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.
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.
Line Katrine Harder Clemmensen is a Professor at the Department of Mathematical Sciences, University of Copenhagen. She specializes in statistical modeling, machine learning, and AI, with emphasis on low resource domains, explainability, and fairness in health/life science applications. She co-founded Interhuman AI as Chief Scientific Officer and maintains an active research program across multiple disciplines. Statistical Modeling Machine Learning Explainable AI Fairness in AI Health/Life Science Applications Her recent publications (2024-2025) span computational biology, neuroscience, environmental science, and emotion recognition. Notable collaborations include interdisciplinary work in pediatric OCD analysis, fungal microbiome prediction, and facial emotion recognition systems. She actively explores fairness and scalability in AI models. Dr. Clemmensen holds 60 publications with significant impact across computational biology (40+ citations), neuroscience (68+ readers), and machine learning (20+ Scopus citations). She has been referenced in news outlets, blogged, and discussed across multiple social platforms.
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.
Irena Vodenska is Professor of Finance and Director of Finance Programs at Boston University’s Metropolitan College, Department of Administrative Sciences. She holds a PhD in statistical finance and an MA in economics from Boston University, an MBA from Vanderbilt University, and a BS in computer information systems from the University of Belgrade. She is also a Chartered Financial Analyst (CFA) charter holder. Her research is at the intersection of finance, complexity science, and artificial intelligence, focusing on systemic risk modeling, ESG investments, and financial network dynamics. She has led major interdisciplinary research projects funded by the National Science Foundation, the European Commission, and the U.S. Army Research Office. PhD, Statistical Finance – Boston University MA, Economics – Boston University MBA – Owen Graduate School of Management, Vanderbilt University BS, Computer Information Systems – University of Belgrade Dr. Vodenska’s research interests include network theory in finance, systemic risk propagation, AI-powered ESG analysis, cryptocurrency price forecasting, and financial regulation. She employs big data, machine learning, and natural language processing to analyze financial news, market dynamics, and corporate sustainability. Her work investigates how climate disinformation spreads via social networks and influences public policy and governance. The recent articles highlight a consistent focus on modeling financial and economic systems using network science and AI. Trends include systemic stress testing, sentiment analysis in financial markets, cascading failures, and the interplay between macroeconomic indicators and financial networks. Her work spans econophysics, behavioral finance, public health economics, and ethical AI in fintech. National Science Foundation (NSF) research grant (2023) NSF EAGER Award (2014–2015) European Commission FET Open Grant (2012–2014) U.S. Army Research Office (ARO) Grant (2020–2021) MEXT Post-K Computer Grant, Japan (2016–2019) Alexander Hamilton Fulbright Fellowship (1994) Owen Graduate School Fellowship (1995–1996) Dr. Vodenska teaches core finance courses such as Investment Analysis and Portfolio Management, Derivatives Securities, and Financial Regulation and Ethics. She co-developed the MET AD 678 course with Professor Tamar Frankel from BU Law, emphasizing real-world case studies and ethical decision-making. Her research grants have supported innovative work in systemic risk modeling, AI for ESG, and financial network stability. She is actively involved in mentoring, conference organization, and editorial roles in leading journals. She is a key organizer of the International School and Conference on Network Science (NetSci) and the Big Data in Economics, Science, and Technology (BEST) Conference. Her lab and research team focus on complexity in financial systems, bringing together economists, physicists, computer scientists, and data analysts to study global financial stability and sustainability.
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.
Toine Bogers is a Part-Time Lecturer at Aalborg University , affiliated with the Department of Communication and Psychology within the Faculty of Social Sciences and Humanities . He is a core member of the AI for the People research group. His work focuses on Recommender Systems , Information Retrieval , and Social Media Analysis , with particular emphasis on applications in talent search, leisure information needs, and multistakeholder evaluation. Research Interests: Recommender systems, information seeking behavior, collaborative filtering, human resources algorithms, and ethical evaluation frameworks. His work bridges technical innovation with societal impact, often addressing challenges in job matching, cultural heritage retrieval, and user-centric design. Awards: Best Paper Award at CHIIR 2021 Outstanding PC Member (2020) Best Reviewer (2020, 2018, and multiple years) Grants & Projects: PI of JobMatch (2020–2023): Developing job recommendation systems for unemployed individuals Co-PI of Flipping Information Studies (2017–2019): Integrating video lectures into problem-based learning Organized workshops at RecSys on topics like Recommender Systems in HR and ComplexEnvironments Labs/Teams: He leads research in the AI for the People group, emphasizing human-centered AI applications in recruitment, cultural heritage, and social media analysis.
Barbara Plank is a Professor and Chair for AI and Computational Linguistics at LMU Munich , where she leads the Munich AI and NLP (MaiNLP) Lab within the Center for Information and Language Processing (CIS) . She also serves as a Visiting Full Professor at the IT University of Copenhagen . Her research focuses on natural language processing under real-world constraints, including domain adaptation, continual learning, and multimodal learning. Current Projects: ERC Consolidator DIALECT Project KLIMA-MEMES Project Her recent work investigates annotation bias , self-consistency in language models , and trustworthy model evaluation . She has delivered keynotes at major conferences like ACL, EMNLP, and CLEF, emphasizing human-centric approaches to NLP. Scientific Awards: ACL 2024 Area Chair Award Barbara actively contributes to the academic community as VP-Elect for the Association for Computational Linguistics (ACL) and through teaching roles in MSc/BSc Computational Linguistics programs.
Leon Derczynski is an Associate Professor of Computer Science at the IT University of Copenhagen , with a dual role as Principal Research Scientist/LLMSEC at NVIDIA . He leads the Strømberg NLP research group and coordinates NLP South at ITU, while also being affiliated with the Machine Learning group. Specializes in Natural Language Processing , Machine Learning , and LLM security Focus on Misinformation detection , Clinical text mining , and Danish language technology Research grants include Verif-AI (2.9M DKK), ClinRead (544K DKK), and LITHME (EU COST action, €11K). He has coordinated major projects like COMRADES and PHEME , and contributed to uComp and TrendMiner . Scientific recognition includes the University of Sheffield Exceptional Contribution Award (twice), WEBIST Best Student Paper award , and FP7 funding . His technical work includes the garak.ai LLM vulnerability scanner and generalised-brown clustering library. Actively supervises students and maintains numerous GitHub repositories (86 public projects) related to NLP, machine learning, and computational linguistics. He has delivered keynotes and guest lectures , including at Innopolis University (Russia) and PET (Danish Security and Intelligence Service).
Gaël Le Mens is Professor (Catedràtic) in the Department of Economics and Business at Universitat Pompeu Fabra, with affiliations at Barcelona School of Economics and UPF-BSM. An ICREA Acadèmia Awardee (2023-2027), he researches learning processes in individuals, organizations, and machines, examining how information sampling affects judgment and belief formation. Current research explores: Social media feedback dynamics and opinion polarization Conceptual categorization using LLMs (GPT-4, Llama 3) Reinforcement learning biases Wisdom-of-crowds phenomena His work combines computational modeling with experimental methods. Publications appear in top journals including Psychological Review (4 papers), PNAS (4 papers), and Management Science. Research themes show consistent focus on decision biases arising from sampling limitations, with recent expansion into AI-assisted text analysis and political scaling applications. Honors include: ERC Consolidator Grant for selective information sampling research Multiple publications in PNAS and Psychological Review ICREA Acadèmia recognition Mentored doctoral students include Elizaveta Konovalova, Nikolas Schöll, and Thomas Woiczyk. Leads the ERC-funded project 'Implications of Selective Information Sampling' examining belief polarization mechanisms. Affiliated with the Rebel Governance Network and Strategic Organization Design Unit, with visiting positions at INSEAD, London Business School, and Stanford GSB.
Professor Daniel S. Hain is an Associate Professor at Aalborg University Business School, part of Aalborg University's Faculty of Social Sciences and Humanities. He is affiliated with the IKE Research Group (AI for the People) and the MASSHINE Cluster for Research on Innovation and Development. His research focuses on innovation dynamics, artificial intelligence, network analysis, and technology forecasting. Hain has been actively involved in strategic initiatives such as the Danish Industry Foundation's AI Denmark program and the Generative Ethnographic AI (GE-AI) seed project, demonstrating his commitment to advancing AI applications in societal contexts. His work bridges academic research with practical industry challenges, particularly in areas like patent analytics (e.g., Patentsberta model), renewable energy innovation, and global venture capital patterns in developing economies. Hain has received the OECD IPSDM “Big Data Analytics” Challenge award (2018) for collaborative research on inventive activity visualization. He contributes to policy discussions on AI governance, the privatization of research talent, and regional green technology development. Key projects include the Danish Research Unit for Industrial Dynamics (DRUID) since 1995, exploring innovation systems, and the EIS project on energy innovation competitiveness. Hain’s recent focus on generative AI ethics and SME predictive analytics reflects his dual expertise in technical methodologies and socio-economic impacts.
Jonas Vinther is a Research Fellow at the Department of Computer Science , University of Copenhagen, specializing in Machine Learning and its intersections with quantum computing, medical data analysis, and sustainability. He is also an external PhD student in the Quantum Information Science & Technology program at the Niels Bohr Institute. Email: jonas.vinther@nbi.ku.dk , jonas.vinther@di.ku.dk Location: Universitetsparken 1, 2100 København Ø His research spans quantum machine learning , AI ethics , medical imaging , and environmentally sustainable AI , with recent publications on topics ranging from quantum neural networks to fairness in recommender systems . He contributes to the SCIENCE AI Centre and collaborates on initiatives like TreeSense for global tree resource monitoring.
Daniel Spikol is an Associate Professor at the Department of Computer Science , University of Copenhagen , affiliated with the Center for Digital Education and Human-Centred Computing section. His research focuses on multimodal learning analytics, computational thinking, and physical computing technologies that enhance learning, play, and reflection. Keywords: Learning Analytics, Human-Computer Interaction, Computational Thinking His recent work examines: Collaborative task design impacts on knowledge construction AI trust dynamics in educational contexts across six countries Smart learning environment integration with MMLA Design frameworks for multimodal analytics systems Key publications (2023-2025) analyze: Cultural factors in AI adoption Collaborative learning metrics Speech analytics for language acquisition He leads research bridging ambient computing, social signal processing, and educational innovation through physical computing toolkits like Talkoo (2016) and mBox (2024).
Omry Ross is an Associate Professor at the Department of Computer Science, University of Copenhagen. His research focuses on Programming Languages and Theory of Computation, with significant contributions to decentralized finance (DeFi), blockchain technology, and algorithmic governance. Research Interests: Decentralized Finance (DeFi) and Smart Contract Systems Blockchain Protocol Design and Cryptoeconomics Programming Language Theory and Formal Verification Token Governance in Decentralized Autonomous Organizations (DAOs) Algorithmic Game Theory and Market Mechanisms Recent publications highlight his work in AI moderation of online communities, compliance reporting in DLT systems, and MEV optimization in multi-block scenarios. His research bridges theoretical computer science with practical applications in financial cryptography. Scientific Awards: Nasdaq Nordic Foundation Grant (2021) Omry Ross collaborates extensively with researchers in blockchain and DeFi, including contributions to the Financial Cryptography and Data Security workshops.
