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Emilia MendesView profile

Professor

• Software Engineering
• Empirical Software Engineering
• Human-Centric Software Engineering
• +12 more

Emilia Mendes is a Full Professor in the Department of Electrical and Computer Engineering at Aarhus University . Her research focuses on Empirical Software Engineering , particularly human-centric approaches, evidence-based decision-making, and the application of machine learning and statistical techniques in software development. Current research themes: Human-Centric Software Engineering, Evidence-Based Research, Statistical/Machine-Learning Techniques, and Value-Based Software Engineering. Developed tools for team climate forecasting, capability measurement, and value-based decision-making. Research Trends: Her work bridges software engineering with psychology (personality traits, team dynamics), machine learning (effort estimation, dementia prognosis), and value-based frameworks for decision-making. She emphasizes industrial applications, including agile methodologies, cross-company predictions, and Bayesian network modeling. Scientific Impact & Awards: 10,018 citations, h-index 58. Ranked #32 in Empirical Software Engineering Scholars (Google Scholar). Ranked #20 in Top Computer Science Scientists in Sweden (2023). Top 2% scientist in the world (2019, 2020, 2022; only female in Sweden for SE in 2022. Nine best paper awards at international conferences. Editorial board member: Information and Software Technology , ACM Computing Surveys , former roles at IEEE Transactions on Software Engineering and others. Grants & Leadership: Awarded €11.921.603 in research grants. Held leadership roles as General Chair (EASE 2017), PC Co-Chair (EASE 2012, ESEM 2012), and active participant in 200+ academic events.

• Information and Knowledge Management
• Product Configuration Systems
• Supply Chain Management
• +5 more

Anders Haug serves as Associate Professor at the Department of Business and Sustainability (DBS) within the University of Southern Denmark's Kolding campus. Having joined the university in 2008 as Assistant Professor in the Department of Entrepreneurship and Relationship Management before transitioning to his current role in 2010, his academic career spans over 15 years of research and teaching in operations, supply chain, and digital transformation contexts. His work bridges theoretical rigor with practical industry applications, particularly in engineer-to-order manufacturing and logistics sectors. Education: PhD in communication, representation and automation of design knowledge (2005-2007) Haug's research centers on information and knowledge management systems, with deep expertise in data quality frameworks, knowledge-based configuration, and digitalization of business processes. His fingerprint reveals distinctive contributions to product configuration systems, digital twin applications, and supply chain resilience—particularly examining how configurators transform warehouse services, manufacturing processes, and product-service ecosystems. Recent work increasingly addresses sustainability through green dynamic capabilities frameworks and life cycle assessment tools, maintaining strong empirical grounding via case studies in Danish manufacturing. Analysis of his 2024-2025 publications shows converging trends: digital technologies (configurators, digital twins) are examined through operational performance lenses while addressing sustainability imperatives. These works span operations management, information systems, and strategic management disciplines but consistently prioritize practical implementation frameworks for manufacturing SMEs. The research demonstrates methodological diversity—from conceptual modeling to empirical case studies—with strong industry relevance in logistics, engineering-to-order contexts, and manufacturing digitization. Scientific Awards: Top read paper in Business 2017/18 (Wiley) (2019) Haug has supervised 34 teaching courses between 2018-2024 covering business information systems, digitalization projects, and supply chain management. His academic service includes extensive peer reviewing for conferences like NOFOMA and DRS, plus organizational roles in Nordic business research networks. While specific grant details aren't provided, his 175+ research outputs and industry collaborations (evidenced by consultant work since 2006) indicate substantial research funding engagement. Media contributions on 3D printing and business process efficiency demonstrate effective knowledge transfer to practitioners. Though no dedicated research lab is specified, Haug's extensive co-authorship network—including collaborations on projects like digital twin implementation and configurator development—reveals embeddedness in multiple research collectives. His industry-facing approach manifests through case studies with logistics providers, manufacturer partnerships, and practical frameworks for warehouse service design and supply chain resilience.

• Molecular Imaging
• Positron Emission Tomography (PET)
• Cancer
• +4 more

Professor Andreas Kjær (MD, PhD, DMSc) serves as Head of the Cluster for Molecular Imaging (CMI) at the Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen. He is also Chief Physician at Rigshospitalet and co-founder of Minerva Imaging, a CRO established in 2011. University of Copenhagen Department of Biomedical Sciences Cluster for Molecular Imaging Rigshospitalet His research focuses on translational molecular imaging with PET/PET-MRI and targeted radionuclide therapies (theranostics) for precision medicine in cancer, cardiovascular, and inflammatory diseases. CMI bridges basic research to clinical implementation, leveraging interdisciplinary expertise in physics, chemistry, biology, and medicine to develop imaging agents and therapies. Recent publications highlight advancements in theranostics , PET/CT , hyperpolarized MRI , and radiopharmaceutical development . Collaborations span oncology, cardiology, and nuclear medicine, with clinical trials in neuroendocrine tumors and metabolic imaging. Current affiliations include University of Copenhagen (Professor, Department of Biomedical Sciences) Rigshospitalet (Chief Physician)

Sergey RosbakhView profile

Assistant Professor

• Plant Ecology
• Seed Biology
• Climate Change Adaptation
• +4 more

Sergey Rosbakh is a Tenure Track Assistant Professor in the Department of Plant and Environmental Sciences at the University of Copenhagen, specializing in organismal biology. His research focuses on plant ecology, seed biology, and climate change adaptation across diverse ecosystems including alpine, Mediterranean, and freshwater habitats. Dr. Rosbakh's research interests encompass several critical areas in contemporary plant science: Seed ecology and germination strategies under climate change Plant adaptation to temperature stress and heat tolerance Functional trait analysis across plant communities Pollen temperature tolerance and reproductive biology Soil seed bank dynamics and persistence mechanisms Long-term vegetation changes in European ecosystems His recent publications demonstrate sophisticated analytical approaches combining field studies with large-scale meta-analyses. Through examining seed morphology, germination patterns, and plant responses across climatic gradients, Dr. Rosbakh contributes significantly to predicting how plant communities might respond to ongoing climate change. His work on the ReSurveyEurope database represents a major collaborative effort to document vegetation changes across the continent. Dr. Rosbakh actively collaborates with researchers across Europe and has published in leading ecological journals including Ecology and Evolution, Plant Cell and Environment, and Journal of Vegetation Science. His publications from 2024-2025 show an active research trajectory with multiple high-impact papers currently in press.

Arijit KhanView profile

Associate Professor

• Graph Neural Networks
• Blockchain
• Data Management
• +5 more

Arijit Khan is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark. He leads the Data Engineering, Science and Systems group and is affiliated with the Technical Faculty of IT and Design. His research focuses on Graph Neural Networks , Blockchain , Data Management , and AI interpretability . He is the Principal Investigator (PI) of a major project on Data Management, Fundamental Algorithms, and Machine Learning for Emerging Problems in Large Networks (2022–2027). Research Interests : Graph Data Management & Machine Learning Blockchain Transaction Analysis Large Language Model + Knowledge Graph Synergies Healthcare AI (e.g., ICU glucose prediction) Explainable AI for Graph Neural Networks Research Trends : His publications emphasize neuro-symbolic systems , uncertain graph analysis , and AI-driven blockchain insights . Recent work bridges large language models with knowledge graphs and explores GPU performance optimization via shader code analysis. Awards & Grants : No explicit awards listed, but his active research grants include a 5-year project on large network analysis with interdisciplinary applications in life and health sciences. Funding emphasizes algorithmic innovation and data science integration. Labs/Teams : Head of the Data Engineering, Science and Systems research group, focusing on AI for societal impact ('AI for the People') and scalable graph data systems. Collaborations span blockchain analytics, healthcare informatics, and GPU architecture design.

• Differential Privacy
• Algorithms
• Theoretical Computer Science
• +3 more

Teresa Anna Steiner serves as an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, specializing in algorithmic research with emphasis on privacy-preserving computational methods and theoretical computer science. Her research centers on differential privacy mechanisms, where she investigates trade-offs between data utility and privacy guarantees through rigorous analysis of noise injection techniques like Laplace and Gaussian distributions. She extends this work to dynamic graph databases requiring real-time privacy protections and develops novel text indexing approaches for regular expression pattern matching, contributing to foundational advancements in algorithm design for sensitive data environments. Recent 2025 publications reveal a cohesive research trajectory focused on practical implementations of differential privacy across diverse data structures, with particular attention to variance optimization in noise mechanisms, edge-level privacy in evolving graphs, and efficient indexing for textual pattern recognition. These works collectively address critical challenges in balancing computational efficiency with robust privacy guarantees in modern data systems. No scientific awards were documented in the available information. Details regarding student advising or research grant funding were not specified in the provided materials.

Chenjuan GuoView profile

Associate Professor

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

Patrick MunkView profile

Associate Professor

• Antimicrobial Resistance
• Genomic Epidemiology
• Metagenomics
• +12 more

Patrick Munk is an Associate Professor at the National Food Institute, Technical University of Denmark (DTU), where he leads research in the Research Group for Genomic Epidemiology and contributes to the DTU Microbes Initiative. His work integrates genomics, metagenomics, and bioinformatics to study antimicrobial resistance (AMR) across human, animal, and environmental systems. He is actively involved in global surveillance efforts and is currently accepting PhD students. PhD, MSc Member, The Danish Young Academy (2024–2029) Patrick Munk’s research focuses on antimicrobial resistance, genomic epidemiology, metagenomics, and bioinformatics . His work leverages large-scale sequencing of sewage, livestock, and environmental samples to understand the global spread of resistance genes. He has led and contributed to major international studies, including the global sewage surveillance project across 101 countries, and has developed key genomic resources such as the PanRes and MetalResistance databases. His recent publications span high-impact journals like Nature Communications and Nature Microbiology , with themes centered on sewage-based surveillance, fecal resistomes in livestock, source attribution of microbial communities, and novel sequencing methodologies . His research has been widely covered in media and policy documents, reflecting its societal impact. Genomic analysis of sewage from 101 countries (Nature Communications, 2022) Time-series metagenomics for source-attributed surveillance (Nature Communications, 2024) High-throughput single-cell metagenomic sequencing (Frontiers in Microbiology, 2025) Patrick Munk has received recognition through significant media coverage, policy citations, and widespread academic engagement. His work has been highlighted in over 40 news outlets, referenced in policy sources and patents, and discussed across social media and academic platforms like Mendeley and Wikipedia. He supervises multiple PhD students and is a Principal Investigator or Supervisor on several active and completed projects, including Bioinformatic methods for multi-metagenome integration and Host range and epidemiology of antibiotic resistance . He is also a co-creator of key datasets that support open science in AMR research. His lab is part of a broad collaborative network involving DTU, international consortia, and public health agencies.

Katja HoseView profile

Professor

• Query Processing
• Semantic Web
• Linked Data
• +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.

Daniel HardtView profile

Associate Professor

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

• Macroevolution
• Convergent Evolution
• Biogeography
• +6 more

Mark D. Scherz is an Associate Professor at the Natural History Museum of Denmark, University of Copenhagen. His work focuses on evolutionary biology, taxonomy, and herpetofauna of Madagascar, integrating molecular and morphological approaches. ERC Starting Grant (GEMINI) for vertebrate miniaturization genomics Co-PI on DFG Taxon-OMICS grant Develops integrative taxonomic methods using micro-CT and museomics Research Interests : Specializes in the evolutionary dynamics of giant pseudo-island systems like Madagascar, investigating macroevolution, convergent evolution, and speciation. Key projects include: Phylogenetic analysis of microhylid and mantellid frogs Comparative osteology of miniaturized vertebrates Biofluorescence mechanisms in geckos and chameleons Biogeographic patterns in Malagasy reptiles/amphibians Publication Trends : Recent work emphasizes cross-disciplinary approaches (2025: skull morphology in snakes; 2024: amphibian genomics) 2021 studies on fluorescence and miniaturization Long-term taxonomic revisions of amphibians (2019-2024) Scientific Awards : ERC Starting Grant (2025) DFG Priority Programme participant (Taxon-OMICS) Collaborations : Works closely with Miguel Vences (Technical University of Braunschweig) and Frank Glaw (Bavarian State Collection of Zoology), focusing on Malagasy amphibians and reptiles.

Jens Dick-NielsenView profile

Professor

• Finance
• Fixed Income
• Credit Risk
• +4 more

Jens Dick-Nielsen is a Professor at Copenhagen Business School in the Department of Finance . His work primarily focuses on fixed income markets, liquidity, credit risk, and financial market microstructure. Institution: Copenhagen Business School Department: Department of Finance Academic Rank: Professor Research interests include: Corporate bond markets and liquidity Bank equity risk and financial premiums Data transparency and TRACE system analysis Market segmentation in arbitrage Role of underwriter relationships in bond issuance Policy implications in banking and capital markets Recent publications highlight trends in bond market dynamics, financial intermediation, and liquidity risk. Key themes include empirical analysis of TRACE data, dealer networks, and post-crisis market structures. Scientific awards and honors: Jens Dick-Nielsen has supervised 10 research works and contributed to 23 media appearances. His collaborations span topics like banking capital costs, liquidity analysis, and market microstructure studies.

Christoph Houman EllersgaardView profile

Lecturer

• Power Elite Networks
• Corporate Governance
• Historical Sociology
• +5 more

Christoph Houman Ellersgaard is a Lecturer at the Department of Organization, Copenhagen Business School, specializing in power elite networks, corporate governance, and social inequality. His work intersects with the United Nations Sustainable Development Goals (SDGs), particularly addressing systemic power structures and democratic accountability. Research focuses on technocratic elements of the Danish power elite Analyses historical and contemporary elite career networks Examines Greenland's political economy and elite dynamics Critiques foundation governance and corporate domination Recent publications highlight climate governance challenges, network analysis methodologies, and the EU's Horizon research program. He contributes to public debates through media outlets like Politiken and Information, addressing inequality, corporate power, and policy transparency.

Maria Del Rio-ChanonaView profile

Assistant Professor

• Network Science
• Agent-Based Modeling
• Large Language Models
• +7 more

Maria Del Rio-Chanona is an Assistant Professor in the Department of Computer Science at University College London (UCL), where she conducts interdisciplinary research at the intersection of network science, machine learning, and economic modeling. She is also a member of the External Faculty at the Complexity Science Hub since January 2025, reflecting her ongoing engagement with complex systems research. Her academic background includes a PhD in Mathematics from the University of Oxford, where she was part of the Institute for New Economic Thinking at the Oxford Martin School, and undergraduate studies in Physics at UNAM, Mexico. Her research interests center on understanding socioeconomic transformations using computational methods. She specializes in applying Large Language Models (LLMs), Agent-Based Models (ABMs), and network analysis to study the impacts of generative AI, the net-zero transition, and global crises like the COVID-19 pandemic on labor markets, economic resilience, and public discourse. Her work often involves analyzing large-scale digital trace data, including online labor platforms and social media, to uncover behavioral and structural shifts. The recent publications reveal a consistent focus on labor market dynamics in the face of technological disruption. Her work examines how generative AI reshapes demand for freelance skills, reduces public knowledge sharing on platforms like Stack Overflow, and influences mental health discourse in relation to employment decisions. She also investigates broader economic modeling, including pandemic shock propagation and employment transitions during decarbonization. Among her scientific recognitions is the Emerging Scientific Award from the Complex Systems Society in 2023. She has collaborated with international policy organizations such as the International Monetary Fund (IMF) and the International Labour Organisation (ILO), underscoring the policy relevance of her research. Maria Del Rio-Chanona has held prestigious research positions, including as a James S. McDonnell Foundation (JSMF) Postdoctoral Fellow at the Complexity Science Hub and a Visiting Fellow at the Growth Lab, Harvard Kennedy School. These roles have enabled her to lead and contribute to high-impact interdisciplinary projects. While no current advisees are listed, she actively engages with PhD opportunities and interdisciplinary research networks. She is involved in multiple research initiatives, including the development of HuBERT, an NLP algorithm for extending the Seshat Global History database, and projects studying labor transitions in net-zero scenarios. Her work on the HiST-LLM benchmark for historical knowledge in LLMs highlights her innovative integration of AI with social science.

• Jurisprudence
• Legal Philosophy
• Human Rights
• +6 more

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.