جست‌وجوی استاد و پژوهشگر

Thomas Asmus Pedersenمشاهده پروفایل

دانشیار

• Circular Economy
• Sustainable Construction
• Building Materials
• +۵ مورد دیگر

Thomas Asmus Pedersen is an Associate Professor and Vice Head of Section at the Department of Technology and Innovation, Faculty of Civil and Architectural Engineering, University of Southern Denmark (SDU). His work bridges engineering and sustainability, with a strong focus on circular economy principles in the construction sector. His research interests lie at the intersection of sustainable construction, circular economy, and building material reuse. He investigates how second-hand building materials can be integrated into modern construction practices to reduce waste and mitigate climate change. His work often engages with policy, organizational structures, and entrepreneurial initiatives in Europe and the Western world. The most recent publication highlights a critical examination of platforms for second-hand building materials, analyzing their potential as hubs in the circular economy versus being speculative ventures by entrepreneurs. The research reflects interdisciplinary engagement across engineering, environmental science, and socio-economic dimensions. Member, Danmarks Akkrediteringsinstitution (2014–present) He is actively involved in teaching and supervision, including student practicum (B-PTE6) and final project guidance (Afgangsprojektvejledning). He also contributes to EU-level research through participation in the EUROFusion BB RH facility project (2024–2026). His collaborative network spans national and international institutions, with a focus on sustainable construction and resource efficiency. He works closely with colleagues on research, supervision, and large-scale technical projects.

• Social Data Science
• Network Analysis
• Generative AI
• +۳ مورد دیگر

Johanna Einsiedler is a Researcher at the Copenhagen Center for Social Data Science (SODAS) and an enrolled PhD student at the University of Copenhagen . Her research focuses on social data science , network analysis , and the economic implications of generative AI , particularly its influence on digital labor markets and freelancing . She has contributed to studies analyzing temporal dynamics of large-scale social networks and AI-driven shifts in freelance demand . Her work spans interdisciplinary domains including computational social science , economic behavior , and technological impact assessment . She can be contacted at johanna.einsiedler@sodas.ku.dk or via her office at Øster Farimagsgade 5, building 1, 2nd floor, Copenhagen K.

• Machine Learning
• Quantum Computing
• Medical Data Analysis
• +۴ مورد دیگر

Jonas Vinther is a Research Fellow at the Department of Computer Science , University of Copenhagen, specializing in Machine Learning and its intersections with quantum computing, medical data analysis, and sustainability. He is also an external PhD student in the Quantum Information Science & Technology program at the Niels Bohr Institute. Email: jonas.vinther@nbi.ku.dk , jonas.vinther@di.ku.dk Location: Universitetsparken 1, 2100 København Ø His research spans quantum machine learning , AI ethics , medical imaging , and environmentally sustainable AI , with recent publications on topics ranging from quantum neural networks to fairness in recommender systems . He contributes to the SCIENCE AI Centre and collaborates on initiatives like TreeSense for global tree resource monitoring.

Anna Rogersمشاهده پروفایل

دانشیار

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Anna Rogers is an Associate Professor of Data Science at the IT-University of Copenhagen , affiliated with the NLPnorth research group. Her work focuses on Natural Language Processing (NLP) , Artificial Intelligence , and Large Language Models (LLMs) , with a particular emphasis on ethical data use, peer review innovation, and transformer model analysis. She leads projects addressing AI transparency, medical QA hallucinations, and generative AI applications. Her research explores topics including: LLM behavior and evaluation Data governance in NLP Peer review systems optimization Transformer model robustness Medical AI applications Key Projects : PlagAIrism : Tracking LLM training data origins Pioneer Centre for AI : Pre-registered replication studies TinyGPT : Efficient NLP models AIInterviewer : Large-scale qualitative data collection Publications span ACL , EMNLP , and specialized NLP workshops, addressing topics from BERT analysis to AI content farms.

• Human-Computer Interaction
• Machine Learning
• Natural Language Processing
• +۱ مورد دیگر

Sarah Frances Homewood is an Assistant Professor (Tenure Track) in the Department of Computer Science at the University of Copenhagen, affiliated with the Human-Centred Computing research section. Her research focuses on the intersection of human-computer interaction and artificial intelligence, with applications in healthcare, natural language processing, and interpretable machine learning. Her diverse research interests span Human-Computer Interaction, Machine Learning, Natural Language Processing, and Artificial Intelligence. Recent investigations include interpretability of large language models, clinical NLP applications, fairness in recommender systems, and quantum natural language processing. Analysis of her recent publications reveals strong emphasis on NLP interpretability techniques, healthcare applications of AI, and theoretical foundations of machine learning. Her work frequently bridges fundamental computer science with practical applications in medicine and human-centered systems. Emerging research directions include quantum NLP and protein sequence modeling. Dr. Homewood's research contributes to the Machine Learning Section's focus on both theoretical foundations and applied domains including medical data analysis and information retrieval.

Markus Strohmaierمشاهده پروفایل

استاد

• Computational Social Science
• Data Science
• Machine Learning
• +۷ مورد دیگر

Markus Strohmaier is Professor and Chair of Data Science in the Economic and Social Sciences at the University of Mannheim, with affiliations as Scientific Coordinator at GESIS – Leibniz Institute for the Social Sciences and External Faculty Member at the Complexity Science Hub Vienna. His interdisciplinary work bridges computer science, economics, and the social sciences. University of Mannheim – Chair for Data Science in the Economic and Social Sciences GESIS – Scientific Coordinator for Digital Behavioral Data Complexity Science Hub Vienna – External Faculty Former Professor at RWTH Aachen University and University of Koblenz-Landau Previous Post-Doc and Visiting Roles at Stanford University, Xerox PARC, University of Toronto, and Graz University of Technology His research focuses on computational social science , algorithmic fairness , network science , and the modeling of human behavior using machine learning and large-scale data. He develops methods to analyze textual, relational, and emerging data types to understand socioeconomic systems and digital societies. The recent articles reflect a strong trend in studying inequality in algorithmic systems , governance in decentralized organizations (DAOs) , and psychological profiling of AI . His work spans high-impact journals like Nature and Scientific Reports , emphasizing fairness, transparency, and societal impact of data-driven technologies. Notable scientific contributions include: Editor-in-Chief of EPJ Data Science (2018–2022) Founding co-chair of the Computational Social Science section of the German Informatics Society He advises students and leads research projects on algorithmic fairness, digital governance, and behavioral modeling. His team engages in both fundamental methodological development and applied studies in real-world digital platforms. He has been involved in significant grants and collaborative initiatives around digital behavioral data and computational social science infrastructure. His lab and projects include the Algorithmic Fairness initiative and the interactive visualization tool Planets of Disparity , which explores how algorithms behave on different network structures. These efforts aim to enhance public understanding and technical scrutiny of algorithmic systems.

Mahdi Abkarمشاهده پروفایل

دانشیار

• Fluid Mechanics
• Turbulence
• Data-driven Modeling
• +۵ مورد دیگر

Mahdi Abkar is an Associate Professor at the Department of Mechanical and Production Engineering, Aarhus University, leading the Fluids and Energy section. His research focuses on fluid mechanics, turbulence modeling, and wind energy optimization using computational fluid dynamics (CFD) and machine learning techniques. Research Areas: Fluid Mechanics, Turbulence, Wind Energy, Data-Driven Modeling Affiliation: Aarhus University, College of Engineering His recent work emphasizes turbulence modeling, LES/RANS simulation improvements, and wind farm power prediction. Articles highlight data-augmented models, flow separation analysis, and drag reduction strategies. He contributes to CFD applications in renewable energy systems, including ultrasonic flow meter design and atmospheric boundary layer interactions.

Mohammad Naser Sabet Jahromiمشاهده پروفایل

استادیار

• Explainable Artificial Intelligence
• Biometrics
• Machine Learning
• +۴ مورد دیگر

Mohammad Naser Sabet Jahromi is an Assistant Professor at the Department of Architecture, Design and Media Technology, Aalborg University, Denmark. He is affiliated with the Visual Analysis and Perception Centre for AI Ethics, Law and Policy. His research focuses on explainable AI (XAI), biometrics, machine learning, and ethical AI applications in legal and medical domains. He actively participates in interdisciplinary projects like REPAI: Responsible AI for Value Creation (2023-2027), which explores AI ethics, computational discourse analysis, and value-driven AI systems. His educational background is not explicitly detailed in the provided text, but his research trajectory indicates strong expertise in computer science and AI systems. Key research interests include interpretable machine learning models, privacy-preserving biometric systems, and AI applications in asylum adjudication and educational assessment. Recent work emphasizes developing XAI frameworks like SIDU-TXT for NLP, verifying machine unlearning mechanisms, and automating large-classroom assessments. His projects bridge technical AI advancements with societal implications through collaborations with legal and ethical scholars. Notable contributions include datasets evaluating XAI methods in medicine and methodologies for transparent AI decision-making. He has participated in conferences such as ICPR 2024 and JURISIN 2023, showcasing interdisciplinary research impact.

• Greenland
• Climate
• Climate change
• +۵ مورد دیگر

Sune Olander Rasmussen is an Associate Professor at the Niels Bohr Institute , University of Copenhagen , specializing in Ice, Climate and Geophysics . With a background in geophysics, mathematics, and complex systems dynamics, his work focuses on integrating large ice core datasets and developing tools for climate data analysis. Research Interests : Greenland ice core studies, climate change, Dansgaard-Oeschger events, volcanic eruption impacts on climate, firn seismic properties, and probabilistic age modeling. Scientific Contributions : Key developer of the GICC05 time scale, with significant publications in Climate of the Past , Nature Communications , and PNAS . Awards : Carlsberg Foundation Distinguished Associate Professor. Leadership Roles : Chair of the NEEM dating consortium (since 2008) and the INTIMATE research network (since 2011).

• Epidemiological methods
• Network science
• Global Health
• +۷ مورد دیگر

Dr. Diego Bassani is an Associate Professor of Epidemiology at the Dalla Lana School of Public Health and the Department of Pediatrics at the University of Toronto. He holds concurrent appointments as a Senior Scientist at The Hospital for Sick Children and Director of the International Program Evaluation Unit at the Centre for Global Child Health. His interdisciplinary work bridges epidemiology and network science to advance population health measurement. M.Sc., Ph.D. (Epidemiology) His research emphasizes large-scale epidemiological studies, network science applications, and methodological innovations in maternal and child health. Current projects include reconstructing population networks from national survey data, developing novel sampling designs, and enhancing timeliness in public health surveillance. Recent work explores allometric scaling, microbiota associations, and neonatal probiotic interventions. Key trends in his recent publications include network-based population health modeling (SEEDNet), microbiota analysis in Bangladeshi infants, and methodological advancements in cross-country health data synthesis. These studies span epidemiology, microbiology, and computational approaches. Dr. Bassani leads projects funded by the New Frontiers in Research Fund, NSERC, CIHR, Bill & Melinda Gates Foundation, and Global Affairs Canada. His methodological focus on network science aims to revolutionize health data collection and disease pattern detection. He collaborates with the ISI Foundation and contributes to open-source data infrastructure through initiatives like SEEDNet, supporting global health research and policy formulation.

Tue Herlauمشاهده پروفایل

دانشیار

• Bayesian Methods
• Complex Networks
• Brain Imaging Data Analysis
• +۳ مورد دیگر

Tue Herlau is an Associate Professor at the Department of Applied Mathematics and Computer Science, Cognitive Systems, within the College of Engineering at Technical University of Denmark. His research focuses on modeling complex networks using Bayesian methods, particularly applied to brain imaging data. He explores multiscale structures in networks (ranging from individual nodes to large-scale temporal dynamics) and causal inference in social and biological systems. Education : Bachelor in Physics and Mathematics (University of Copenhagen), Masters in Informatics (DTU) Recent publications highlight work on generative AI for educational psychology, causal probability trees, Bayesian neural network training, and moral reinforcement learning. Key collaborations span institutions in Denmark and international partners in AI and neuroscience research. He supervises students in probabilistic methods and machine learning applications, including a 2019-2020 Master project on prognostics for carbon/epoxy composites. His network shows strong engagement with computational science, relational databases, and formal concept analysis.

Christopher P. Kempesمشاهده پروفایل

استاد

• Complexity Science
• Physical Biology
• Astrobiology
• +۷ مورد دیگر

Christopher P. Kempes is a Professor at the Santa Fe Institute and an External Professor at the Complexity Science Hub Vienna, where he conducts interdisciplinary research at the intersection of biology, physics, and complex systems. His work is focused on uncovering universal principles governing life across scales—from cells to cities. Research Interests: Dr. Kempes explores the fundamental laws underlying biological organization, the origins of life, ecological dynamics, and the complexity of human systems. His approach integrates physical constraints, mathematical modeling, and data-driven analysis to predict large-scale patterns in nature and society. Key themes include scaling laws, metabolic theory, astrobiology, urban systems, and evolutionary transitions. Publication Trends: Over the past 15 years, his research has consistently advanced theoretical frameworks in complexity science. His publications reveal a strong focus on universal patterns in biology and society, with recurring themes in scaling, thermodynamics, and system-level organization. He applies these principles to diverse domains including climate change, inequality, biosignatures, and organizational growth. Scientific Recognition: Work featured in BBC, Wired, and other major media outlets. Contributions to foundational theories in complexity and biological organization. Advising and Grants: While specific students and grants are not listed, his senior faculty role at leading complexity research institutes suggests active mentorship and leadership in funded interdisciplinary projects. He likely advises postdoctoral researchers and collaborates on large-scale initiatives in complexity science, astrobiology, and urban sustainability. Labs and Research Groups: Dr. Kempes is affiliated with the Santa Fe Institute’s core faculty, participating in its collaborative, theory-driven research environment. He also contributes to the Complexity Science Hub Vienna, engaging in cross-institutional projects on complex systems in natural and social domains.

János Kertészمشاهده پروفایل

استاد

• Network Science
• Computational Social Science
• Econophysics
• +۷ مورد دیگر

János Kertész is a Professor and Head of the Department of Network and Data Science at the Central European University (CEU), currently based in Vienna. He is also an elected member of the Hungarian Academy of Sciences. Previously, he held professorial and research positions at the Budapest University of Technology and Economics and the Hungarian Academy of Sciences, where he served as Director of the Institute of Physics. His research lies at the intersection of statistical physics and social sciences, with major contributions to network science, econophysics, computational social science, and algorithmic fairness. He investigates systemic risks in economic networks, opinion dynamics in algorithmically biased environments, poverty mapping using big data, and the role of automation in online extremism. The most recent articles highlight a strong trend in applying complex systems methodologies to real-world socioeconomic problems—particularly through large-scale network analysis, multimodal data integration, and policy-relevant modeling in inequality, supply chains, and digital fairness. His collaborative work spans institutions like the Complexity Science Hub (CSH) in Vienna. Elected member of the Hungarian Academy of Sciences He has advised numerous researchers and contributed to major interdisciplinary grants, particularly in complexity science and data-driven social science. His leadership in organizing NetSci 2023, the largest network science conference, underscores his role in fostering global scientific collaboration. He is central to projects involving high-resolution poverty mapping, economic systemic risk, and ethical AI applications. He is actively involved with the Complexity Science Hub (CSH), where CEU is a partner institution, and contributes to interdisciplinary teams working on economic modeling, social network analysis, and crisis preparedness using granular supply chain and VAT data.

Ivan Smirnovمشاهده پروفایل

استادیار

• Computational Social Science
• Generative AI
• Artificial Intelligence
• +۱۰ مورد دیگر

Ivan Smirnov is a computational social scientist currently serving as the AI Lead in Research and Researcher Training at the Graduate Research School, University of Technology Sydney (UTS). He is also an External Faculty Member at the Complexity Science Hub in Vienna. His work spans the intersection of AI, sociology, and education, with a focus on Generative AI's role in research and doctoral training. University: University of Technology Sydney School: Graduate Research School Previous Positions: Assistant Professor, University of Mannheim; Group Leader, Higher School of Economics, Moscow Smirnov’s research explores how digital traces and machine learning can illuminate social dynamics such as inequality, gender bias, and academic performance. He employs computational methods to study topics like online toxicity, student wellbeing, and the digital representation of gender. His methodological expertise includes natural language processing, social network analysis, and predictive modeling using social media data. His recent publications reveal a consistent trend in using large-scale digital data to address pressing social questions, particularly in education and mental health. He frequently publishes in leading interdisciplinary journals such as Proceedings of the National Academy of Sciences , EPJ Data Science , and Royal Society Open Science , and presents at major conferences including IC2S2 and ICWSM. His scientific contributions have been recognized through media coverage in Nature , MIT Technology Review , and The Times , as well as a Best Course Award at HSE for his pioneering teaching in computational social science. Best Course Award at Higher School of Economics Media features in Nature, MIT Technology Review, The Times, ABC TV Smirnov is actively involved in research leadership and training, having led AI initiatives at UTS and co-organized the Summer Institute in Computational Social Science. He has secured funding from the European Commission and the Russian Science Foundation. He supervises research students and has developed the open educational course Getting Started with Generative AI in Research , reflecting his commitment to empowering the next generation of researchers. He also contributes to the academic community through peer review for top conferences and journals. He is affiliated with professional groups including the Human-AI Collaborative Knowledgebase for Education and Research (HACKER) and the SDG Classification Expert Group, and leads initiatives focused on integrating AI into research training and supporting HDR student wellbeing.

• Wind Energy Systems
• Renewable Energy
• Energy Systems Engineering
• +۴ مورد دیگر

Matti Juhani Koivisto is a Senior Researcher at the Department of Wind and Energy Systems at the Technical University of Denmark (DTU). His work focuses on renewable energy systems integration, particularly wind power modeling and analysis within large-scale energy systems. His research expertise spans several critical domains in renewable energy: Wind Energy Systems Engineering (100%) Wind Power Engineering (89%) Variable Renewable Energy Integration (80%) Power Engineering (56%) Time Series Analysis in Energy Systems (37%) Energy Engineering (35%) Dr. Koivisto's research addresses fundamental challenges in renewable energy integration, with particular emphasis on wind power forecasting, variability analysis, and the impacts of climate change on renewable resources. His work bridges theoretical modeling with practical energy system applications, contributing to more reliable and efficient integration of renewable energy sources into power grids. His recent publications demonstrate a strong focus on offshore wind energy, hybrid power plants, and advanced modeling techniques for wind farm generation. His work on increasing the resolution of solar and wind time series has been particularly influential in energy system modeling, addressing critical data requirements for high renewable penetration scenarios. Dr. Koivisto contributes significantly to UN Sustainable Development Goals related to affordable and clean energy (SDG 7) and climate action (SDG 13), with research directly addressing the technical challenges of transitioning to sustainable energy systems. He is actively involved in several major international research projects including AWETRAIN (Airborne Wind Energy Training), DTWO (Federated Digital Twins for Wind-Offshore), and GREAT (GRid Enhancement for Ancillaries in Tomorrow's power systems), where he serves as both project participant and PhD supervisor.