جست‌وجوی استاد و پژوهشگر

Tomer Sagiمشاهده پروفایل

دانشیار

• Data Integration
• Artificial Intelligence
• Computer Science
• +۳ مورد دیگر

Tomer Sagi is an Associate Professor in the Department of Computer Science at Aalborg University (AAU), Denmark. He is affiliated with The Technical Faculty of IT and Design and leads projects in the AI for the People and BLUE – Marine & Maritime Research groups. His research focuses on data integration, ontology engineering, artificial intelligence applications in healthcare and environmental science, and knowledge graph development. PhD in Information Systems from Technion-Israel Institute of Technology (2015) Former Lecturer at University of Haifa (2017–2022) Principal Investigator/Co-PI in projects like ODINI (AI-based Data Integration), MEHDIE (Middle Eastern Heritage Knowledge Graph), and DarkScience (Microbial Data Science) Research Interests: Data Integration, AI for Ocean Science, Medical Informatics, Ontology Evaluation, Multilingual Knowledge Systems, and Explainable AI. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure. Key Projects (2022–2025): DarkScience: Metagenomic data analysis funded by Villum Foundation ODINI: AI-driven ocean data fusion and 3D reconstruction MEHDIE: Multilingual historical knowledge graphs for Middle Eastern heritage Awards: Received NLP4KGC Best Paper Award (2023) and AIME 2020 Best Paper Nomination. His contributions span 46+ publications, 8 datasets, and media coverage on AI applications in healthcare and environmental science. Labs/Teams: Active in AI for the People (applied AI solutions) and BLUE (marine data science). Collaborations include work on virtual twin technology for stroke management and medical data analytics.

Søren Knudsenمشاهده پروفایل

استادیار

• Information Visualization
• Human-Computer Interaction
• Data Visualization
• +۳ مورد دیگر

Søren Knudsen is an Assistant Professor at the Digital Design Department, IT University of Copenhagen and holds a Marie Curie Fellowship for postdoctoral work at the University of Copenhagen. He serves as Academic Director of the Human Data Interaction Lab. Positions: Assistant Professor, Marie Curie Postdoctoral Scholar Affiliations: IT University of Copenhagen, University of Copenhagen Key Roles: Academic Director, Human Data Interaction Lab His research centers on information visualization and human-computer interaction , particularly in face-to-face and online contexts. He explores how large-area displays and social media can enhance data comprehension and collaborative discussion. Notable work includes surveys on interactive visualization techniques for large displays, frameworks for data visualization education , and EMR-based algorithms for healthcare performance evaluation. His publications span journals like IEEE Transactions on Visualization and Computer Graphics and PLOS ONE . Awards and honors: Marie Curie Fellowship He collaborates across disciplines, with ties to healthcare analytics, educational technology, and computer science departments. His recent studies address challenges in visualization design handoff , proxemics , and ubiquitous data interaction .

• Software Engineering
• Process Modeling
• Business Process Management
• +۵ مورد دیگر

Thomas Troels Hildebrandt is a Professor in the Department of Computer Science at the University of Copenhagen, where he heads the Software, Data, People & Society research section. His work focuses on developing reliable and flexible software systems that adapt to user needs and legislative changes, with applications in digital law, workflows, and business processes. His educational background includes: PhD in Computer Science from Aarhus University (awarded February 23, 2000) Professor Hildebrandt's research spans Software Engineering , Process Modeling , and Business Process Management , with a focus on declarative approaches like Dynamic Condition Response (DCR) graphs. His work integrates formal methods to ensure system reliability in contexts ranging from smart contracts to public governance. He actively explores societal implications of AI, advocating for transparency and user-centered design in digital systems. Recent publications demonstrate a strong trend toward declarative process modeling applied to smart contracts and public governance . Key developments include DCR graphs for dynamic behavior modeling, cross-chain business logic monitoring, and digital compliance frameworks. His interdisciplinary approach bridges computer science with real-world societal challenges, particularly in adapting systems to evolving legislation and user requirements. No specific scientific awards are mentioned in the provided information. Professor Hildebrandt leads interdisciplinary research projects and serves on advisory boards for digitalization and AI. His work has fostered industry collaboration, including founding DCR Solutions based on his research. He acts as an independent consultant and speaker in digital transformation, with recent projects focusing on blockchain integration and public sector AI ethics. He directs the Software, Data, People & Society research section, which develops human-centered methods for adaptable digital systems. Current initiatives include DCR graph applications for GDPR compliance, smart contract security, and transparent AI in public services, with strong industry and government partnerships.

Ira Assentمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Clustering Algorithms
• +۵ مورد دیگر

Professor Ira Assent is affiliated with the Department of Computer Science at Aarhus University. Their research focuses on machine learning, data mining, and visualization, with applications in climate science, medical informatics, and computer vision. Professor Assent leads projects such as Light-IoT (analytics on compressed IoT data), WallViz (interactive visualization for massive datasets), and eData (anomaly detection in e-science). Their work emphasizes scalable algorithms, explainable AI, and interdisciplinary applications. Recent publications address rainfall prediction using deep learning, entity summarization via knowledge graphs, and efficient clustering techniques. Projects like RainAI demonstrate contributions to weather modeling and satellite data analysis. Collaborative efforts span academic and industrial domains, with a strong emphasis on practical, user-centric solutions. Selected research contributions include advancements in density-based clustering (e.g., AnyDBC, DISCO), parallel algorithms optimized for GPUs (HUNIPU), and visualization frameworks (AVID). Their work bridges theoretical computer science with real-world challenges, such as improving decision-making through interactive visualizations and enhancing medical information retrieval systems. Ongoing projects aim to address computational efficiency in large-scale data analytics while maintaining interpretability. Key areas of innovation include explainable AI (e.g., InteDisUX), climate modeling (DROPP), and hardware-accelerated algorithms (GPU-FAST-PROCLUS). These efforts reflect a commitment to advancing both foundational methods and applied technologies that impact diverse fields from environmental science to healthcare.

Tiziana Di Matteoمشاهده پروفایل

استاد

• Econophysics
• Complex Systems
• Complex Networks
• +۷ مورد دیگر

Tiziana Di Matteo is a Professor of Econophysics in the Department of Mathematics at King's College London, part of the Faculty of Natural, Mathematical & Engineering Sciences. She is also External Faculty at the Complexity Science Hub and affiliated with the UCL Centre for Blockchain Technologies, the Complex System Laboratory, the Complex Systems Society, and the Museo Storico della Fisica e Centro Studi e Ricerche “E. Fermi”. Her research focuses on econophysics, complex systems, complex networks, and data science , with applications in financial modeling, systemic risk, and multiscaling analysis. She integrates methods from statistical physics and network science to analyze financial markets and economic interactions. The most recent publications reveal a strong trend in financial network modeling, multiscaling volatility, tensor-based learning for multidimensional data, and early warning systems using scaling properties . Her work bridges econophysics and traditional financial economics, emphasizing empirical validation and interdisciplinary approaches. Scientific Leadership and Editorial Roles: Editor-in-Chief, Journal of Advances in Mathematical Physics Main Editor, Physica A Editor, European Physical Journal B Editor, Artificial Intelligence in Finance Former Editor-in-Chief, Journal of Network Theory in Finance Guest Editor for multiple special issues Tiziana is the co-founder of the Econophysics Network and has served as a consultant for the Financial Services Authority, hedge funds, and financial companies. She has authored over 100 papers and delivered keynote talks globally. She supervises PhD students and leads collaborative research in complex financial systems. She is actively involved in interdisciplinary research collaborations with institutions like the Francis Crick Institute and industrial partners including EDF Research and Unilever.

M. Ángeles Serranoمشاهده پروفایل

استاد پژوهشی

• Complex Networks
• Network Science
• Multiplex Networks
• +۹ مورد دیگر

M. Ángeles Serrano is an ICREA Research Professor at the Universitat de Barcelona, where she conducts interdisciplinary research in complex systems and network science. Her work bridges physics, biology, and economics through the study of complex networks and their real-world applications. Research Interests: Her primary focus lies in complex networks , including multiplex networks , networks of networks , and time-varying connectivity . She applies these frameworks to diverse systems such as molecular networks in cells, international trade, and socio-technological infrastructures. Her research emphasizes structural and dynamic properties of interconnected systems, aiming to uncover universal principles across domains. The most recent publications reflect a consistent trend in advancing theoretical and applied aspects of network science, particularly in multiscale modeling, visualization, and interdisciplinary applications. Her work integrates physics-based approaches with data-driven analysis across biological, economic, and social contexts. Scientific Awards: Outstanding Referee Award, American Physical Society Advising and Grants: While specific students are not listed, her leadership roles suggest active mentorship. She is a founding member of Complexitat , the Catalan network for complex systems, and a promoter of the UBICS (Universitat de Barcelona Institute of Complex Systems), indicating involvement in large-scale collaborative research initiatives and likely grant-funded projects. Labs and Teams: She is closely associated with UBICS and has collaborated internationally through institutions such as Indiana University, EPFL, and IFISC, suggesting integration into major research consortia focused on complexity science.

Jørgen Villadsenمشاهده پروفایل

دانشیار

• Logic
• Artificial Intelligence
• Multi-Agent Systems
• +۵ مورد دیگر

Roles and Affiliations: Jørgen Villadsen is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the Department of Applied Mathematics and Computer Science and the Algorithms, Logic and Graphs Section. He serves as Head of Study and focuses on formal logic, multi-agent systems, and computer science education. Education: MSc in Computer Science and Engineering (1989, DTU) and PhD in Computer Science (1995, DTU). Research Interests: Villadsen’s work centers on logic and its applications in computer science and artificial intelligence, including formal methods, multi-agent systems, type theory, and non-classical logics. He emphasizes the development of proof assistants like Isabelle and tools for teaching logic and formal reasoning. Recent Research Trends: His publications from 2020–2017 highlight contributions to logic education, automated reasoning, and multi-agent system design. Key areas include Isabelle-based proof systems, hybrid logic formalization, and competitive agent development in programming contests. Scientific Contributions: He has led teams in the Multi-Agent Programming Contest, published extensively in journals like Annals of Mathematics and Artificial Intelligence , and contributed to open-source tools like NaDeA and SPA. Grants and Projects: Involved in projects such as CONTROL (constraint-based language processing) and HyLoMOL (hybrid logic integration). Supervised numerous MSc and PhD students in multi-agent systems and formal methods. Labs/Teams: Active in the Multi-Agent Systems Lab, leading development of agents for competitions and educational tools. Collaborates internationally on logic, AI, and formal verification.

• Computational Social Science
• Urban Science
• Spatial Analysis
• +۳ مورد دیگر

Rossano Schifanella is an Associate Professor in Computer Science at the University of Turin and a Researcher at ISI Foundation's Data Science for Social Impact and Sustainability group. His interdisciplinary research spans machine learning, urban science, and computational social science, with a focus on modeling human behavior dynamics in urban contexts. Primary Affiliation: University of Turin Secondary Affiliation: ISI Foundation Research Focus: Data-driven urban sustainability, social behavior analysis, and spatial-temporal modeling His recent work explores: Urban mobility resilience during pandemics Equitable green space accessibility Socioeconomic-environmental conflict drivers Opioid use recovery dynamics in digital communities He specializes in: Geospatial data analysis Network science applications Interactive urban visualization Integration of social and environmental datasets

Christoph Seidlمشاهده پروفایل

دانشیار

• Software Engineering
• Virtual Reality
• Software Visualization
• +۵ مورد دیگر

Christoph Seidl is an Associate Professor in the Department of Software Engineering at the IT University of Copenhagen. His research focuses on software visualization, software archaeology, and the application of virtual reality in software engineering tasks. He leads the Immersive Software Archaeology project, exploring collaborative systems and legacy system analysis in virtual environments. Key research areas include software product lines, feature modeling, and model-driven development. Seidl has received awards such as the SoSyM Best Reviewer Award (2018) and SPLC'19 Distinguished Reviewer Award (2019). His work emphasizes innovative tools for software exploration, including immersive 3D visualization and collaborative note-taking systems in VR. He has published extensively on topics like variability management, configuration testing, and domain-specific languages. Research trends in his articles highlight advancements in using VR for software archaeology, collaborative software exploration, and methodologies for managing software evolution. His projects address challenges in legacy system understanding, feature model synthesis, and automated testing for configurable systems. Seidl collaborates internationally, with projects funded by the Independent Research Fund Denmark. His contributions span academic publications, datasets (e.g., VaMoS 2022 artifacts), and tool development for software product line engineering.

Daniele Dell'Aglioمشاهده پروفایل

دانشیار

• Computer Science
• Knowledge Graph Embedding
• Differential Privacy
• +۶ مورد دیگر

Daniele Dell'Aglio is an Associate Professor at the Department of Computer Science , Aalborg University , affiliated with the Technical Faculty of IT and Design . His research focuses on privacy-preserving data synthesis, knowledge graphs, and semantic data integration. Academic Rank: Associate Professor Department: Computer Science School: Technical Faculty of IT and Design University: Aalborg University Research Interests : Development of privacy metrics for synthetic data generation Heterogeneous graph representation in knowledge graphs Interactive evaluation tools for data privacy Transformer-based biomedical data extraction Integration of gut-brain axis scholarly data Scientific Awards : Best paper at the Deep Learning for Knowledge Graphs workshop (2022) Collaborative Projects include HEREDITARY (Heterogeneous semantic data integration for gut-brain interplay) as Principal Investigator, and partnerships with NASA on Mars exploration data processing. His work spans privacy-preserving technologies, knowledge graph engineering, and biomedical text mining applications.

Samir Bhattمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Public Health
• +۵ مورد دیگر

Samir Bhatt is a Professor of Machine Learning and Public Health at the University of Copenhagen's Faculty of Health and Medical Sciences, Department of Public Health, Section for Health Data Science and AI. He also holds a position as Professor of Statistics and Public Health at Imperial College London since 2016. His work focuses on developing mathematical, statistical, and computer science tools to address critical questions in human health. His educational background includes a DPhil in Statistical Genetics from the University of Oxford (2010), an MPhil in Computational Biology from the University of Cambridge (2006), and a BEng in Chemical and Bioprocess Engineering from the University of Bath (2005). Professor Bhatt's research spans the intersection of statistics, machine learning, and public health with particular emphasis on infectious disease modeling. His primary research areas include Bayesian inference, genomic epidemiology, and kernel methods applied to health data. His work bridges theoretical statistical approaches with practical public health applications, particularly in disease surveillance and outbreak response. The integration of AI with traditional epidemiological methods represents a key innovation in his research program. Analysis of his recent publications reveals a strong focus on applying advanced computational methods to pressing public health challenges. His work demonstrates consistent innovation in developing AI-driven approaches for infectious disease modeling, genomic surveillance, and survival analysis. Notable themes include the application of graph neural networks to epidemiological data, development of interpretable AI tools for public health decision-making, and sophisticated modeling of disease transmission dynamics across multiple pathogens including malaria, cholera, and respiratory viruses. Professor Bhatt has published extensively with over 107 research outputs to date. His work has received significant attention, with multiple publications covered by news outlets, referenced on social media platforms, and read by researchers on academic platforms like Mendeley. His research on AI for infectious disease modeling published in Nature demonstrates the high impact of his work in both academic and policy spheres. His research group at the University of Copenhagen appears to focus on developing computational tools for health data science, with particular emphasis on creating analytical frameworks that can be rapidly deployed during disease outbreaks. The development of GRAPEVNE (Graphical Analytical Pipeline Development Environment for Infectious Diseases) represents one such effort to create accessible tools for public health practitioners.

J. Andreas Bærentzenمشاهده پروفایل

دانشیار

• Computer Graphics
• Shape Modelling
• Real-Time Rendering
• +۵ مورد دیگر

Professor J. Andreas Bærentzen is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), where he has held academic positions since 2001. His research focuses on computer graphics, shape modeling, real-time rendering, and geometry processing. He holds a MSc Eng (1998) and PhD (2003) from DTU. His work emphasizes topological adaptability in shape representation and efficient geometry processing, with notable contributions to structural topology optimization, 3D reconstruction, and virtual reality applications. Education: MSc Eng, Technical University of Denmark, 1991–1998 PhD, Technical University of Denmark, 1998–2003 Research Interests: Interactive 3D modeling and real-time graphics Topology optimization and structural infill design Shape modeling using distance fields and deformable hypersurfaces Applications in biomedical imaging (e.g., white matter dynamics) and environmental engineering (e.g., tree geometry simulation) His recent publications highlight advancements in neural network-based shape representation, skeletonization for tree reconstruction, and inverse-designed structural infill for engineering. He supervises multiple PhD students and leads projects on robotic manufacturing and virtual reality visualization. Bærentzen collaborates internationally, including visits to Stony Brook University (USA) and Padova University (Italy). Advisees & Projects: PhD Students: Rui Cui, Thomas D. V. Christiansen, Elias Theil Gæde Key Projects: 'Generative Methods for Brain Tissue Phantoms', 'Neural Form Representation', 'Graph Algorithms with Geometric Applications' His work bridges theoretical geometry processing with practical applications in architecture, biomedical engineering, and environmental science, leveraging DTU's interdisciplinary research ecosystem.

Tianyi Liمشاهده پروفایل

استادیار

• Digital Twin
• Graph Neural Networks
• Energy Engineering
• +۸ مورد دیگر

Tianyi Li is a Tenure Track Assistant Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design and the Daisy - Center for Data-intensive Systems. His research focuses on data engineering, spatiotemporal systems, and digital twins, with applications in energy, transportation, and vehicular networks. Key areas include trajectory data management, graph neural networks, compression techniques, and federated learning. He has contributed to over 37 publications, including works on digital twin frameworks for autonomous driving and smart grids, trajectory prediction systems, and efficient time series compression. His research integrates machine learning, distributed systems, and cybersecurity to address challenges in data-intensive environments. Key Projects: Digital twin-enabled voltage control, federated vehicular networks, trajectory simplification frameworks. Labs: Involved with the Daisy Center, focusing on data-centric systems and scalable solutions. Technical Expertise: Blockchain systems, energy systems optimization, and real-time data processing.

Gilles Claude Vanwalleghemمشاهده پروفایل

استادیار

• Neurobiology
• Molecular Biology
• Zebrafish Research
• +۴ مورد دیگر

Gilles Claude Vanwalleghem is a Tenure Track Assistant Professor at Aarhus University, affiliated with the Department of Molecular Biology and Genetics (Neurobiology) and DANDRITE's Vanwalleghem Team. His research spans neuroscience, molecular biology, and zebrafish models. Department: Molecular Biology and Genetics - Neurobiology Institution: Aarhus University (Denmark) His research interests focus on neuro-immune interactions, oxytocin signaling, and brain network dynamics using zebrafish as a model organism. Specializations include calcium imaging, graph topology analysis, and developmental disorders like autism. Recent publications highlight interdisciplinary work in neuro-immune interactions in autism, optopharmacological tools for oxytocin control, and network neuroscience approaches to zebrafish visual habituation. Key themes include neurodevelopmental mechanisms and systems biology. Collaboration networks span neuroscience, molecular biology, and immunology, with strong emphasis on zebrafish models for studying pervasive developmental disorders and neural coding.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +۵ مورد دیگر

Oliver Mortensen is a PhD Fellow (Research Fellow) at the Machine Learning Section , Department of Computer Science (DIKU) , University of Copenhagen , Denmark. He is affiliated with the university’s Faculty of Science and participates in the cross-faculty SCIENCE AI Centre , a strategic initiative to advance artificial intelligence research and applications. Research Interests Mortensen’s research lies at the intersection of machine learning , quantum computing , and neuro-symbolic AI . His work spans both theoretical foundations—such as entropic risk optimization in reinforcement learning and Riemannian generative models—and highly applied domains including medical AI, recommender-system fairness, and brain-computer interfaces. A recurring theme is trustworthy AI , where he investigates explainability, fairness, and sustainability across large language models and clinical decision-support systems. Scientific Contributions & Trends Across more than 60 peer-reviewed contributions (2024-2025), Mortensen demonstrates a clear trajectory toward hybrid quantum-classical algorithms , energy-efficient AI , and human-centric evaluation . His publications integrate rigorous theoretical guarantees with empirical validation on real-world data from electronic health records, satellite imagery, and conversational corpora. Collaborations & Resources He carries out his doctoral research under the supervision of Professor Yevgeny Seldin within DIKU’s vibrant Machine Learning Section. The group offers access to a dedicated high-performance compute cluster, the SCIENCE AI Centre ’s GPU/TPU pools, and interdisciplinary ties to life-science, geoscience, and humanities researchers across the university.