Rob Gleasure is a Professor in the Department of Digitalization at Copenhagen Business School (CBS), Denmark. His research focuses on the intersection of information systems, digital technologies, and human behavior, with particular expertise in blockchain technology, crowdfunding, AI applications, and technology adaptation. Based at Solbjerg Square 3 in Frederiksberg, he contributes to CBS's mission of advancing knowledge in business and society through digital transformation. Professor Gleasure's research spans several key areas within information systems and digital innovation: Digital finance and blockchain technologies, including cryptocurrency and financial applications Crowdfunding platforms and digital fundraising mechanisms Artificial intelligence applications in various domains including healthcare and banking Human-computer interaction and the psychological aspects of technology adoption Digital collaboration and the affective dimensions of online work Quantum computing infrastructure and emerging technologies His recent publications reveal an evolving research trajectory that increasingly addresses the societal implications of digital technologies. Gleasure has moved from foundational work on crowdfunding and blockchain to more complex examinations of AI ethics, gender bias in technological systems, and the psychological impacts of digital media. His research demonstrates growing attention to sustainable development goals, particularly those related to responsible consumption and production, reduced inequalities, and climate action. The interdisciplinary nature of his work bridges business, technology, and social sciences, often employing both qualitative and experimental methodologies. Professor Gleasure has served as a supervisor for numerous students (21 supervisor tasks mentioned) and has been active in academic service including co-chairing the ACM Collective Intelligence Conference in 2021. His research has attracted media attention, with contributions to discussions on cryptocurrency, carbon offsetting in aviation, and AI applications.
Jes Frellsen is an Associate Professor at the Department of Applied Mathematics and Computer Science (DTU) since 2016. Previously, he held academic positions at the IT University of Copenhagen (2016-2019), postdoctoral roles at University of Cambridge (2013-2016) and University of Copenhagen (2011-2013). Education: PhD in Bioinformatics (2011), University of Copenhagen MSc in Bioinformatics (2007), University of Copenhagen BSc in Mathematics and Computer Science (2005), University of Copenhagen EAP Exchange at University of California, Santa Cruz (2004-2005) Research Focus Jes Frellsen specializes in statistical machine learning , particularly generative AI and deep generative models with applications in bioinformatics . His work integrates Bayesian inference , directional statistics , and Markov chain Monte Carlo methods to address challenges in macromolecular structure prediction and missing data imputation . Recent efforts explore uncertainty quantification in image segmentation and generative modeling for materials science. Advising & Collaborations He actively supervises PhD students and postdoctoral researchers in projects spanning news recommendation systems , medical imaging , and 3D structure generation . Collaborations include work with Zoubin Ghahramani (Cambridge) and Thomas Hamelryck (Copenhagen), with contributions to protein structure prediction and statistical methods in structural bioinformatics .
Kohei Nakajima is an Associate Professor at the Department of Intelligent Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo. He holds concurrent positions at the Department of Creative Informatics and the Next Generation Artificial Intelligence Research Center (AI Center). As an Endowed Chair in Advanced Artificial Intelligence Education, he leads the Physical Intelligence Lab, which focuses on the intersection of soft robotics, nonlinear dynamics, and physical computing. His research interests center on Physical Reservoir Computing (PRC), a paradigm that exploits the natural dynamics of physical systems for computation, with applications in soft robotics, spintronics, and quantum machine learning. Nakajima's work demonstrates how physical systems can inherently process information without traditional digital computation, leveraging phenomena like chaos, bifurcations, and embodied intelligence. Nakajima's publications reveal a strong focus on understanding how physical systems can perform computational tasks. His recent work spans from biological applications (jellyfish cyborgs, ostrich-inspired robotics) to fundamental theoretical advances in reservoir computing. The research demonstrates how physical phenomena can be harnessed for information processing, with implications for energy-efficient computing and novel robotic control paradigms. As the organizer of the Reservoir Computing Seminar, Nakajima has built a vibrant research community exploring the nature of information processing across disciplines. His lab actively recruits graduate students and postdocs, indicating strong research momentum and institutional support for his work in physical intelligence.
Raghavendra Selvan, an Assistant Professor (Tenure Track) at the University of Copenhagen, holds joint appointments in the Machine Learning Section (Department of Computer Science), Kiehn Lab (Department of Neuroscience), and the Data Science Laboratory. His academic journey includes a PhD in Medical Image Analysis (2018), MSc in Communication Engineering (2015), and BSc in Electronics and Communication Engineering (2009). PhD - Medical Image Analysis, University of Copenhagen (2018) MSc - Communication Engineering, Chalmers University (2015) BSc - Electronics and Communication Engineering, BMS Institute of Technology, India (2009) His research focuses on Bayesian Machine Learning with emphasis on Medical Image Analysis, Graph-based Learning, Tensor Networks, Approximate Inference, and Multi-Object Tracking Theory. Recent publications highlight his contributions to environmentally sustainable AI practices, efficient deep learning in medical imaging, and novel applications of tensor networks. Key research areas: Green AI and Environmental Sustainability Medical Image Analysis Graph Neural Networks Crystal Structure Prediction Model Compression Materials Science Applications
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.
Thomas Bjørner is an Associate Professor at Aalborg University's Department of Architecture, Design and Media Technology within The Technical Faculty of IT and Design. He serves as Head of the Media Innovation & Game Research (Me-Ga) unit and co-founded the research network for qualitative methods (since 2007) with 40+ company collaborations. Specializes in qualitative/mixed methods for technology evaluation Teaches PhD courses in advanced qualitative methods EU expert evaluator for research grants His research focuses on gamified learning , VR for social communication , and technology acceptance studies , often addressing UN Sustainable Development Goals. Recent projects include: Audio-only VR for blind gamers Generative AI integration in education Plastic crisis awareness games Smart city implementation barriers Scientific Awards: Serious Game Competition Award (2022) International conference prizes (2020, 2018) With over 121 publications including two textbooks, his work emphasizes applied qualitative methods with improved validity in technology contexts, particularly for youth education and media research.
Sara Shafiee is a Senior Researcher at the Department of Civil and Mechanical Engineering , Technical University of Denmark (DTU) . She specializes in product configuration systems, manufacturing engineering, and AI-driven innovation. Her work bridges technical systems with organizational agility, emphasizing sustainability and customer-centric design. External Roles: Founder & CEO of DivERS (Jan 2021–) External Lecturer at Copenhagen Business School (2022–2024) Senior Business Consultant at Haldor Topsoe AS (2017–2019) Research Focus: Her work addresses challenges in product configuration systems, generative AI applications, and sustainable construction. Key themes include: Optimal product design through recommendation systems Agile methodologies in knowledge-intensive development Environmental impact monitoring via configurators Publications Trends (2023–2025): Recent work explores AI-driven manufacturing optimization, consumer-centric innovation strategies, and the integration of environmental monitoring into design systems. High-impact areas include generative AI applications (13K+ downloads) and modular construction configurators. Awards: Agnes & Betzy Award (2025) Nordic Women in Tech Leadership Award (2022) Best Digital Startup (Venture Cup Denmark, 2021) Innovation Fund Denmark Role Model (2018) Advising & Grants: Supervised PhD projects on recommendation systems and configurator design. Lead PI of the RECODE project (DFF Grant DKK 10M+, 2024–2027) focusing on deep learning for engineer-to-order systems. Labs & Teams: Core member of DTU’s Design and Manufacturing Systems group, collaborating with industry partners like Haldor Topsoe and DivERS to develop scalable configurator solutions.
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.
Anders Koed Madsen is a Professor at the Department of Culture and Learning, Faculty of Humanities and Social Sciences, Aalborg University. He is affiliated with the Technoanthropological Laboratory and leads research initiatives including AI for the People and MASSHINE. His work centers on digital methods, urban belonging, smart cities, and citizen science, often integrating ethnographic and participatory approaches. His research interests lie at the intersection of technology and society, focusing on digital placemaking , urban sensing , pragmatist philosophy , and inclusive urban planning . He explores how digital tools can be used to understand lived experiences in cities, especially among marginalized communities. His methodological focus includes participatory digital methods, geospatial photovoice, and computational ethnography. The recent articles highlight a consistent trend toward using digital and ethnographic methods to study urban life, technology assessment, and democratic participation. Themes include generative AI , digital epistemology , friction in machine reasoning , and inclusive city planning . His work increasingly incorporates large language models and open-source toolkits to democratize research and planning processes. Scientific Awards: European Union Prize for Citizen Science (2023) World Summit Awards (WSA) shortlist, Urban Belonging Project (2024) Ziman Award 2020: TANTlab AAU Talent (2018) Videnskabsministeriets EliteForsk-rejsestipendium (2011) Anders Koed Madsen has been actively involved in research grants and collaborative projects such as the Urban Belonging Project , Digital Placemaking and Soft City Sensing Research Network , and GE-AI: Generative Ethnographic AI . He frequently collaborates with interdisciplinary teams and institutions like IT University of Copenhagen and Gehl Architects. He advises on public engagement and has contributed to national and international discourse on digitalization and urban futures. He is a core member of the Technoanthropological Laboratory , which fosters interdisciplinary research on technology and society. The lab supports initiatives in digital methods, citizen science, and urban innovation. Madsen also contributes to public understanding through media engagement and workshops, promoting humanistic perspectives in technological development.
Georgios Arvanitidis is an Associate Professor at the Technical University of Denmark (DTU) in the Department of Applied Mathematics and Computer Science, specifically within the Section for Cognitive Systems (CogSys). He has established himself as a leading researcher in geometric machine learning, focusing on the application of differential geometry principles to enhance machine learning models. His work bridges theoretical mathematics with practical applications in artificial intelligence, with particular emphasis on understanding the geometric structure of data manifolds and latent spaces. Dr. Arvanitidis completed his educational journey with a Bachelor's degree from the Department of Informatics at the Aristotle University of Thessaloniki, followed by a Master's degree in Computer Science from Saarland University supported by the Max Planck Institute for Informatics. He earned his PhD at DTU's Cognitive Systems section under the supervision of Søren Hauberg, with additional research experience at Philipp Hennig's Probabilistic Numerics group. Prior to his current position as associate professor, he was a PostDoc at the Max Planck Institute for Intelligent Systems working with Bernhard Schölkopf. Dr. Arvanitidis's research primarily focuses on differential geometry in machine learning , where he explores how geometric structures can enhance representation learning and statistical modeling. His work in generative models investigates how learning the geometry of data manifolds can improve deep learning architectures. In the domain of deep learning theory , he examines why deep learning models generalize effectively on unseen data, with particular attention to the curvature properties of loss landscapes. His research in approximate Bayesian inference applies geometric principles to improve uncertainty quantification in neural networks. Through his innovative approaches, Dr. Arvanitidis has established himself as a leading researcher in geometric machine learning, contributing to both theoretical foundations and practical applications across various domains including robotics and life sciences. The publication trends of Dr. Arvanitidis reveal a consistent and evolving focus on geometric approaches to machine learning problems. His recent work (2023-2025) demonstrates increasing sophistication in applying Riemannian geometry to deep learning architectures, with particular emphasis on latent space geometry, optimization on manifolds, and geometric interpretations of neural network behavior. A notable pattern is the progression from foundational work on geometric representations to more applied research in areas like robotics and causal inference. His publications span top-tier conferences including NeurIPS, ICML, ICLR, and AISTATS, reflecting the high impact of his research. The interdisciplinary nature of his work is evident in collaborations across mathematics, computer science, and robotics domains, with recent papers addressing challenges in multimodal sampling, safety guarantees for dynamical systems, and counterfactual explanations. Dr. Arvanitidis has received several notable scientific awards and recognitions: Sapere Aude starting grant from the Independent Research Fund Denmark (DFF) GADL funding i-Rase, Pathfinder, and EIC (European Innovation Council) funding Best reviewer award for NeurIPS 2019 Best reviewer award for NeurIPS 2018 Best student paper award at Robotics: Science and Systems (R:SS) 2021 Dr. Arvanitidis actively mentors PhD students and researchers, currently supervising Alejandro Valverde, Johanna Gegenfurtner, and Albert Kjøller Jacobsen. He has previously co-supervised Alison Pouplin's PhD and worked with research assistant Georgios Pantis. His group receives substantial funding through multiple prestigious grants including the Sapere Aude starting grant from the Independent Research Fund Denmark, as well as European Innovation Council funding. He has been instrumental in creating opportunities for students interested in geometric machine learning, offering BSc and MSc thesis projects focused on generative models, deep learning theory, and optimization techniques. Dr. Arvanitidis also contributes significantly to the academic community as a reviewer for top conferences including ICLR and TMLR, and as an area chair for NeurIPS, ICML, AISTATS, and UAI. He co-organized the Machine Learning Summer School 2020 in Tübingen, further demonstrating his commitment to education and community building. Dr. Arvanitidis leads a vibrant research group focused on geometric machine learning within the Cognitive Systems section at DTU. His team includes multiple PhD students working on cutting-edge research at the intersection of differential geometry and artificial intelligence. The group has developed notable software tools, including the "geometric_ml" GitHub repository with over 70 stars, which contains implementations for applying Riemannian geometry in machine learning. His research has practical applications in robotics, where geometric approaches enable more robust motion planning, as evidenced by his work on "Reactive Motion Generation on Learned Riemannian Manifolds" which received a best student paper award. Additionally, his methodologies have found applications in life sciences, as mentioned in his 2022 AISTATS paper. The collaborative nature of his work is evident through extensive partnerships with researchers at institutions including the Max Planck Institute for Intelligent Systems, University of Cambridge, and various European universities. His recent news items indicate active engagement with the academic community through talks, conference presentations, and ongoing supervision of new PhD students joining his group.
Sadegh Talebi is a Tenure Track Assistant Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen . His research focuses on theoretical aspects of reinforcement learning, Markov decision processes, online learning, stochastic multi-armed bandit problems, and resource allocation in networks. Education BSc in Electrical Engineering (minor: Electronics) from Iran University of Science and Technology (IUST) (2004) MSc in Electrical Engineering (minor: Communication Systems) from Sharif University of Technology (2006) PhD in Electrical Engineering from the Department of Automatic Control at KTH Royal Institute of Technology (supervised by Alexandre Proutiere and Mikael Johansson) Research Specializes in theoretical foundations of reinforcement learning and online learning Key contributions in stochastic optimization, MDPs, and bandit algorithms Collaborates on applications in resource allocation and quantum computing Publications include high-impact work on offline RL, differentially private exploration, and scalable MDP solutions in journals like Neural Processing Letters and conferences such as NeurIPS and UAI.
Luka Radic is a Researcher in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His work bridges theoretical and applied research in machine learning, with a focus on quantum machine learning , large language models , and fairness in AI systems.
Md Saifuddin Khalid is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on digital training technologies, learning analytics, and human-computer interaction in educational contexts. He leads and collaborates on projects exploring AI applications in education, adaptive learning systems, and the impact of digital tools on teaching and learning outcomes. His work contributes to UN Sustainable Development Goals related to quality education and industry innovation. Khalid actively supervises PhD students and has published extensively on topics such as generative AI for literature reviews, user satisfaction in AI chatbots, and usability of digital training technologies in industries like pharmaceuticals. Key Projects: MIXAP-EU: Developing mixed reality tools for teachers in Europe. HEGenAI: Integrating generative AI into higher education assessments. Methods for evaluating digital training technologies in pharmaceutical production. Editorial Roles: Peer reviews for journals like International Journal of Human-Computer Interaction and Computers & Education . Khalid’s research emphasizes practical applications of technology in education and industry, with a focus on improving user experiences and outcomes through systematic evaluations and design frameworks.
Shashi Raj Pandey serves as Assistant Professor in the Department of Electronic Systems at Aalborg University's Technical Faculty of IT and Design, Denmark. His research is anchored in the Connectivity section and Connectivity Classique-Center for Classical Communication in the Quantum Era, with office location at Fredrik Bajers Vej 7C, C1-111, 9220 Aalborg Øst. His core research spans Network Economics, Game Theory, and Wireless Networks, with specialization in Decentralized Machine Learning and Semantic/Goal-oriented Communications. Current work integrates Digital Twin technologies with 6G systems for industrial automation and earth observation, emphasizing resource-efficient protocols for Internet of Things and edge intelligence applications. Recent publications (2024-2025) reveal a clear trajectory toward AI-6G convergence, featuring semantic communications for satellite imaging, game-theoretic network resource allocation, and digital twin implementations for autonomous systems. Key themes include communication efficiency in distributed learning and physical-digital world integration. Notable recognitions include: Best PhD Thesis Nominee (2021) Excellent Paper at Korea Software Congress, KIISE 2021 Student Best Paper Award at APNOMS 2019 Best Paper at Korea Software Congress, KIISE, 2018 Brain Korea 21st Century Plus Fellowship Academic service includes external PhD examination for EU SNS projects and peer review for premier conferences (AAAI, ICLR, ICML). His lab work within the Connectivity Classique-Center explores classical communication frameworks applicable to quantum-era networks, with focus on semantic information theory and decentralized network architectures.
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).
