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Finn Årup NielsenView profile

Associate Professor

• Neuroinformatics
• Text Mining
• Data Mining
• +8 more

Finn Årup Nielsen is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the Cognitive Systems department under Applied Mathematics and Computer Science. His career spans neuroinformatics, text mining, and social media analysis, with a focus on integrating neuroscience and computer science. Education : Master in Engineering (1993-1996), Technical University of Denmark Research Interests : Neuroinformatics: Developing software and databases for brain imaging data and neuroscience text analysis Social Media Analysis: Mining Twitter and Wikipedia for citation patterns, sentiment, and topic trends Knowledge Graphs: Wikidata integration, lexeme alignment, and semantic representation Environmental Informatics: Applying AI to sustainable assessment through projects like DreamsKG Project Supervision : Supervised PhD projects on federated learning, neural network explainability, and big data analytics at DTU Contributed to foundational neuroimaging research in the Human Brain Project since 1994

Carolina Barra QuagliaView profile

Associate Professor

• Bioinformatics
• Protein Immunoinformatics
• Immunogenicity
• +5 more

Carolina Barra Quaglia is an Associate Professor at the Department of Health Technology , Technical University of Denmark. Her research focuses on bioinformatics applications in immunology, particularly in antigen presentation and T-cell recognition. She specializes in immunopeptidomics, epitope prediction, and machine learning applications for immunotherapy. Active in 6 ongoing PhD projects Supervisor role in multiple AI-driven biomedical research initiatives Key expertise in HLA ligandome analysis and cancer antigen discovery Research Trends : Recent publications emphasize: Machine learning for immunogenicity prediction (IMPROVE framework) Novel methods for non-canonical antigen discovery (MARS algorithm) HLA-DR/DQ immunopeptidome characterization Peptide-MHC interaction modeling Applications in cancer immunotherapy and autoimmune disease research Integration of mass spectrometry with computational biology Supervision: Currently supervising multiple PhD students in projects spanning AI applications for molecular binders, plant-based starter cultures, and autoimmunity research.

Corrado GioanniniView profile

Researcher

• Computational Epidemiology
• Epidemic Modeling
• Complex Networks
• +4 more

Corrado Gioannini is a Senior Research Scientist at ISI Foundation in Turin, Italy, specializing in computational epidemiology and epidemic forecasting systems. With a physics background, he bridges theoretical modeling and public health applications through software development. His research focuses on computational epidemiology , complex networks , and data-driven forecasting . Key interests include global disease spread modeling, real-time surveillance systems, and visualization tools for public health decision-making. He designs software frameworks like GLEAMviz to translate complex epidemic dynamics into actionable insights for researchers and policymakers. Gioannini's publication record (2010-2025) shows consistent contributions to epidemic modeling, particularly in influenza and COVID-19. His work combines mobility data, network science, and computational tools to analyze intervention strategies, with high-impact publications in Nature and Science demonstrating practical applications for pandemic response. He secures significant funding through EU projects including STAMINA, Epydemix, Influcast, and RespiCast. These initiatives develop open-source forecasting systems for respiratory diseases and pandemic management, emphasizing technical coordination across national and European health agencies. Gioannini leads the GLEAM Project team responsible for the GLEAMviz simulator, an open platform used globally for epidemic scenario testing. His work prioritizes practical software solutions that enable real-time modeling of disease spread through intuitive interfaces and robust computational backends.

Keita TokudaView profile

Associate Professor

• Machine Learning
• Alzheimer's Disease
• Biomarkers
• +7 more

Keita Tokuda is an Associate Professor at the Faculty of Health Data Science, Juntendo University , specializing in the intersection of machine learning, nonlinear dynamics, and neuroscience. His research focuses on leveraging chaotic systems and reservoir computing for modeling biological processes like hippocampal activity and cerebellar computation, with applications in Alzheimer's disease biomarker discovery. Research Keywords : Machine Learning, Alzheimer's Disease, Biomarkers, Neural Networks, Dynamical Systems, Chaos Theory, Cerebellum, Hippocampus, Applied Mathematics. Scientific Awards : 2022 Japanese Neural Network Society Paper Award 2012 Tokyo University Life Science Symposium Poster Award 2024-2029: JSPS Grant-in-Aid for Scientific Research (Transformative AI in Healthcare) 2023-2028: JSPS Grant for Multicellular Neural Dynamics 2023-2026: JSPS Grant on Chaotic Neural Networks 2022-2025: JST Moonshot Project on Disaster Prevention 2020-2022: JSPS Grant for Cerebellar Reservoir Computing

Panagiotis KarrasView profile

Professor

• Data Management Systems
• Graph Algorithms
• Data Mining
• +3 more

Panagiotis Karras is a Professor at the Department of Computer Science , University of Copenhagen , with a faculty affiliate status at Aarhus University . His research focuses on robust and versatile methods for data access, mining, and representation. Education: MSc in Electrical and Computer Engineering from National Technical University of Athens , PhD in Computer Science from University of Hong Kong Research Interests: Spanning data management systems, graph algorithms, and privacy-preserving data publishing, his work addresses challenges in efficient query processing, influence propagation in networks, and semantic-aware data compression. Notable contributions include adaptive indexing techniques, graph alignment frameworks, and algorithms for fair stable marriage problems. Recent Publications (2024-2025): Focus on graph alignment, subgraph matching, and influence maximization, with applications to knowledge graphs, social networks, and time-series data analytics. Scientific Recognition: 2008 Young Scientist Award from the Hong Kong Institute of Science Keynote invitations at ACM SIGKDD and IEEE ICDE Professor Karras actively seeks motivated PhD students to collaborate on these research directions. His lab maintains a powerful compute cluster and modern facilities for data-intensive research.

• Machine Learning
• Natural Language Processing
• Medical Data Analysis
• +10 more

Tobias Nordholm-Højskov is an Instructor at the Department of Computer Science , University of Copenhagen (DIKU). His research intersects machine learning with healthcare, sustainability, and quantum computing, focusing on theoretical foundations and applications in medical data analysis, climate-aware AI, and quantum systems. He is affiliated with the SCIENCE AI Centre and contributes to projects like QDarts (quantum dot array simulation) and TreeSense (remote sensing for environmental monitoring). His work spans diverse subfields, including Explainable AI for healthcare records Federated Learning in rare disease research Quantum-inspired neural networks Retrieval-Augmented Generation frameworks Environmental impact mitigation in AI

Ana Alina TudoranView profile

Associate Professor

• Machine Learning
• Data Mining
• Behavior Analysis
• +7 more

Ana Alina Tudoran is an Associate Professor at Aarhus University, specializing in quantitative methods within business intelligence. Her academic work bridges machine learning, data mining, and consumer behavior analysis. Primary affiliation: Department of Economics Research focus areas: Artificial Intelligence, Customer Lifetime Value modeling, and Hybrid Intelligence systems Notable projects include pandemic-era consumer behavior studies and IoT privacy reviews Her recent publications span supply chain optimization, organizational text analytics, and responsible AI deployment. Tudoran actively contributes to methodological advancements through hybrid PLS-SEM/machine learning frameworks and is a frequent speaker at management conferences.

Rongling LiView profile

Associate Professor

• Smart Cities
• Smart Buildings
• Energy Resilience
• +5 more

Rongling Li is an Associate Professor at the Department of Civil and Mechanical Engineering, Technical University of Denmark (DTU). His research focuses on smart energy systems, smart cities, and building energy flexibility, emphasizing modeling and data-driven approaches. He leads projects like SEEDS and IEA EBC Annex 82, contributing to resilient low-carbon energy systems. He has supervised seven PhD graduates and currently advises two students on energy flexibility topics. Key affiliations include chairing the IEA EBC Annex 82 and serving on the Built4People board under Horizon Europe. His expertise spans energy resilience, building physics, and machine learning applications. Notable awards include the 2019 Best Presentation Award and a 2021 research fellowship at the University of Tokyo. His research portfolio includes 55 publications across journals like Applied Energy and Energy and Buildings. Current projects involve data-driven control for smart energy systems and energy management in sports facilities. He actively reviews PhD theses at institutions such as UC Dublin and Chalmers University.

• Network Analysis
• Social Networks
• Data Mining
• +6 more

Michele Coscia is an Associate Professor in the Department of Data Science at the IT University of Copenhagen. He also serves as the Head of Programme for the BSc in Data Science. His research focuses on network analysis, social networks, data mining, and their applications in understanding human mobility, complex systems, economic development, and memetics. He has contributed to projects such as the Pioneer Centre for Artificial Intelligence and leads initiatives like ROMNET (Past social network reconstruction from material culture data) and Work2Vec (exploring deep learning in healthcare and work hours analysis). His research interests encompass interdisciplinary network science, including misinformation dynamics, ideological polarization, and the impact of social media systems. He has developed methodologies like the generalized Euclidean measure for multilayer networks and explored cultural data analytics through case studies like Italian music history. His work also addresses real-world challenges, such as quantifying the effects of violence on migration patterns and optimizing network sampling strategies for cost efficiency. Michele Coscia has received the Årets Forskningsmiljø 2022 award, recognizing his contributions to fostering outstanding research environments. He actively engages with media, discussing topics like misinformation and data visualization. As a principal investigator, he oversees projects funded by institutions such as the Villum Foundation and Danish National Research Foundation. His research spans academic collaborations across countries, reflecting his global network and commitment to advancing network theory and its applications.

Hua LuView profile

Professor

• Computer Science
• Data Mining
• Data Science
• +4 more

Hua Lu is a Professor in the Department of People and Technology Programming, Logic and Intelligent Systems at Roskilde University in Denmark. His research focuses on computer science with specialties in database systems, data mining, spatial data processing, and AI/ML applications for databases. He leads projects on AI-powered spatial databases and data-driven systems. His research interests span multiple domains including data science fundamentals, IoT data streams, location-based services, and database optimization techniques. Recent publications demonstrate strong focus on spatial-temporal data processing, machine learning integration with databases, and practical applications of data science. Professor Lu has received multiple scientific awards including Best Paper nominations and runner-up awards at premier conferences, recognizing his contributions to database and mobile computing research.

Pep CharusantiView profile

Researcher

• Microbiology
• Bioinformatics
• Natural Product Discovery
• +5 more

Pep Charusanti is a Senior Researcher at the Natural Products Genome Mining section of the Novo Nordisk Foundation Center for Biosustainability at the Technical University of Denmark (DTU). His work focuses on genomic analysis of microorganisms, particularly Actinomycetes, to discover novel natural products with pharmaceutical applications. He is actively involved in the DTU Microbes Initiative and maintains an ORCID profile (0000-0003-0009-6615). His research spans several critical areas in modern microbiology and bioinformatics: Genomic mining of Actinobacteria for natural product discovery Analysis of biosynthetic gene clusters in Streptomyces and related organisms Development of computational approaches for secondary metabolite prediction Integration of informatics and metabolic engineering for antibiotic discovery Understanding transcriptional regulation in bacterial production hosts Charusanti's publication record shows a strong focus on leveraging genomic data to uncover new natural products, particularly through pangenome analysis of Actinomycetes. His recent work combines machine learning approaches with traditional microbiological techniques to identify and characterize novel biosynthetic pathways. The research has significant implications for antibiotic discovery and development, contributing to UN Sustainable Development Goals related to health and wellbeing. As a main supervisor for PhD projects, Charusanti mentors the next generation of researchers in natural product discovery. He has been involved in significant research grants, including the 'iimena' project funded by the Novo Nordisk Foundation (2017-2023), which focused on Integration of Informatics and Metabolic Engineering for the discovery of Novel Antibiotics, and currently leads a PhD project on Data-driven investigation of mechanisms that underlie secondary metabolite biosynthesis in Actinomycetes (2023-2026). Charusanti is actively involved in the Natural Products Genome Mining team at DTU Biosustain, which leverages extensive genomic datasets and computational tools to accelerate the discovery of bioactive compounds from microbial sources. His research has received media attention, including features in 'I Am Science' (2019) and coverage of work on antibiotic resistance and antimicrobial agents.

Magnus BenderView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +7 more

Magnus Bender is an Assistant Professor at the Department of Programming, Logic and Intelligent Systems , part of the Department of People and Technology at Roskilde University (DK-4000 Roskilde, Denmark). He holds a Dr. rer. nat. (2024), M.Sc. (2021), and B.Sc. (2019) in Computer Science from Universität zu Lübeck. His research focuses on Artificial Intelligence in the Humanities and Law , including transformer models (BERT/GPT), text mining, legal text analysis, and ethics in AI. Recent work includes projects like DiRec: Safeguarding Diversity in News Recommendation (2022–2027) and organizing workshops such as Workshop on Humanities-Centred Artificial Intelligence (2025). He received the Best Paper Award (IEEE ICSC 2024) for work on human feedback in AI systems. Education: Dr. rer. nat. in Computer Science, Universität zu Lübeck (2024) M.Sc. in Computer Science, Universität zu Lübeck (2021) B.Sc. in Computer Science, Universität zu Lübeck (2019) External Positions: Research Associate (Postdoc) at University of Hamburg (2024–2025) Research Assistant at Universität zu Lübeck (2021–2024) His research integrates technical AI advancements with societal applications, emphasizing ethics and human-centered approaches. He actively contributes to interdisciplinary projects bridging computer science, law, and humanities.

• Data systems
• Knowledge graphs
• Large-scale data analysis
• +4 more

Zoi Kaoudi is an Associate Professor at the IT University of Copenhagen, affiliated with the Data, Systems, and Robotics school and the Data-intensive Systems and Applications department. Her research focuses on advancing data systems, knowledge graphs, and large-scale data analysis. She leads the Rank4QO project (2024–2027), funded by the Carlsberg Foundation, which explores query optimization using ranking algorithms. Her work emphasizes machine learning integration in data management systems, including frameworks like Apache Wayang, which unifies diverse data analytics platforms. Key collaborations include projects with Volkswagen Group and SAP, addressing dynamic graph processing and knowledge graph embeddings. Notable contributions include innovative approaches to parameter management (e.g., Good Intentions ), automated data science pipelines ( DORIAN ), and efficient graph processing algorithms. Her recent publications (2023–2025) highlight advancements in machine learning systems, distributed data processing, and adaptive optimization techniques. Current projects aim to bridge machine learning and data management through frameworks like Wayang and Dorian, with applications in air cargo revenue management and semantic web systems.

• Data Science
• Network Science
• Computational Social Science
• +7 more

Vedran Sekara is an Associate Professor in Data Science at the IT University of Copenhagen, focusing on the intersection of Network Science, Computational Social Science, and Algorithmic Auditing. His work examines how data-driven technologies impact societal systems, with particular attention to biases in algorithmic decision-making and humanitarian applications of machine learning. Research Interests: Algorithmic fairness in social systems Human mobility modeling and pandemic response Socioeconomic indicator estimation via digital footprints Climate change communication strategies Machine learning for humanitarian work Recent Projects: Principal Investigator for DIREC: Digital Research Centre Denmark (2020-2025) Lead investigator in DIREC Mobility Analysis Project (2022-2023) Awards: Recipient of Årets Forskningsmiljø 2022 Award for collaborative research excellence Media Engagement: Frequently discusses algorithmic impacts on Danish media including PRM/Hvordan påvirker algoritmer vores samfund? (April 2021) and Nu vil regeringen og opruste mod cyberangreb (June 2021). Key Collaboration Areas: Extensive international research networks in mobility science, computational social science, and algorithmic auditing.

• Data Science
• Computational Social Science
• Climate Change Communication
• +4 more

Luigi Arminio is a Research Fellow at the Data Science Center for Climate IT within the IT University of Copenhagen . His work bridges data science and computational social science, focusing on the emotional and ideological dynamics of online climate debates. Active in analyzing polarized discourse, visual narratives, and sociolinguistic patterns related to climate change Current research explores emotional reactions to visual content, polarization mechanisms on Facebook, and embeddings-based ideological profiling Collaborates with Principal Investigator Lorenzo Rossi on the PolarVis project funded by the Independent Research Fund Denmark (2022-2025) Using advanced data analysis and machine learning techniques, he investigates how climate change communication is shaped across digital platforms. His methodologies span: Polarization detection in social networks Emotion and sentiment mapping Visual content analysis Embeddings and semantic modeling His research contributes to understanding the intersection of climate change communication and social media dynamics, with a particular focus on: Visual narratives in online debates Algorithmic amplification of ideological positions Interpretability of image-to-text mappings Arminio actively collaborates with researchers in climatic change and digital humanities, as evidenced by his network activity showing 100% overlap in climate change and computational social science domains.