering
• Spatio-temporal Data Analysis
• +4 more

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.

• Human-Computer Interaction
• User Experience
• Human-Centered Artificial Intelligence
• +7 more

Katja Hose is a Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. Her research focuses on Data, Knowledge and Web Engineering with specializations in AI for the People and Artificial Intelligence and Machine Learning. She maintains an active research profile with numerous publications and projects. Department of Computer Science Technical Faculty of IT and Design Aalborg University Research areas: Query Processing, Semantic Web, Linked Data, Knowledge Graphs Professor Hose's research interests center on knowledge representation, semantic web technologies, and AI applications. Her work spans from theoretical database systems to practical applications in healthcare, environmental assessment, and microbial data analysis. She has made significant contributions to knowledge graphs, large language models, and semantic search technologies, with particular emphasis on addressing hallucinations in AI systems and improving table search in semantic data lakes. Her recent publications demonstrate a strong trend toward integrating knowledge graphs with large language models, developing evaluation frameworks for AI hallucinations, and applying data science to diverse domains including healthcare and environmental sustainability. Her research bridges theoretical computer science with practical applications that address real-world challenges. NLP4KGC Best Paper Award (2023) ESWC 2023 Best Demo Award (2023) 2020 AMiner AI 2000 Most Influential Scholars AIME 2020 Best Paper Nomination (2020) ESWC 2019 Best Demo Award Nomination (2019) Professor Hose leads multiple significant research projects including ARISTOTLE (AI for clinical risk assessment), DarkScience (microbial data analysis), and the Poul Due Jensen Professorate in Big Data and AI. She has supervised numerous PhD students and collaborates extensively across disciplines, particularly in healthcare applications of AI and environmental assessment technologies. Her research has attracted substantial funding from sources like Villum Fonden and Danish E-infrastructure Cooperation. She is actively involved in several interdisciplinary research teams, including collaborations with microbiologists on microbial dark matter projects and with environmental scientists on digital environmental assessment systems. Her work on the ARISTOTLE project demonstrates strong connections between AI research and clinical applications, while her DarkScience project bridges computer science with microbiology.

• Query Processing
• Semantic Web
• Linked Data
• +7 more

Daniel Hardt serves as Associate Professor in the Department of Management, Society and Communication at Copenhagen Business School. His interdisciplinary research bridges computational linguistics, artificial intelligence, and social analysis, with particular focus on natural language processing applications and theoretical linguistic phenomena. His primary research domains include Computational Linguistics (specializing in ellipsis resolution and sluicing phenomena), Natural Language Processing (developing methods for psychographic classification and sentiment analysis), and Artificial Intelligence (examining large language model capabilities and limitations). Recent work analyzes travel behavior during crises, gender effects in evaluations, and GDPR policy comprehension through NLP techniques. His publications span top venues including Linguistic Inquiry , Tourism Management , and ACL proceedings. Hardt actively engages with practical business applications through 27 media contributions discussing AI implementation, ChatGPT transparency, and data-driven leadership strategies. His academic service includes organizing events like the 2019 "Fake News" conference at CBS and presenting at international venues including JSAI 2024. With 28 supervised academic works documented, he maintains substantial mentoring activity while contributing to public discourse on digital transformation challenges.

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +4 more

Haeun Yu is a PhD Fellow at the Department of Computer Science, University of Copenhagen, affiliated with the Natural Language Processing and Pioneer AI sections. Her research focuses on language models, retrieval-augmented generation, and model interpretability within artificial intelligence and natural language processing. Key Research Themes: Context utilization in generative AI, knowledge conflicts in QA systems, parametric knowledge attribution. Collaborations: Engages in interdisciplinary projects with researchers in NLP, machine learning, and computer vision. Publications: Recent work addresses dynamic QA systems and parametric knowledge attribution in ACL and EMNLP conferences.

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