جست‌وجوی استاد و پژوهشگر

Henrik Jeldtoft Jensenمشاهده پروفایل

استاد

• Complexity Science
• Statistical Mechanics
• Mathematical Physics
• +۷ مورد دیگر

Henrik Jeldtoft Jensen is a Professor of Mathematical Physics and leads the Centre for Complexity Science at Imperial College London. His work spans multiple disciplines, focusing on the statistical mechanics of complex systems, with applications in physics, biology, neuroscience, and finance. Professor, Mathematical Physics Leader, Centre for Complexity Science Institution: Imperial College London His research interests lie at the intersection of theoretical physics and complex systems. He is best known for developing the Tangled Nature Model of evolving ecosystems, which has been extended into financial modeling through the Tangled Finance approach. His work in brain dynamics involves analyzing fMRI and EEG data using tools from statistical physics. He has made significant contributions to self-organized criticality and stochastic dynamics of complex systems, particularly in condensed matter and evolutionary contexts. The recent publications reflect a strong trend toward interdisciplinary complexity science, integrating concepts from physics, biology, economics, and neuroscience. Keywords across these works include complexity, statistical mechanics, dynamical systems, and network theory, with subfields ranging from neural avalanches to financial instability and biodiversity modeling. Henrik Jensen is the author of two influential books: Self-Organized Criticality and Stochastic Dynamics of Complex Systems (with Paolo Sibani), which have been widely cited across disciplines. He has supervised numerous PhD and postdoctoral researchers through the Centre for Complexity Science, though specific names are not listed. His research has been supported by grants from UK research councils and international collaborations, particularly in interdisciplinary complexity projects. He is affiliated with the Centre for Complexity Science, a multidisciplinary research hub at Imperial College London that brings together physicists, mathematicians, biologists, and social scientists to study complex adaptive systems.

Giovanni Colavizzaمشاهده پروفایل

استاد

• Open Science
• Scientometrics
• Digital Humanities
• +۴ مورد دیگر

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.

Filipe Rodriguesمشاهده پروفایل

دانشیار

• Machine Learning
• Intelligent Transportation Systems
• Transportation Science
• +۷ مورد دیگر

Filipe Rodrigues is an Associate Professor in the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), where he conducts research in intelligent transportation systems and transportation science. His work integrates machine learning, artificial intelligence, and behavioral modeling to improve urban mobility and public transport systems. His research interests lie at the intersection of machine learning , transportation science , and behavioral modeling . He specializes in discrete choice modeling , reinforcement learning , graph neural networks , and smart card data analytics . His work contributes to sustainable urban mobility, leveraging big data and AI for proactive traffic control and public transport optimization. The recent publications highlight a strong trend toward integrating AI and behavioral science in transportation. Key themes include ride-sourcing driver behavior , public transport trip validation , autonomous fleet control , and causal machine learning . These works predominantly employ deep learning , Bayesian modeling , and offline reinforcement learning techniques, often applied to real-world datasets from Denmark and beyond. Scientific Contributions: Active contributor to journals like Transportation Research Part C and Journal of Choice Modelling . Supervises multiple PhD projects on AI in transportation and causal modeling. Regular presenter at major transportation and AI conferences. Advising and Grants: Filipe Rodrigues is the main or co-supervisor of several PhD students including O. B. Lassen, F. M. F. Santos, A. Nguyen, and X. Wu. He leads and participates in funded research projects such as 'Proactive traffic control through AI and Big Data' and 'Causal Graph Neural Networks for machine learning meta-modelling', indicating sustained grant support. His collaborative network spans institutions in Europe and beyond. Labs and Teams: He is part of the Intelligent Transportation Systems research group at DTU, collaborating closely with researchers like F. C. Pereira and C. M. L. Azevedo. The team focuses on data-driven mobility solutions, combining simulation, machine learning, and behavioral insights.

Martin Skovgaard Andersenمشاهده پروفایل

دانشیار

• Optimization
• Signal Processing
• Machine Learning
• +۱۲ مورد دیگر

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.

Katja Hoseمشاهده پروفایل

استاد

• Query Processing
• Semantic Web
• Linked Data
• +۷ مورد دیگر

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.

Alberto Santos Delgadoمشاهده پروفایل

پژوهشگر

• Data Engineering
• Data Science
• Multi-omics Network Analytics
• +۸ مورد دیگر

Alberto Santos Delgado is the Director of the Informatics Platform at the Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark. His work focuses on data engineering, multi-omics analytics, and microbial biotechnology, with applications to sustainable development goals. Projects: Supervising PhD research on protein sequencing with deep learning, microbial community analysis via single-cell sequencing, graph-based precision fermentation optimization, and knowledge graph applications for microbiome identification. Research Themes: Biotransformation of pollutants, metabolic modeling, microbial genomics, and computational systems biology. Collaborations: Engaged in interdisciplinary projects under DTU Microbes Initiative and with supervisors like Bernhard Palsson. Recent publications highlight his contributions to microbial pangenome knowledgebases and metabolic engineering for environmental remediation. No scientific awards are explicitly mentioned in the available data.

• Spatial data analysis
• Time series data
• Machine learning
• +۴ مورد دیگر

Maria Sinziiana Astefanoaei is an Assistant Professor at the IT University of Copenhagen , affiliated with the Data, Systems, and Robotics department. Her research focuses on spatiotemporal data analysis, urban computing, and graph algorithms using machine learning techniques. Research Interests: Spatial data analysis, Time series data processing, Large-scale visualizations, Machine learning, Human mobility modeling, and Embeddings. Projects: Principal Investigator for CCAI: Towards greener last-mile operations (2022-2023), contributing to cargo-bike logistics optimization, and Co-Investigator for the Pilot Hub project (2020-2022) funded by the Danish Agency for Research and Education. Publications: 2021 conference paper at CIKM '21 on spatiotemporal signal processing frameworks with neural machine learning models. Her work intersects computer science , urban logistics , and environmental sustainability , with applications in smart city technologies and multi-modal transportation systems.

Matthias Mannمشاهده پروفایل

استاد

• Proteomics
• Mass spectrometry
• Clinical proteomics
• +۶ مورد دیگر

Professor Matthias Mann is Research Director and Group leader at the Proteomics Program at Novo Nordisk Foundation Center for Protein Research (CPR) at the University of Copenhagen's Faculty of Health and Medical Sciences. He also holds a Director position at the Max-Planck Institute of Biochemistry in Munich. As one of the most highly cited researchers in the world with h-index 216 and over 200,000 citations, Mann is a pioneer of mass spectrometry-based proteomics who has made landmark contributions to the development of electrospray ionization. Professor Mann's research interests focus on proteomics technology development and its application to biological and clinical problems. His Clinical Proteomics group applies mass spectrometry-based proteomics to understand human health and disease, with the goal of improving patient diagnosis, stratification, and prevention of diseases such as metabolic disorders and cancer. The group has established robust, high-throughput proteome profiling pipelines for clinical cohorts and develops AI-guided platforms for analyzing proteomes from low amounts of formalin-fixed, paraffin-embedded samples. A key research area is the interpretation of multi-omics data through the Clinical Knowledge Graph, which harmonizes multi-omics data with meta-data for machine learning applications. Professor Mann's recent publications demonstrate trends across several fields including clinical proteomics, biomarker discovery, mass spectrometry technology development, and multi-omics integration. His work spans applications in cancer research, metabolic diseases, neuroscience, and cardiac biology, with a consistent focus on translating proteomic technologies into clinical applications for personalized medicine. Dr H.P. Heineken Prize for Biochemistry and Biophysics 2024 Louis-Jeantet Foundation Prize for Medicine (2012) Leibniz Prize of the German Research Society (2012) Körber European Science Award (2012) Ernst Schering Prize (2012) Protein Society Anfinsen Award (2005) Novo Nordisk Prize (2004) Professor Mann has mentored numerous researchers, with several former post-docs receiving prestigious ERC Starting Grants. His research has been supported by significant funding from the Novo Nordisk Foundation and other major research organizations. The Mann Group maintains collaborations with clinical researchers across multiple institutions to apply proteomics to patient cohorts and disease studies. The Mann Group operates within the Novo Nordisk Foundation Center for Protein Research at the University of Copenhagen, working closely with other research groups including the Choudhary Group, Olsen Group, and others within the CPR. The group maintains state-of-the-art mass spectrometry facilities and develops computational tools for proteomic data analysis, creating an integrated environment for technological innovation and biological discovery.

Alvaro Torralbaمشاهده پروفایل

دانشیار

• Symbolic Search
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Alvaro Torralba is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark, affiliated with the Technical Faculty of IT and Design. His research focuses on symbolic search, heuristic functions, and planning algorithms within artificial intelligence and machine learning. Notable projects include the ConAn initiative exploring contrastive analysis for state-space exploration. He has contributed extensively to classical planning, probabilistic planning, and automated planning competitions, earning awards such as the First Prize in the Agile Track of the 10th International Planning Competition (IPC’23). His work often bridges theoretical advancements with practical applications, including game-based network update synthesis and believable non-player character development. Research outputs include over 60 publications since 2011, with a focus on optimizing search algorithms and enhancing planning efficiency through techniques like operator-potential heuristics and bidirectional search strategies. His scientific contributions span algorithmic innovation, verification methodologies, and large-scale abstraction evaluation. Collaborations and datasets include foundational work on PDDL generators and pattern databases, with open-access resources available via Zenodo. As a program committee member and award-winning researcher, Torralba actively contributes to advancing the frontiers of AI planning and decision-making systems.

Bernardo Machado Davidمشاهده پروفایل

دانشیار

• Cryptography
• Secure Multiparty Computation
• Blockchain Technology
• +۳ مورد دیگر

Bernardo Machado David is an Associate Professor at the IT University of Copenhagen (ITU) within the Theoretical Computer Science department and a member of the Center for Information Security and Trust. Previously, he was an Assistant Professor at Tokyo Institute of Technology (2017–2018) and completed his Ph.D. in Computer Science at Aarhus University (2013–2017) under Ivan Damgård and Jesper Buus Nielsen. His research focuses on cryptography and information theory, particularly secure multiparty computation (MPC), blockchain technologies, and privacy-preserving protocols. He has held visiting positions at NTT Secure Platform Laboratories and Bar Ilan University’s Cryptography Group. Educational Background: Ph.D. in Computer Science, Aarhus University (2017) Visiting Researcher at NTT Secure Platform Laboratories and Bar Ilan University Research Interests: Bernardo’s work bridges theoretical and practical aspects of cryptography, emphasizing MPC protocols, blockchain consensus mechanisms, and privacy-enhancing technologies. Key areas include constructing efficient cryptographic primitives with provable security guarantees, exploring asymptotic limits of protocols, and designing scalable solutions for privacy in decentralized systems. His projects are funded by institutions like the Danish Independent Research Fund (DFF), Concordium Foundation, and Protocol Labs. Recent Research Trends: His publications (2023) highlight advancements in privacy-preserving smart contracts, differential privacy in financial systems, and composable MPC frameworks. He frequently collaborates on blockchain security, time-based cryptographic primitives, and anonymous credential systems. Awards and Grants: Best Paper Awards at SBSEG 2012 and ICoFCS 2011 Principal Investigator for projects like PUMA (Publicly Verifiable MPC), TrA²C (Transaction Anonymity in Cryptocurrencies), and Scalable Proof-of-Stake Blockchain Protocols Advising & Teams: Supervises PhD students (e.g., James Hsin-yu Chiang, Anders Konring) and collaborates with postdocs (e.g., Esra Yeniaras). Leads research groups focused on MPC, blockchain, and privacy-preserving applications. Active in organizing academic events like the Summer School on Privacy-Preserving Machine Learning and workshops at CRYPTO and Eurocrypt. Labs & Collaborations: Engages with industry via roles like Scientific Advisor at Concordium. His work integrates academic rigor with real-world applications, such as privacy-preserving decentralized exchanges and audit-compliant financial systems.

Adem R. N. Aouichaouiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Deep Learning
• Molecular Property Prediction
• +۶ مورد دیگر

Adem R. N. Aouichaoui is a postdoctoral researcher at the Department of Energy Conversion and Storage (Technical University of Denmark). His work focuses on machine learning applications in chemical engineering, particularly for molecular property prediction and hybrid modeling systems. Research areas: Machine Learning, Deep Learning, Graph Neural Networks Specialization: Data-driven process optimization and digital transition in (bio)chemical engineering Recent work includes real-time optimization in industrial processes (Viking Malt A/S case study) and self-consistent graph neural networks for thermodynamic property prediction. He actively contributes to AI applications in energy systems and process engineering. Contact: arnaou@kt.dtu.dk | arnaou@dtu.dk

Zhe Chenمشاهده پروفایل

استاد

• Wind Turbine Engineering
• Power Engineering
• Control Strategy
• +۷ مورد دیگر

Professor Zhe Chen is a distinguished academic at Aalborg University's Faculty of Engineering and Science, where he leads research in Electric Power Systems and Microgrids within the Intelligent Energy Systems and Flexible Markets department. With an extensive publication record spanning over two decades and more than 1,200 publications, he has established himself as a leading expert in power engineering and renewable energy systems. Professor Chen's research focuses on Wind Turbine Engineering, Power Engineering, Control Strategy, Wind Power Engineering, Energy Engineering, and Reinforcement Learning. His work bridges theoretical advancements with practical applications in smart grid technology and microgrid systems. His research interests center around developing innovative solutions for renewable energy integration, power system stability, and efficient energy management. His recent publications demonstrate a strong trend toward integrating artificial intelligence with traditional power engineering, particularly in applying deep reinforcement learning to power system control, state estimation with noisy data, and optimization of power electronic converters. His work shows increasing focus on carbon emissions optimization, thermal management in power electronics, and the application of advanced neural network architectures to energy systems. MPCE 2023 Best Paper Award for research on reinforcement learning applications in electric vehicles MPCE 2022 Best Paper Award for distribution network optimization MPCE 2021 Best Paper Award for reinforcement learning in energy systems WATAB Best Paper Award 2005 for offshore wind farm research IEEE Fellow recognition for contributions to power electronics and renewable energy systems Professor Chen has supervised 27 PhD students throughout his career, demonstrating his commitment to academic mentorship. His current research is supported by significant grants including the Erasmus+ funded S3SF project (Smart Energy Solutions for a Sustainable Future) and multiple projects focused on machine learning-based stability analysis for multi-energy systems. His collaborative work spans numerous international partnerships, with recent projects involving researchers from China, Europe, and other global institutions. Professor Chen leads a research team focused on intelligent energy systems, working on advanced control strategies for microgrids, power quality analysis, and the integration of renewable energy sources into existing power infrastructure. His team is particularly known for innovative approaches to wind power integration and DC microgrid technologies.

• Graph Theory
• Data Mining
• Network Analysis
• +۲ مورد دیگر

Aristides Gionis is a Research Professor Fellow at Aalto University specializing in advanced network analysis and data mining. His work bridges theoretical graph algorithms with practical applications in dynamic and signed networks. His research focuses on graph theory and temporal network analysis , particularly in discovering dense subgraphs, community structures, and event patterns in evolving networks. Key methodologies include contrastive subgraph mining for explainable AI in brain networks and polarization detection in social systems. His publication trends reveal consistent contributions to top-tier venues like KDD, PAKDD, and ECML PKDD, with emphasis on algorithmic solutions for network segmentation, correlation mining, and interpretable classification. Primary application domains include social networks, neuroscience, and temporal data streams. As a Fellow at Aalto University, he leads research in computational network science without explicit departmental affiliation stated in available sources.

• Artificial Intelligence
• Medical Imaging
• Machine Learning
• +۳ مورد دیگر

Anna Anikina is a PhD Fellow at the Department of Computer Science (DIKU), University of Copenhagen, affiliated with the Image Analysis, Computational Modelling and Geometry section. Her research focuses on applying artificial intelligence and machine learning to analyze eye-tracking data for predicting radiological diagnostic errors. Current position: PhD Fellow in Image Analysis and Computational Modelling University: University of Copenhagen Department: Image Analysis, Computational Modelling and Geometry Anna’s work spans Medical Imaging , Eye Tracking , and Transformers for diagnostic error prediction, as well as Latent-Space Modelling in reinforcement learning systems. Her recent publications highlight applications of Graph Neural Networks and Multi-Agent Systems to biomedical image analysis and visual data processing. Her research outputs include four peer-reviewed publications (2023–2025) addressing radiological decision errors through gaze analysis and advanced machine learning architectures. Collaborations with researchers like B. Ibragimov, C. Mello-Thoms, and others demonstrate her focus on AI-driven healthcare solutions.

• Medical Image Analysis
• Image Segmentation
• Image Registration
• +۳ مورد دیگر

Jens Petersen is an Associate Professor at the Department of Computer Science , University of Copenhagen, specializing in medical image analysis. He works within the Image Analysis, Computational Modelling, and Geometry research section. Education: B.Sc. (2005), M.Sc. (2010) in Computer Science from University of Copenhagen; Ph.D. (2014) in Medical Image Analysis from University of Copenhagen with visiting researcher experience at University College London. Research Interests: Focus on medical image analysis techniques for segmentation and statistical analysis of tubular/branching structures like airways and carotid arteries. Key methodologies involve graph cuts, optimal surface methods, and computational modeling. Current research includes AI-guided tumor delineation, longitudinal image synthesis, and physics-based deformable registration. Publications Trends: Recent work spans AI applications in oncology, computational methods for cardiovascular imaging, and algorithm development for radiation therapy. Collaborations include international institutions and cross-disciplinary teams in machine learning and medical imaging.