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Jeppe Lund NielsenView profile

Professor

• Microbial Ecology
• Functional Ecology
• Genomics
• +10 more

Jeppe Lund Nielsen is a Professor in the Department of Chemistry and Life Sciences at the Faculty of Engineering and Science, Aalborg University, Denmark. His research lies at the intersection of microbial ecology, genomics, and environmental biotechnology, with a strong focus on sustainable solutions for wastewater treatment, anaerobic digestion, and environmental monitoring. His primary research interests include Microbial Ecology, Functional Ecology, Genomics, Molecular and Technical Microbiology, Anaerobic Digestion, Metagenomics, and eDNA Metabarcoding . He applies molecular tools to understand microbial community dynamics in engineered and natural systems, particularly in wastewater and marine environments. His work contributes to the UN Sustainable Development Goals related to clean water, sustainable cities, and climate action. The recent publication trends reflect a strong emphasis on environmental applications of microbiome science, including nitrogen cycling in wastewater, bioaerosol exposure in workers, eDNA-based biodiversity monitoring, and valorization of waste streams through biotechnology. His articles frequently appear in high-impact environmental and microbiological journals and demonstrate interdisciplinary collaboration across engineering, ecology, and public health. Among his notable scientific contributions are leadership roles in significant research projects such as the AAU Bubble Project (Power2Proteins), JAMBO seabed impact study, and investigations into biotechnological pesticides. He has also contributed to public discourse through media engagement on topics like pesticide regulation and environmental innovation. Professor Nielsen has supervised PhD students, including M. Eskeldsen, and is actively involved in grants and collaborative research across Europe. He is a key member of research teams focused on environmental microbiology, wastewater biotechnology, and marine impact assessments. His lab integrates molecular biology, bioinformatics, and environmental engineering to address pressing sustainability challenges.

Alex ArenasView profile

Professor

• Complex Systems
• Network Science
• Multilayer Networks
• +12 more

Alex Arenas is a Full Professor in the Department of Computer Engineering and Mathematics at Universitat Rovira i Virgili (URV), Tarragona, Spain. He is also an External Faculty member at the Complexity Science Hub in Vienna and Chief of Complex Systems Science at the Pacific Northwest National Laboratory, USA. His research spans complex systems, network science, computational epidemiology, and multilayer dynamics, with applications in public health, neuroscience, and social systems. Research Interests: His work focuses on the physics of multilayer networked systems, particularly the interplay between structure and function in complex networks. Key areas include synchronization, epidemic modeling, network medicine, the physics of the microbiome, and higher-order interactions in spreading processes. He investigates dynamic transitions using functional multilayer frameworks and develops models for real-world systems like urban mobility and misinformation diffusion. The recent articles highlight a strong trend in computational epidemiology, especially post-COVID modeling of vaccination strategies, rebound dynamics, and wastewater surveillance. There is also significant work on synchronization in oscillator networks, chimera states, and higher-order network effects, reflecting a deep engagement with nonlinear dynamics and theoretical network science. Applications span medicine, urban planning, and social systems. Scientific Awards: Fellow, American Physical Society (2018) Fellow, Network Science Society (2020) ICREA Academia (2011, 2017, 2022) Narcís Monturiol Medal (2022) Web Science Trust Test of Time Award (2024) Complex Systems Society Senior Award (2024) Advising and Grants: Arenas has supervised numerous PhD students and postdoctoral researchers, though specific names are not listed. He has been Principal Investigator on 47 research projects, including EU FP7 projects, a James S. McDonnell Foundation grant, and Horizon Europe's CREXDATA project. He has served as an editor for Physical Review E , Journal of Complex Networks , and Network Neuroscience , and has reviewed for major funding agencies including ERC, MINECO, and international bodies. Labs and Teams: He leads the Alephsys Lab at URV, which develops tools like Radatools for network analysis and community detection. His team focuses on interdisciplinary modeling of real-world complex systems using data-driven and theoretical approaches.

Arash NematiView profile

Assistant Professor

• Solid Oxide Fuel Cells
• Renewable Energy
• Electrochemistry
• +5 more

Arash Nemati is an Assistant Professor at the Department of Energy Conversion and Storage, Technical University of Denmark (DTU). His research focuses on sustainable energy technologies, particularly solid oxide fuel cells, ammonia-fueled systems, and multiphysics modeling of energy conversion processes. He leads and participates in EU-funded projects such as RESCUE and X-SEED, aiming to advance renewable energy storage and green hydrogen production. His work addresses challenges in electrochemical systems, including durability, efficiency, and integration of renewable energy sources. He has received notable recognition, including a Highly-cited Paper award (Web of Science, 2022) and the DAAD Green Hydrogen Research Tour Scholarship (2024). Nemati actively supervises PhD students in areas like co-electrolysis and solid oxide electrolysis cell modeling. His expertise spans thermochemical processes, numerical simulations, and techno-economic evaluations of energy systems. Key projects include developing ammonia-driven reversible solid oxide cells for grid storage and optimizing pyrolysis-electrolysis systems for methanol and char production. His research aligns with UN Sustainable Development Goals, emphasizing clean energy and climate action.

Johannes BjervaView profile

Professor

• Natural Language Processing
• Artificial Intelligence
• Linguistic Typology
• +5 more

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).

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.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +5 more

Sophia Natasha Wilson is a Research Fellow in the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in machine learning applications across interdisciplinary domains. She is affiliated with the SCIENCE AI Centre and holds a cross-departmental position at the Niels Bohr Institute . Her research bridges theoretical machine learning with practical implementations in healthcare, quantum computing, and environmental sustainability. University of Copenhagen Department of Computer Science (DIKU) Niels Bohr Institute SCIENCE AI Centre Her research focuses include: Quantum-enhanced machine learning algorithms Explainable AI for healthcare applications Environmental sustainability in computing Emotion-aware language models Quantum computing hardware optimization Public health risk modeling Her recent publications demonstrate cross-disciplinary work in quantum machine learning (hybrid optical processors, qubit stabilization), health informatics (hypothyroidism analysis, nursing values evaluation), and ethical AI (sustainable AI, fairness in recommender systems). Technical work also appears in non-Euclidean generative models and real-time adaptive systems . Current projects include quantum dot array simulation (QDarts platform) and federated learning for personalized medicine . She contributes to the TreeSense center for remote sensing of global tree resources and works on climate-aware AI frameworks.

• Soil Microbiology
• Antibiotic Resistance
• Environmental Contaminants
• +5 more

Kristian Koefoed Brandt serves as Associate Professor in the Department of Plant and Environmental Sciences at the University of Copenhagen's Faculty of Science, specializing in Microbial Ecology and Biotechnology. His research focuses on environmental microbiology, antibiotic resistance dynamics, and contaminant fate in agricultural systems. His primary research interests span Soil Microbiology, Antibiotic Resistance, Environmental Contaminants, and Wastewater Treatment, with significant contributions to understanding microbial processes in organic agriculture and wastewater systems. Key themes include bioremediation strategies, pesticide risk assessment, and the One Health implications of emerging contaminants. Analysis of his recent publications reveals a strong emphasis on antimicrobial resistance surveillance across environmental interfaces, particularly in wastewater-river systems and agricultural settings. His work integrates molecular techniques with large-scale environmental monitoring, often through European collaborative projects examining climate gradients and farming practices. 295 citations for 'Emerging contaminants: A One Health perspective' (2024) Featured in 13 news outlets and referenced in Wikipedia High-impact research on wastewater-based disease surveillance Extensive collaboration across European research networks Professor Brandt actively contributes to environmental risk assessment frameworks, particularly regarding recycled nutrients in organic agriculture. His research group employs advanced molecular markers and isotope techniques to study microbial interactions, with significant implications for sustainable agricultural practices and public health monitoring systems.

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.

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +3 more

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.

• Bioinformatics
• Protein Stability
• Molecular Biology
• +3 more

Amelie Stein is an Associate Professor at the Department of Biology, University of Copenhagen, specializing in Bioinformatics and RNA Biology. Her research focuses on protein stability, molecular mechanisms of disease variants, and computational methods for protein design. She is affiliated with the UCPH Quantum Hub, reflecting interdisciplinary interests in biological systems. Her work integrates bioinformatics tools, mutational scanning, and structural biology to understand protein degradation pathways and their relevance to human diseases such as Lynch syndrome and metabolic disorders. Key research areas include analyzing protein variants using deep learning models (e.g., SSEmb), developing web-based tools like MutationExplorer for 3D visualization, and characterizing disease-linked mutations in proteins such as Parkin and MLH1. Her publications highlight breakthroughs in rapid protein stability predictions, degon mapping, and the interplay between protein toxicity and degradation. No scientific awards are explicitly mentioned in the provided texts. Stein’s research also explores the application of computational approaches to biotechnology and therapeutic development, emphasizing translational applications of her findings. Her lab, linked to the SCARB research group (https://www1.bio.ku.dk/english/research/scarb/), focuses on structural and computational biology, with ongoing projects involving protein quality control networks and enzyme variant analysis. Collaborations span molecular biology, bioinformatics, and interdisciplinary quantum-related research through her UCPH Quantum Hub membership.

Martin ElsmanView profile

Professor

• Programming Languages
• Theory of Computation
• Functional Programming
• +5 more

Martin Elsman is a full-time Professor in the Programming Languages and Theory of Computation section at the Department of Computer Science, University of Copenhagen (DIKU). He serves as head of the PLTC section and head of studies for the BSc education in Computer Science and Economics. Elsman is also an active maintainer of several software tools including the MLKit and SMLtoJs. Joined DIKU in 2012 after 4 years at SimCorp (2008-2012) and previous Associate Professorship at IT University of Copenhagen (2003-2008). Co-developer of Futhark, TAIL APL compiler, SMLtoJs, and SMLserver. Education: M.Sc. in Engineering, Technical University of Denmark Ph.D. in Computer Science, University of Copenhagen (DIKU), supervised by Mads Tofte. Research Interests: Elsman works on programming language design and implementation, with a focus on functional programming, module systems, domain-specific languages for financial contracts, region-based memory management, compilation techniques for parallelism, program optimization, and static type systems. His work spans both theoretical and applied domains, including blockchain-based financial contract execution, web technology, and GPU programming using functional languages. Publication Trends: His recent articles focus on functional programming, array programming, parallelism, and memory management. Topics include region inference, type systems for data-parallelism, program optimization techniques, and domain-specific compilation for financial and quantum computing. He frequently collaborates with Troels Henriksen and others on tools like Futhark and MLKit.

Abdulkadir CelikkanatView profile

Assistant Professor

• Computer Science
• Machine Learning
• Data Science
• +3 more

Abdulkadir Celikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design and the Data, Knowledge and Web Engineering research group. His research focuses on graph representation learning, network analysis, bioinformatics, and machine learning applications in dynamic systems. Key projects include the Villum Foundation-funded 'DarkScience: Illuminating microbial dark matter through data science,' which explores metagenomic binning and microbial ecology using advanced data science techniques. He has been recognized with the Best Paper Award (2023) for contributions to temporal graph analysis and modeling. His work spans continuous-time dynamic node representations, scalable genome profiling, and polarization detection in social networks. Celikkanat collaborates widely, contributing to interdisciplinary research at the intersection of computer science, biology, and environmental science. Recent publications highlight innovations in graph embeddings, citation network modeling, and hybrid membership latent distance models. His research addresses challenges in low-dimensional graph representations, efficient kernel methods, and integrating biological networks for protein analysis.

Henning Tangen SøgaardView profile

Associate Professor

• Mechanical Engineering
• Agricultural Engineering
• Control Systems
• +4 more

Henning Tangen Søgaard is an Associate Professor at the Department of Mechanical and Production Engineering, part of AU Engineering at Aarhus University. He teaches mathematics, numerical methods, and mathematical statistics to BEng students. His research focuses on robotics in agriculture , dynamic modeling , and precision agriculture . Primary Affiliation: Department of Mechanical and Production Engineering, AU Engineering, Aarhus University Research Expertise: Computer vision, control systems for agricultural robotics, environmental modeling (ammonia emissions, spray drift), and wireless sensor networks. His work includes developing autonomous systems for weed control, GPS-based geo-referencing of crops, and mathematical models for fertilizer-related emissions. Publications span peer-reviewed journals and reports in agricultural engineering , robotics , and environmental science . Scientific awards are not mentioned in the provided text. He has no listed PhD students but has collaborated on multiple projects. For direct contact, his email is hts@mpe.au.dk .

• Distributed Systems
• Embedded Systems
• Real-Time Systems
• +3 more

Brian Nielsen is an Associate Professor at Aalborg University's Department of Computer Science, under The Technical Faculty of IT and Design. His research focuses on Distributed Systems, Embedded Systems, Real-Time Systems, Model Checking, IoT Networks, and Autonomous Vehicles. He has been actively involved in projects such as domOS (operating system for smart buildings), FED (Flexible Energy Denmark), and compositional verification of multicore avionics systems. His work emphasizes model-based validation, formal methods, and industrial applications, particularly in safety-critical systems. Recent research highlights include energy-efficient motion planning for autonomous vehicles, comparative analysis of network simulators, and sigfox-based IoT modeling. He has received awards including the Application Coordinator Pool and START funds (both in 2007). Key Projects: domOS, FED, compositional verification of multicore systems Grants: Multiple EU-funded projects (e.g., Horizon Europe) and industry collaborations Awards: Application Coordinator Pool (2007), START funds (2007) His publications span over 80 articles, with a focus on real-time systems, IoT interoperability, and model-driven development. He has advised two PhD students and has been actively involved in organizing international conferences and workshops.

Peter MayerView profile

Assistant Professor

• Cybersecurity
• User-centred Security
• User Authentication
• +9 more

Peter Mayer is an Assistant Professor at the University of Southern Denmark's Department of Mathematics and Computer Science. His research focuses on end-user viable security and privacy solutions, emphasizing usability for diverse audiences, including laypersons, administrators, and developers. He coordinates the Human and Societal Factors research group within the Helmholtz Association's Engineering Secure Systems initiative at Karlsruhe Institute of Technology (KIT), where he is a KASTEL Fellow. Research Areas: User-Centred Security Email and Password Security Security Awareness and Training Cybersecurity in SMEs Phishing Detection and Prevention Key Projects: Cybersecurity and Business Continuity in Danish SMEs (Industriensfond-funded) INSPECTION: Website Hacks in Fake Shops Mapping the Cybersecurity Landscape in Danish SMEs His work bridges technical security mechanisms with human-centric design, addressing challenges like phishing mitigation, password manager adoption, and cross-cultural privacy awareness. He collaborates internationally and publishes extensively in top venues like CHI and CCS.