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Filippo Menczer is a Professor of Informatics and Computer Science and Director of the Center for Complex Networks and Systems Research at Indiana University School of Informatics and Computing. He maintains courtesy appointments in Cognitive Science and Physics, and is affiliated with the Center for Data and Search Informatics and the Biocomplexity Institute. Additionally, he holds a Fellowship at the ISI Foundation in Torino, Italy. His research spans computational analysis of digital ecosystems with emphasis on: Web Science: structural and behavioral analysis of internet-scale systems Social Media Dynamics: information diffusion, meme competition, and attention economy modeling Complex Networks: traffic pattern analysis, popularity dynamics, and social link prediction Publications from 2009-2012 reveal consistent focus on social network analytics and information diffusion mechanisms. Key trends include modeling attention-limited meme competition, bursty popularity patterns in social media, and social link prediction through metadata analysis. His work integrates network science, computational social science, and data mining to decode online behavior. His scientific recognition includes: Fellow of ISI Foundation (2013) He leads the NaN research group within the Center for Complex Networks and Systems Research, focusing on interdisciplinary approaches to complex information networks and social media analytics.

• Web Science
• Social Media
• Social Networks
• +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).

• Natural Language Processing
• Artificial Intelligence
• Linguistic Typology
• +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.

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

Allan Hanbury is a Full Professor for Data Intelligence at the Faculty of Informatics, TU Wien, and a faculty member at the Complexity Science Hub Vienna. He leads the Data Science Research Unit and serves as the Faculty Representative for financial affairs and internationalization. He holds a PhD in Applied Mathematics from Mines ParisTech and a Habilitation in Practical Informatics from TU Wien. PhD in Applied Mathematics, Mines ParisTech, 2002 Habilitation in Practical Informatics, TU Wien, 2008 Bachelor’s and Master’s in Physics and Applied Mathematics, University of Cape Town His research focuses on information retrieval, data mining, natural language processing, and information extraction, with applications in healthcare, legal, and patent domains. He has coordinated major EU projects including Khresmoi, VISCERAL, KConnect, and DoSSIER, the latter training 15 PhD students. He is co-founder of contextflow, a spin-off commercializing radiology search technology. His recent publications (2024–2022) highlight a strong trend in systematic literature review automation, neural re-ranking, large language models, and domain-specific information extraction. Key themes include improving citation screening, patient-trial matching, evaluation metrics, and dataset creation for offensive language and legal text. His work combines technical innovation with real-world impact in medical, legal, and scientific communication contexts. Allan Hanbury has received no explicitly mentioned scientific awards in the provided text. He actively supervises numerous PhD and master’s students and leads large research projects such as DoSSIER, Transparent Automated Content Moderation, and PLFDoc, funded by FWF, WWTF, and EU. His group develops tools for evidence synthesis, clinical data extraction, and legal document analysis. He also contributes to AI and data strategy in Austria and Europe. He leads the Data Science Research Unit at TU Wien and is involved in multiple interdisciplinary projects including BRISE (building regulation analysis), CDL-RecSys, and TACo, focusing on legal and scientific document processing. His work bridges academia and industry through spin-offs like contextflow and collaborations with Deutsche Telekom, Siemens, and FMA.

• Information Retrieval
• Data Mining
• Natural Language Processing
• +13 more

Giovanni Colavizza holds a dual academic appointment as Professor in the Department of Communication at the University of Copenhagen and Associate Professor at the University of Bologna (since November 2023). His research bridges information science, digital humanities, and open science practices. His primary research areas include: Quantitative analysis of open research practices (data/code/preprint sharing) and citation impact Wikipedia's role in scientific knowledge dissemination and news source reliability assessment Digital tools for humanities including knowledge graphs and NLP applications for historical archives Recent publications (2024-2025) reveal strong interdisciplinary trends, combining scientometrics with natural language processing to analyze scientific communication patterns and colonial archival materials. His work appears in leading venues including PLoS ONE, Scientometrics, and IEEE Journal of Biomedical and Health Informatics, demonstrating methodological innovation across information science and humanities domains.

• Open Science
• Scientometrics
• Digital Humanities
• +4 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.

• Query Processing
• Semantic Web
• Linked Data
• +7 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.

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +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.

• Computer Science
• Machine Learning
• Data Science
• +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.

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

Lisa Merete Kristensen is a Research Fellow in the Department of Communication and Humanities at Roskilde University, Denmark, specializing in digital journalism's transformation through datafication and algorithmic systems. Her work critically examines news values, gatekeeping mechanisms, and infrastructure dynamics in contemporary media ecosystems. Education: Master of Arts, University of Southern Denmark Journalist, University of Southern Denmark Research Focus: Kristensen investigates how datafication reshapes news production, emphasizing algorithmic influence on editorial decisions and audience engagement. Her methodology combines scoping literature reviews with comparative platform analysis to map evolving journalistic practices across digital environments, particularly highlighting tensions between traditional news values and platform-driven content distribution. Key Projects: As a core member of the DATAPUBLICS initiative (2020-2024), she explored datafication's societal impact on journalism and audiences. She also contributed to the 'Cultivating student empowerment' teaching project (2022), integrating her media expertise into pedagogical innovation at Roskilde University. Academic Engagement: Kristensen actively participates in international forums including the International Communication Association and Nordmedia conferences, with upcoming presentations on journalism infrastructure scheduled through 2026. Her research directly informs public discourse through media appearances analyzing digital news metrics and journalist-data relationships.

• Digital Journalism
• News Values
• Gatekeeping
• +3 more

Anders Møller is a Professor and Vice Head of Department at the Department of Computer Science, Aarhus University, Denmark. He is a leading researcher in programming languages and software engineering, with a primary focus on static and dynamic program analysis. He serves as Chairman of the PhD Committee and holds leadership roles in the international research community, including Vice-Chair of ACM SIGPLAN and Associate Editor for ACM TOPLAS and ACM TOSEM. His research interests include programming languages, software engineering, static and dynamic analysis, program verification, and security. His work bridges theoretical foundations and practical applications, particularly in improving software reliability and security through advanced analysis techniques. The trends in his recent publications reflect a strong emphasis on static analysis for security, scalability, and real-world impact—especially in web applications, smart contracts, and open-source software supply chains. His research has evolved toward practical deployment, demonstrated by the founding and acquisition of Coana by Socket in 2025 for enhanced vulnerability detection. Recipient of the Danish Elite Research Prize 2020 ACM Distinguished Member He actively mentors students and contributes to the academic community through conference leadership (e.g., OOPSLA, PLDI, ICSE). He also co-authored the widely used textbook Static Program Analysis with Michael I. Schwartzbach. His work is deeply integrated into both academic and industrial advancements in software analysis and security.

• Programming Languages
• Software Engineering
• Static Program Analysis
• +4 more

Petra Hermankova serves as an Assistant Professor in Classical Archaeology at Aarhus University's School of Culture and Society, where she also contributes to the Social Resilience Lab. Her academic appointment is active with ongoing projects through 2025 and recent publications in 2025 confirm her current faculty status. Her research focuses on digital epigraphy and the application of computational methods to ancient inscriptions. She employs text mining, machine learning, and social network analysis to study inscription functions across Graeco-Roman societies, economic history patterns, and labor specialization. Her work emphasizes FAIR and Open Science principles in archaeological data management, particularly through leadership in the Epigraphy.info initiative which develops international standards for digital epigraphy. Hermankova's publication trends reveal increasing engagement with computational approaches to ancient social networks and epigraphic big data , with recent work integrating SPARQL querying, RDF modeling, and network visualization techniques. Her research bridges traditional archaeological methods with cutting-edge digital humanities tools, focusing on Mediterranean communities from Thrace to the Roman Empire. She actively participates in major collaborative projects including The Past Social Networks Project (Carlsberg Foundation, 2022-2025), PPAP: Perachora Peninsula Archaeological Project (2019-2024), and SDAM: Small data - Big Challenges (2019-2023). Hermankova regularly organizes international workshops through Epigraphy.info and teaches practical courses in digital epigraphy methodology.

• Digital Epigraphy
• Greek Epigraphy
• Latin Epigraphy
• +5 more

William Henrich Due serves as a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, within the Machine Learning section. His work intersects with the SCIENCE AI Centre and leverages the department's high-performance compute cluster for research in quantum computing, sustainable AI, and medical applications. Research focuses span quantum machine learning (biomolecular simulations, photonic processors), sustainable AI systems (energy efficiency, climate impact), and clinical applications (EEG analysis, medical imaging). His recent publications reveal strong activity in quantum-classical hybrid systems, with 8/15 recent papers addressing quantum computing challenges. The work emphasizes practical implementations in medical imaging and resource-constrained environments. His research aligns with DIKU's Machine Learning section priorities including medical imaging biomarkers and sustainable computing. Key infrastructure includes TreeSense for remote sensing and the department's dedicated compute cluster. No scientific awards were explicitly documented in the provided materials. Due contributes to DIKU's teaching mission as a Lecturer while engaging with the SCIENCE AI Centre's interdisciplinary initiatives. His work connects with medical imaging applications and quantum computing infrastructure development. Active in the Machine Learning section's research ecosystem, his work intersects with medical imaging analysis and quantum computing applications, utilizing specialized resources like TreeSense for environmental monitoring.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +3 more

Maria Maistro is a Tenure Track Assistant Professor at the Department of Computer Science, Faculty of Science, University of Copenhagen. Her research focuses on Information Retrieval (IR) with emphasis on evaluation, reproducibility, click log analysis, learning to rank, expert search, and machine learning applications to IR. She contributes to the AMAOS project aiming to advance expert search methodologies. PhD in Computer Science (IR) from University of Padua (2017) MSc in Mathematics (probability, stochastic methods) from University of Padua (2014) Maria's research addresses critical challenges in IR evaluation through stochastic modeling and user signal analysis. She investigates fairness-relevance tradeoffs in recommender systems, cross-cultural retrieval frameworks, and explainability techniques for language models and recommendation algorithms. Her work combines theoretical foundations with practical applications in healthcare records, insurance domains, and culinary contexts. Recent publications (2024–2025) demonstrate her expertise in: Retrieval-Augmented Generation (RAG) for cross-cultural adaptation Fairness evaluation in recommender systems EEG-based semantic relevance analysis Session-based recommendation explainability Language model pruning consistency Reproducibility frameworks for IR experiments Maria actively participates in academic activities through conference organization roles, including: Organizer, CENTRE@CLEF 2019 Organizer, CENTRE@NTCIR 2019 Organizer, CENTRE@CLEF 2018 Organizer, LEARNER 2017

• Information Retrieval
• Machine Learning
• Evaluation
• +4 more

Peter Gorm Larsen is Professor in the Department of Electrical and Computer Engineering at Aarhus University, affiliated with the DIGIT Center. With over 300 research outputs since 1989, his work centers on digital twins, cyber-physical systems, and embedded AI. Current projects include RoboSAPIENS (robotic adaptation), CP-SENS (cyber-physical sensing), and DIGIT-BENCH (digital twins for wind industry test benches). Research focuses on developing reusable digital twin platforms and autonomous reconfiguration frameworks. Recent publications demonstrate advancements in semantic interoperability for coupled digital twins and service-oriented architectures. Since 2018, Professor Larsen has received international recognition including the Reviewer Recognition award. His work spans robotics, sustainable energy systems, and healthcare applications. Professor Larsen maintains active collaborations across Europe and serves on multiple program committees. Teaching activities include Discrete Mathematics and supervision of graduate students in software engineering and embedded systems.

• Digital Twins
• Cyber-Physical Systems
• Robotics
• +2 more