Anders Søgaard is a Professor at the University of Copenhagen , affiliated with both the Department of Computer Science and the Department of Communication. His research bridges Natural Language Processing and Machine Learning with a focus on AI ethics , explainability , and human-AI interaction . Primary Affiliation: Department of Computer Science, University of Copenhagen Secondary Affiliation: Department of Communication, University of Copenhagen Email: soegaard@di.ku.dk, soegaard@hum.ku.dk Research Interests His work spans Natural Language Processing , Machine Learning , and AI ethics , with recent studies addressing: Trustworthiness in AI systems Explainable AI (XAI) frameworks Multilingual model fairness and alignment Human-AI collaboration in reasoning tasks Ethical implications of social robots Mental health analytics using ML Recent Publications His 2025 output highlights trends in: AI ethics (e.g., fairness metrics, trustworthy systems) Multilingual model analysis (knowledge retention, cross-lingual transfer) Human-centric AI (gaze data, cultural considerations) Applications in healthcare and social good
Manex Aguirrezabal Zabaleta is an Associate Professor in the Department of Nordic Studies and Linguistics at the University of Copenhagen. He previously held positions as a Postdoc (2017-2019) and Assistant Professor at the same institution. His educational background includes: PhD in Natural Language Processing from the University of the Basque Country (UPV/EHU), conducted at the IXA NLP group. Master's degree in Natural Language Processing from UPV/EHU. Bachelor's degree in Computer Science (5 years) from UPV/EHU. Dr. Aguirrezabal's research focuses on the computational analysis of poetry , particularly stress patterns in English. He explores whether computers can effectively analyze poetic structures, a field with roots in the 1980s but revitalized by modern techniques. Additionally, he investigates language generation , computational morphology and phonology , and finite-state methods . His work bridges traditional linguistic inquiry with cutting-edge natural language processing. Recent publications (2023-2024) demonstrate a diverse engagement with computational linguistics, including poetry generation, multimodal corpus development, clickbait analysis, and fact-checking. His research often employs zero-shot learning and language models, reflecting current trends in AI-driven linguistic analysis. He has contributed to international collaborations such as ParlaMint (multilingual parliamentary corpora) and the GEHM Zoom corpus. While specific grant details are not provided, his active publication record indicates ongoing research support. Dr. Aguirrezabal maintains a strong connection to his Basque heritage, having pursued his early education in the Basque language.
Desmond Elliott is an Associate Professor in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen (UCPH). His research focuses on multimodal and multilingual models with specific emphasis on vision-language integration and tokenization-free NLP approaches. He teaches Bachelor and Master's level courses including Advanced Topics in Natural Language Processing (since 2019), Grundlæggende Data Science (since 2023), and previously Data Science (2021-2023). His research interests center on building and understanding multimodal and multilingual models , particularly exploring vision and language interactions through billion-parameter systems. Current work investigates cultural representation disparities in vision-language models, parameter-efficient captioning, and multimodal distributional semantics across diverse domains including food culture and medical imaging. His methodology emphasizes real-world applicability in non-English contexts and ethical considerations in multimodal systems. Elliott's recent publications (2025) demonstrate leadership in multimodal NLP, with significant contributions to vision-language pretraining, multilingual evaluation frameworks, and clinical NLP applications. His work spans theoretical advancements in model architectures and practical implementations addressing challenges in low-resource languages and domain adaptation. Best Long Paper Award at EMNLP 2021 Best Poster Award at COLING 2019 As an active educator, Elliott contributes to courses on Fair and Transparent Machine Learning and previously taught Information Retrieval. His research collaborations span international institutions with particular focus on European and non-English language contexts, reflecting UCPH's recognition as Europe's #1 institution for HCI research over the past decade.
Daniel Hershcovich is a Tenure Track Assistant Professor at the Department of Computer Science (Faculty of Science, University of Copenhagen) specializing in Natural Language Processing and Machine Learning . His research focuses on cross-cultural adaptation of language models, integrating human values into AI, and analyzing food-related cultural narratives for sustainable diets. Education: Ph.D. in Computational Neuroscience from Hebrew University of Jerusalem B.Sc. in Mathematics and Computer Science from Open University of Israel Recent publications highlight his work on multimodal models (haptic captioning, visual assistants for the blind), historical text analysis (Danish/Norwegian literature, euphemism detection), and cross-cultural NLP (recipe adaptation, cultural value alignment, climate awareness). His projects frequently combine AI ethics with domain-specific applications like food studies, historical linguistics, and accessibility research. Key collaborative networks include institutions in Denmark, Israel, and international partnerships through conferences like ACL, EMNLP, and workshops on cross-cultural NLP. The NLP section at DIKU serves as his primary affiliation for these efforts.
Arnav Arora is a PhD Fellow at the Department of Computer Science , University of Copenhagen (DIKU), specializing in Natural Language Processing . His work focuses on ethical AI, bias detection, and societal impacts of language models. Email: aar@di.ku.dk Location: Universitetsparken 1, 2100 København Ø Arnav's research explores fine-grained value alignment in language models, harmful content detection , and cross-cultural differences in AI responses. His work bridges technical NLP advancements with social responsibility, including dual use ethical frameworks and community value analysis . Key publication trends include: 2025: Bias mitigation through BiasGym framework 2024: Factcheck-Bench benchmark development 2023: Thorny Roses dual use analysis 2022: Cross-cultural value probing methods 2020: Multi-hop fact checking systems Arnav contributes to the Software, Data, People & Society (SDPS) section, collaborating with interdisciplinary teams on projects involving language model evaluation and societal impact mitigation . His work often addresses real-world AI deployment challenges through academic-industry partnerships.
Susanne Jacobsen Perez is a Part-Time Lecturer at the Department of People and Technology, Roskilde University, Denmark. Her research focuses on cultural encounters , multiculturalism , and intercultural education , alongside topics like multilingualism , language and content didactics , and equal rights . Key projects include 'For sent? Modtagelsesundervisningens betydning for sent ankomne unge flygtninge og indvandreres indslusning i det danske uddannelsessystem' (2013–2017), which examined reception education for late-arriving refugee and immigrant youth. Her work has been featured in media coverage, such as the 2015 article 'Særskilt sproglæring er vigtigt' (Language learning is important). Research Outputs : 8 publications (3 book chapters, 3 journal articles, 1 literature review, 1 internet publication), with themes spanning multilingual education, postcolonial studies, and cultural diversity in educational systems. Email : susannep@ruc.dk
Isabelle Augenstein is a Professor at the University of Copenhagen, Department of Computer Science (DIKU), where she heads the Copenhagen Natural Language Understanding (CopeNLU) research group and the Natural Language Processing section. She is also a co-lead of the Danish Pioneer Centre for Artificial Intelligence, Denmark's largest research center initiated by the Danish Ministry of Higher Education and Science. In October 2022, she became Denmark's youngest ever female full professor. Dr. Augenstein earned her undergraduate degree in Computational Linguistics and Psychology from Heidelberg University, followed by a Master's in Computational Linguistics. She completed her PhD in Computer Science at the University of Sheffield under the supervision of Dr. Diana Maynard and Prof. Fabio Ciravegna. In 2021, she earned a Habilitation at the University of Copenhagen in Explainable Fact-checking. Professor Augenstein's primary research focuses on fair and accountable Natural Language Processing, with particular emphasis on explainability, factuality, and bias detection. Her work spans multiple subfields including automated fact-checking, stance detection, gender bias analysis, and cultural bias in language models. She has pioneered research in explainable fact-checking, developing methods that not only predict claim veracity but also provide meaningful explanations of the decision-making process. Her research group has produced numerous influential papers on measuring model fragility, quantifying gender biases, and developing robust fact-checking systems that account for distribution shifts. Her significant contributions have been recognized with several prestigious awards: ERC Starting Grant on 'Explainable and Robust Automatic Fact Checking' DFF Sapere Aude Research Leader fellowship on 'Learning to Explain Attitudes on Social Media' Karen Spärck Jones Award from the British Computing Society and Bloomberg Hartmann Diploma Prize from the Hartmann Foundation Member of the Royal Danish Academy of Sciences and Letters since 2024 Professor Augenstein has secured significant research funding including her ERC Starting Grant supporting five years of blue-sky research. She actively mentors PhD students and postdoctoral researchers through her 'ExplainYourself' project. She served as President of SIGDAT (which organizes the EMNLP conference series), having previously held leadership roles as Vice President and Vice President-Elect. She is a co-founder of Widening NLP (WiNLP), an initiative to increase diversity in the NLP community, and maintains the BIG Directory of underrepresented groups in NLP. She leads the Copenhagen Natural Language Understanding (CopeNLU) research group, which relocated to the historic Østervold Observatory in Copenhagen's Botanical Gardens in 2023. The group focuses on developing methods for explainable and robust natural language understanding, with applications in fact-checking, bias detection, and social media analysis. Professor Augenstein also co-leads the Speech and Language collaboratory at the Pioneer Centre for Artificial Intelligence, where her team investigates how language models can better serve diverse populations while maintaining accountability and transparency.
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.
Johannes Bjerva is a Full Professor at Aalborg University's Department of Computer Science (Campus Copenhagen), leading the Copenhagen branch and conducting interdisciplinary NLP research integrating linguistic typology. His work focuses on low-resource languages, language model security, and societal AI impact. PhD (University of Groningen, 2017): Thesis on multitask/multilingual lexical modeling M.A. & B.A. in Computational Linguistics (Stockholm University) Research interests span linguistically-informed NLP , language model security , and low-resource language technology . Current projects include the DFF Sapere Aude grant (2025) for language model detection security and the LM2-SEC project (2025–2030). His 2024 ACL paper on embedding inversion security and 2024 EMNLP paper on typological diversity exemplify recent work. Scientific awards include: 2021: Teacher of the Year (AAU Computer Science) 2019: Google Cloud research credits 2022: Carlsberg Semper Ardens (5M DKK) 2024: Novo Nordisk Data Science grant (~10M DKK) Supervision includes 8 PhD students across projects like CreoleVal and HiFi-KPI . He serves on the Industrial Researcher Committee at Innovation Fund Denmark and is a member of Det Unge Akademi (2023–2028).
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.
Tina Paulsen Christensen is an Associate Professor at Aarhus University's School of Communication and Culture. Her research focuses on AI-driven technologies in education and society, particularly exploring interactions between humans, machines, and languages. She specializes in areas such as generative AI, machine translation literacy, and digital text generation. Her educational background includes expertise in German Business Communication and advanced studies in language technology applications. Key projects include AI-literacy i sproguddannelserne (2025-2027) addressing language education in the AI era, and Generative AI-værktøjer i fremmedsproglig tekstproduktion (2024-2025) focusing on AI tools in multilingual text creation. Research interests span translation technology, human-computer interaction ethics, and educational strategies for integrating AI tools. She has contributed to discussions on machine translation's societal impact and translator work practices through articles like 'What motor vehicles and translation machines have in common' and 'Vær smartere end dine elever.' Notable collaborations include the HAL research project (2018-2023) exploring translation technology's human, application, and language dimensions. She actively engages in policy discussions, including the legislative proposal for interpreter education in Denmark.
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.
Lisbeth Verstraete-Hansen is an Associate Professor at the Department of English, Germanic and Romance Studies, University of Copenhagen (UCPH), where she also serves as Head of Department since 2021. Previously, she held roles at Copenhagen Business School (2009-14) and UCPH (2002-06). Her research focuses on French and Francophone literature , literary history , and transnational cultural transfer , particularly in the context of language policy and Danish-French intercultural relations . Ph.D. in French (Francophone Belgian) Literature (University of Copenhagen, 2002) DEA in Comparative Literature (Université Lille III, France, 1995) MA in French Studies (University of Copenhagen, 1992) Her recent work examines the international circulation of Francophone literatures and translation studies , with a focus on paratextual representation and language ideologies in higher education . She has published extensively in journals like Meta: Translators' Journal and French Studies in Southern Africa . Notable awards include Einar Hansens Legat for Fremstående Humanistisk Forskning and Chevalier dans l’Ordre des Palmes Académiques . She has led initiatives on parallel language use and participated in international conferences on linguistic policy and postcolonial studies .
Henrik Palmer Olsen is a Professor of Jurisprudence at the Faculty of Law, University of Copenhagen (UCPH), where he has been a central figure since earning his Cand.jur. in 1993. He is a co-founder and member of the Centre of Excellence for International Courts and Governance (iCourts), a leading research center in global legal studies. His academic leadership includes serving as Associate Dean for Research and Head of the PhD School at UCPH. Education: Dr.jur. (2005), Faculty of Law, University of Copenhagen PhD (1997), Faculty of Law, University of Copenhagen MA in Socio-Legal Studies (1994), University of Sheffield Cand.jur. (1993), Faculty of Law, University of Copenhagen Henrik Palmer Olsen's research lies at the intersection of legal philosophy, human rights, and international courts, with a growing emphasis on data science applied to legal analysis. His work explores the theoretical foundations of law, judicial legitimacy, and the methodological evolution of legal scholarship. He has pioneered the integration of quantitative methods—such as citation network analysis and corpus linguistics—into doctrinal legal studies, bridging traditional and computational approaches. His recent publications reflect a consistent trajectory in analyzing international courts, legal diplomacy, and legal methodology. Works like 'Can quantitative methods complement doctrinal legal studies?' and 'Providing Legal Pincite Recommendations using Language Representations' illustrate his innovative approach, combining jurisprudence with AI and data-driven legal research. This positions him at the forefront of the digital transformation in legal science. Scientific Engagement: Speaker, Regulating “AI” in the EU: The “Artificial Intelligence Act” (2021) Speaker, EURECO Distinguished Lecture Series (2010) Lecturer, Bristol Law School Annual Jurisprudence Lecture (2010) Olsen has held major research management roles, including co-founding the Center for Studies in Legal Culture and leading the PhD school at UCPH. He has secured recognition through extensive research output and collaborations across Europe. His work is referenced in academic databases, Mendeley, and multiple Wikipedia pages, indicating broad scholarly impact. He is actively involved in shaping legal education, particularly through his textbook on legal methodology. He is associated with research environments such as the Nordic Asylum Law & Data Lab and maintains strong international networks, especially in EU legal studies and global mobility law. His multilingual abilities (Danish, English, French) support his transnational academic engagement.
Ingemar Johansson Cox serves as a Professor within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His research bridges theoretical machine learning foundations with practical applications across medical data analysis, information retrieval, remote sensing, and sustainability initiatives. His research portfolio emphasizes machine learning applications in high-impact domains, particularly medical data analysis (e.g., early detection of gynecological malignancy using online search activity) and sustainability (e.g., reducing AI's carbon footprint). The Machine Learning section actively contributes to the university's SCIENCE AI Centre, focusing on both algorithmic innovation and real-world problem-solving in biological modeling and environmental monitoring. Recent publication trends reveal expanding work in quantum computing applications for biomolecular modeling, sustainable AI frameworks, and cross-cultural NLP systems. His 2024-2025 output demonstrates strong interdisciplinary collaboration, especially in medical informatics and climate-related AI research. Professor Cox operates within the Department of Computer Science's robust research ecosystem, which includes dedicated compute clusters and specialized initiatives like TreeSense for global tree resource monitoring through remote sensing and deep learning. The department's infrastructure supports large-scale machine learning projects requiring significant computational resources.
