Philip Rajani Lassen is a Lecturer at the Department of Computer Science , University of Copenhagen, affiliated with the Natural Language Processing (NLP) section . His work focuses on methods for automatic text processing, understanding, and generation using statistical models and machine learning. University: University of Copenhagen Department: Department of Computer Science Email: prl@di.ku.dk Research Interests: Natural Language Processing Machine Learning Computational Linguistics Artificial Intelligence Text Processing Multimodal Learning The NLP section at DIKU explores core and emerging topics in natural language processing, including meaning representations, cross-lingual NLP, parsing, and applications like automatic fact-checking and machine translation. They are part of the SCIENCE AI Centre at the University of Copenhagen.
Xiangyu Lu is a Lecturer in the Department of Computer Science at the University of Copenhagen, affiliated with the Natural Language Processing (NLP) section. Their research focuses on statistical models and machine learning methods for text processing, with applications in automatic fact-checking, machine translation, and multimodal language processing. University: University of Copenhagen Department: Department of Computer Science Email: xilu@di.ku.dk
Peter Trip Malmroes is an Instructor at the Department of Computer Science, University of Copenhagen. He is affiliated with the Natural Language Processing (NLP) section, which focuses on methods for automated text processing, understanding, and generation using statistical models and machine learning. His research interests align with NLP section priorities, including applications in automatic fact-checking, machine translation, question answering, and multi-modal language processing. The section also contributes to the SCIENCE AI Centre at the university. Contact details: Email: pema@di.ku.dk Address: Universitetsparken 1, 2100 Copenhagen Ø
Daniel Nicholas Mølhave is an Instructor at the Department of Computer Science (DIKU) , University of Copenhagen, located at Universitetsparken 1, 2100 København Ø. His work is associated with the Natural Language Processing (NLP) section, which focuses on methods for automated text processing, understanding, and generation using statistical models and machine learning. University affiliation: University of Copenhagen Department: Department of Computer Science (DIKU) Email: damo@di.ku.dk The NLP section at DIKU engages in cutting-edge research spanning core areas such as: Natural language understanding Multi-modal machine learning Explainable AI Visually-grounded language learning Cross-lingual NLP Language technologies Research applications include machine translation, misinformation detection, image captioning, and multilingual multimodal representation learning. The section contributes to the SCIENCE AI centre and offers courses in the Bachelor's and Master's programs in Computer Science, Machine Learning, and Data Science.
Mathias Theodor Jul Overby serves as a Lecturer at the Department of Computer Science (DIKU) and the Department of Mathematical Sciences at the University of Copenhagen (Faculty of Science). His academic work intersects with the Natural Language Processing (NLP) section, which focuses on statistical models, machine learning, and applications like automatic fact-checking, machine translation, and multi-modal language processing. Instructor roles in NLP and mathematical sciences Part of the SCIENCE AI centre at UCPH Teaching involvement in Computer Science, Machine Learning, and Data Science programs His research interests include natural language understanding, computational linguistics, and machine learning techniques for text analysis. The NLP section's work spans core tasks like parsing, transfer learning, and multimodal translation. Contact: maov@di.ku.dk , mtjo@math.ku.dk
Benjamin Daniel Nyberg Shultz is an Instructor at the Department of Computer Science (DIKU), University of Copenhagen. His work aligns with the Natural Language Processing (NLP) section , focusing on methods for automated text processing, understanding, and generation using statistical models and machine learning. Research Focus : Core NLP tasks like parsing, cross-lingual processing, and transfer learning; applications in automatic fact-checking, machine translation, and visually grounded language learning. Teaching : Contributes to courses in Computer Science, Machine Learning, and Data Science, including advanced topics in NLP and deep learning. Collaboration : Part of the SCIENCE AI Centre, exploring intersections between NLP and computer vision in multi-modal learning.
Dr. Ahmad Salim Al-Sibahi is an Assistant Professor in the Programming Language and Theory of Computation Section at the University of Copenhagen's Department of Computer Science. He is a core member of the Aleatory Science Team, focusing on probabilistic programming applications in protein folding and inference algorithm optimization. His work combines formal methods with practical machine learning frameworks like Pyro. Educational Background: PhD in Computer Science (2014-2017), IT University of Copenhagen MSc in Computer Science (2014), IT University of Copenhagen Postdoctoral Researcher at University of Copenhagen (2017-2019) Research Interests: His primary focus lies at the intersection of probabilistic programming and computational biology. He develops efficient inference algorithms for probabilistic models, particularly in protein structure prediction and voucher scanning applications. His earlier work includes formal verification of program transformations using inductive refinement types and static analysis techniques. Awards: Best Paper Award at GPCE 2018 Contributions: He contributed to dependently-typed language Idris and co-developed family-based model checking techniques. His research bridges theoretical programming language constructs with applied problems in bioinformatics and software modernization.
Jakob Grue Simonsen is a Professor and Department Chair at the Department of Computer Science (DIKU), University of Copenhagen. He holds a Dr. Scient., PhD, and MBA. His research focuses on the mathematics of computation, including computability theory, term rewriting, lambda calculus, complexity hierarchies, and symbolic dynamics, with applications in information retrieval and human-computer interaction. His primary research explores infinite computational processes and their finite representations, alongside practical work in neural hashing, fact-checking systems, and quantum language models. Previously, he contributed to constructive mathematics and biocomputing. An analysis of his 15 most recent publications (2015–2022) reveals strong emphasis on: Theoretical computability and game-theoretic models Neural networks for NLP (BERT, semantic hashing) Fact verification and explainable AI methodologies Efficient retrieval algorithms and recommendation systems Awards: RTA 2004 Best Paper Award CHI '13 Honorable Mention CHI '14 Honorable Mention
Abhijit Mazumdar serves as a Research Fellow in the Department of Electronic Systems within Aalborg University's Faculty of IT and Design. He is actively affiliated with the Automation & Control Learning and Decisions Lab, focusing on theoretical and applied aspects of safety-critical decision systems. His research centers on Markov Decision Processes , safety verification , and reinforcement learning with critical applications in control systems. Key interests include stochastic safety analysis of hybrid systems, constrained optimization under uncertainty, and model-free safety verification techniques that prevent safety violations during learning. His work bridges theoretical computer science with practical engineering implementations in autonomous systems. Recent publications demonstrate a strong trend toward formal safety guarantees in learning-based control, with 75% of his 2023-2024 output addressing safety verification for Markovian systems. His fingerprint analysis shows dominant specialization in Computer Science (100%) and Engineering (100%) , particularly in safety-critical domains where theoretical rigor meets real-world implementation constraints. Dr. Mazumdar collaborates extensively within Aalborg University's technical ecosystem, particularly with researchers in control theory and formal methods. His laboratory work in the Automation & Control Learning and Decisions Lab focuses on developing mathematically rigorous frameworks for safe decision-making in uncertain environments.
Roger Buch is a Professor at the Danish School of Media and Journalism, where he holds positions in both the Journalism program and Research department. With a Ph.D. from the University of Southern Denmark (1993-1996), he has established himself as a prominent researcher in Danish political systems and media studies. His research focuses on Political Systems , Party Organization , Danish Politics , and Media's Role in Democracy . Buch employs case study methodology across various political contexts, with particular emphasis on municipal governance, electoral processes, and the intersection of international events with local politics. His fingerprint analysis shows strong engagement with communities, organizational perspectives, and comparative studies of Denmark, UK, and Italy. Buch has produced an impressive 142 research outputs, including numerous journal articles in major Danish publications. His recent work (2024-2025) demonstrates continued productivity with articles examining municipal elections, AI's role in voting decisions, and how international conflicts like Gaza influence local politics despite constitutional limitations on municipal foreign policy. Active Principle researcher on multiple significant projects (2022-2026) 3,899 press/media mentions reflecting substantial public engagement 41 documented academic and public speaking activities Buch maintains an active role in both academic research and public discourse, serving as a bridge between scholarly analysis and practical political understanding in Denmark. His work on media ethics, local journalism, and political decision-making continues to shape discussions about democracy and governance at multiple levels.
Seraina Nett serves as a Teaching Assistant Professor in the Department of Cross-Cultural and Regional Studies at the Faculty of Humanities, University of Copenhagen. Her research integrates Assyriology with digital methodologies to investigate ancient West Asian societies through Sumerian and Akkadian texts from 2100-1200 BCE. Education: PhD in Assyriology, Department of Cross-Cultural and Regional Studies, University of Copenhagen Research Focus: Dr. Nett specializes in social and economic history of the Ancient Near East, examining interactions between linguistic/ethnic groups through interdisciplinary frameworks. Her linguistic research analyzes multilingualism and structural features of Sumerian and Akkadian, while her Digital Humanities work pioneers computational tools for cuneiform analysis, including geospatial mapping and quantitative indices for textual corpora. Publication Trends: Recent output (2023-2025) reveals three dominant themes: 1) Digital innovation in cuneiform studies (e.g., CIGS-AE index development), 2) Geospatial analysis of textual landscapes, and 3) Public engagement addressing historical misrepresentation in contemporary discourse. This reflects a strategic shift toward methodological advancement while maintaining core expertise in ancient socioeconomics. Professional Engagement: Dr. Nett actively shapes her field through conference organization including the Nordic Summer School on Digital Applications in Assyriology (2022-2024) and peer review for archaeology journals. Her media appearances in podcasts like Thin End of the Wedge demonstrate commitment to public scholarship. Research Infrastructure: She co-leads collaborative projects such as Geomapping Landscapes of Writing and the CIGS-AE initiative, developing digital frameworks that integrate material culture with textual analysis to reconstruct ancient writing ecosystems.
