جست‌وجوی استاد و پژوهشگر

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.

Chenjuan Guoمشاهده پروفایل

دانشیار

• Machine Learning
• Data Engineering
• Spatio-temporal Data Analysis
• +۴ مورد دیگر

Chenjuan Guo is an Associate Professor at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. She is affiliated with the Data Engineering, Science and Systems group and the AI for the People initiative, and is part of the Daisy - Center for Data-intensive Systems. Her research focuses on machine learning, data engineering, spatio-temporal data analysis, and time series forecasting. Key projects include the Villum Foundation-funded 'Explainable AI for Complex Microbial Community Interactions and Predictions' (2021-2024) and the Astra project on time series analytics in spatial networks (2018-2021). Her research interests span representation learning, autoencoders, path representation, outlier detection, trajectory data analysis, and time series modeling. She has supervised 3 PhD students and contributed to over 60 publications, with a recent emphasis on transformer-based forecasting, neural architecture search, and continuous learning frameworks for spatio-temporal data. Her work bridges theoretical advancements with practical applications in environmental science, cloud computing, and urban mobility systems. Key achievements include developing frameworks like AutoCTS++ for automated time series forecasting and LightGTS for lightweight models. She actively collaborates internationally, contributing to conferences like ECML PKDD and CVPR. Her research is supported by grants from the Villum Foundation and other institutions.

Klaus Ingemann Pedersenمشاهده پروفایل

استاد

• Wireless Communication Networks
• Dependable Wireless Communications
• 5G-Advanced Technology
• +۱۰ مورد دیگر

Klaus Ingemann Pedersen is a Professor in the Department of Electronic Systems at the Technical Faculty of IT and Design, Aalborg University, Denmark. His research focuses on wireless communication networks and dependable wireless communications with substantial contributions to 5G-Advanced and 6G technologies. His research interests include: Wireless Communication Networks Dependable Wireless Communications 5G-Advanced Technology 6G Networks Radio System-Level Simulations Ultra-Reliable Low Latency Communication Heterogeneous Networks Professor Pedersen's recent work demonstrates cutting-edge research in next-generation wireless systems, with particular emphasis on deep reinforcement learning applications, link adaptation techniques, and system-level simulations. His publications show consistent high-impact output with 152 total publications spanning from 2012 to 2025, reflecting ongoing productivity and relevance in the rapidly evolving field of wireless communications. His notable achievements include: 5G-prisen (The 5G Award) received on October 23, 2019, shared with E. Mogensen and I.R. Larrad Professor Pedersen has supervised 8 PhD students according to institutional records. His research has practical applications in wireless communication, as evidenced by his involvement in patented technology 'Method and apparatus for wireless communication' which delivers social impact and quality of life improvements. His work shows strong international collaboration across multiple countries as indicated by the research network visualization.

• Bioinformatics
• Machine Learning
• Computational Biology
• +۷ مورد دیگر

Jan Baumbach is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he leads cutting-edge research at the intersection of computer science, bioinformatics, and biomedical data science. His work integrates machine learning, network biology, and systems medicine to address complex challenges in health and disease. His research interests include Bioinformatics , Machine Learning , Gene Regulatory Networks , Drug Repositioning , Biomedical AI , and Computational Biology . He applies advanced computational methods to analyze large-scale biological data, with applications in cancer, metabolic diseases, dermatology, and infectious diseases. The 15 most recent publications highlight a strong trend toward biomedical artificial intelligence , network-based analysis , and translational bioinformatics . His work spans from foundational machine learning in healthcare to clinical applications in osteoarthritis, bone regeneration, and skin biology. A consistent theme is the integration of multi-omics data and the development of privacy-preserving federated learning frameworks for distributed healthcare systems. Jan Baumbach has been principal investigator on several major research projects, including: EU Horizon2020: Privacy-preserving federated machine learning in distributed healthcare Danish National Research Foundation: ATLAS Center for Functional Genomics of Tissue Plasticity Villum Foundation: Big Data Bioinformatics (Young Investigator Programme) Carlsberg Foundation: Computational profiling of bacterial volatile metabolomes (ProVol) He has supervised 13 PhD students and has an extensive publication record of 262 works. His research has been featured in high-profile media outlets, emphasizing the societal impact of drug repositioning and AI in medicine. He leads a research group focused on computational systems biology , with strong collaborations across Europe in the fields of genomics, metabolomics, and clinical data science.

Jørgen Ellegaard Andersenمشاهده پروفایل

استاد

• Quantum Topology
• Geometric Quantization of Moduli Spaces
• Topological Quantum Field Theory
• +۷ مورد دیگر

Jørgen Ellegaard Andersen is a Professor and Center Director at the Department of Mathematics and Computer Science, University of Southern Denmark (SDU), and holds the D-IAS Chair at the Danish Institute for Advanced Study (DIAS). He is also a Distinguished Visiting Professor at the California Institute of Technology since 2013, reflecting his international stature in mathematical sciences. University: University of Southern Denmark School: Faculty of Science Department: Department of Mathematics and Computer Science Position: Professor, Center Director, D-IAS Chair Andersen earned his DPhil in Mathematics from the University of Oxford in 1992, with a thesis on Jones-Witten theory and the Thurston Compactification of Teichmüller space. His research lies at the intersection of quantum theory and geometry, focusing on quantum topology, geometric quantization of moduli spaces, topological quantum field theory, low-dimensional geometry, and applications to RNA and protein folding. His work combines deep mathematical rigor with potential applications in quantum computing and biophysics. The trends in his recent publications show a consistent focus on quantization of moduli spaces, topological recursion, mapping class group representations, and quantum Chern-Simons theory. His work often bridges pure mathematics and theoretical physics, with increasing attention to computational and applied aspects in quantum technologies. ERC Synergy Grant ReNewQuantum (EUR 10m, Principal Investigator) Distinguished Visiting Professor, Caltech Andersen leads major research projects such as ReNewQuantum and TopQC2X, securing significant funding from the EU and Danish agencies. He actively supervises PhD students and collaborates with leading mathematicians worldwide, including Maxim Kontsevich and Bertrand Eynard. His work is frequently highlighted in the media for its potential impact on quantum computing and Danish industry. He is a central figure in the Quantum Mathematics center at SDU and collaborates with institutions like MSRI and the University of Hamburg. His research group is part of international networks focused on geometric and topological methods in quantum theory.

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +۳ مورد دیگر

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.

Farshid Naseriمشاهده پروفایل

دانشیار

• Electric Vehicles
• Battery Management Systems
• Power Electronics
• +۵ مورد دیگر

Farshid Naseri serves as an Associate Professor at AAU Energy within The Faculty of Engineering and Science at Aalborg University, Denmark. His research centers on advancing electric vehicle technologies through innovations in energy storage, power electronics, and control systems, with significant contributions to battery and supercapacitor management. Education: Ph.D. in Electrical Engineering specializing in State Estimation in Advanced Electric Vehicles Research Focus: Dr. Naseri's work spans Electric Vehicles , Battery Management Systems , and Energy Storage , with emphasis on state estimation, predictive control, and system stabilization. His fingerprint analysis reveals core expertise in Supercapacitor engineering (100%), Kalman Filtering (100%), and State of Charge determination (100%), reflecting deep specialization in electrochemical energy systems for sustainable transportation. Publication Trends: Recent publications (2020-2025) demonstrate a strategic evolution from foundational supercapacitor modeling toward AI-integrated battery management and cybersecurity for connected vehicle systems, with increasing focus on real-world vehicular applications and next-generation lithium iron phosphate cells. Scientific Recognition: Awarded Europe's most prestigious research grant in 2025 for groundbreaking work in electric vehicle technology Research Leadership: Principal Investigator for the DeepBMS project (2023-2025), developing deep reinforcement learning-based battery management systems. His work directly contributes to UN Sustainable Development Goal 7 (Affordable and Clean Energy) through advancements in electric mobility infrastructure. Research Environment: Active member of the E-Mobility and Drives research group at AAU Energy, collaborating with international partners on cutting-edge projects at the intersection of power electronics, artificial intelligence, and sustainable transportation systems.

• 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.

• 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.

Silvia Toluمشاهده پروفایل

دانشیار

• Neurorobotics
• Bio-inspired Control Systems
• Neuromorphic Engineering
• +۳ مورد دیگر

Silvia Tolu is an Associate Professor at the Technical University of Denmark's Department of Electrical and Photonics Engineering, specializing in Neurorobotics. She leads the NeuroRobotics Technology Lab (NRT-LAB), focusing on bio-mimetic control architectures for compliant robotic systems. Her research integrates neuroscience, computer science, and biology to develop solutions for assistive robotics and neurodegenerative disease diagnosis. Her research interests span: Neuro-robotics and neuromorphic engineering Bio-inspired control systems and adaptive motor control Machine learning for robotic applications Human-robot compliant interaction Cerebellar control models Publications primarily focus on neurorobotics, bio-inspired control, and human-robot interaction, with recent advances in learning-based control systems for soft robots and aerial manipulation. Awards include the AEG Elektrofonden Research Grant and funding for human-robot interaction safety research. Current projects include LOCOPD (Lundbeck Foundation), AEROTRAIN (EU Marie Curie ITN), and compliant human-robot interaction systems. She supervises multiple PhD students in neurorobotics and maintains international collaborations across Europe and Asia. Laboratory resources include advanced robotic platforms for musculoskeletal and soft robot control.

Anders Bjorholm Dahlمشاهده پروفایل

استاد

• Medical Imaging
• Computer Vision
• Biomedical Engineering
• +۶ مورد دیگر

Anders Bjorholm Dahl is a Professor at the Department of Applied Mathematics and Computer Science , DTU Compute , Technical University of Denmark (DTU). His research focuses on medical imaging, computer vision, and biomedical engineering. He leads projects in ultrasound imaging, AI-driven medical diagnostics, and advanced imaging technologies for healthcare applications. Education: Ph.D. in Computer Science (Image Analysis and Computer Vision), DTU (2005–2009) Forestry, Royal Veterinary and Agricultural University (1997–2004) Research Interests: Combines machine learning and advanced imaging techniques to address challenges in medical diagnostics, including ultrasound super-resolution, stenosis detection in coronary angiographies, and material anisotropy analysis. His work bridges anatomy and histology using X-ray tomography and explores AI applications in healthcare. Key Projects: Crowd Counting through Remote Sensing (2025–2027) AI for Extreme Super-Resolution CT (2024–2026) Fighting Cancer with Generative AI (2024–2027) Labs/Teams: Leads the UltraSound and Biomechanics Visual Computing Center for Fast Ultrasound Imaging , focusing on real-time medical imaging solutions.

Amin Hajizadehمشاهده پروفایل

دانشیار

• Renewable Energy Systems
• DC Microgrids
• Offshore Renewable Energy
• +۶ مورد دیگر

Amin Hajizadeh is an Associate Professor at Aalborg University (AAU), affiliated with the Esbjerg Energy Section in the Faculty of Engineering and Science . His research focuses on renewable energy systems, DC microgrids, offshore renewable energy integration, and AI-driven energy solutions. He holds a PhD in Electrical Power Engineering (2010). Research Highlights: Developing control strategies for modular multiport DC-DC converters and offshore wind-hydrogen systems Advancing energy management in net-zero buildings and smart city frameworks Pioneering work in AI-based wake steering for wind farm optimization Projects & Awards: Lead investigator in BlueBARGE (€5M EU project on renewable bunkering for anchored ships) Recipient of the 2024 Best Paper Award for work on neural network-based wake steering Key Contributions: Published 140+ papers in journals like IEEE Transactions and Renewable Energy Supervised 6 PhD students and contributed to 19 funded research projects Active in editorial roles (e.g., IET Renewable Power Generation)

Abdulkadir Celikkanatمشاهده پروفایل

استادیار

• Computer Science
• Machine Learning
• Data Science
• +۳ مورد دیگر

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.

• Antenna Engineering
• 6G/5G Communications
• Metamaterials
• +۶ مورد دیگر

Ming Shen is an Associate Professor at the Department of Electronic Systems, part of The Technical Faculty of IT and Design at Aalborg University. His research focuses on antennas, millimeter-wave systems, and AI-driven RF sensors with applications in 5G/6G communications, biomedical engineering, and smart systems. His research interests span antenna design (including phased arrays, metamaterials, and compact structures), AI integration in electromagnetic systems, and medical sensor technologies. Recent projects include drone-based electromagnetic signature analysis, vibration energy harvesting for pacemakers, and smart healthcare systems for posture recognition and surgical site infection monitoring. Key projects include DRONES: Drone-Obtained Electromagnetic Signatures (2024–2028), Sensor Intelligence for Healthcare and Sports (2022–2027), and DeepBone (2021–2022), which explored deep learning for surgical infection detection. His work also bridges machine learning and electromagnetic design, with breakthroughs in surrogate modeling and automated antenna optimization. Ming Shen has supervised 6 PhD students and published over 160 peer-reviewed articles. Notable contributions include AI-assisted NLOS sensing, ultra-wideband antenna innovations, and medical applications such as electrical impedance-based bone healing assessment.

Hans Martin Kjerمشاهده پروفایل

دانشیار

• Ultrasound Imaging
• Super-Resolution Imaging
• Biomedical Image Analysis
• +۷ مورد دیگر

Hans Martin Kjer is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the UltraSound and Biomechanics group within the Visual Computing Center and the Center for Fast Ultrasound Imaging. His research bridges engineering and medical imaging, with a strong emphasis on developing and validating advanced ultrasound techniques for biomedical applications. Research Interests: His work focuses on super-resolution ultrasound imaging, microvascular analysis, 3D reconstruction of biological structures, and image registration. He applies computational methods to improve the resolution and accuracy of ultrasound, particularly in renal and lymph node vasculature imaging. His research contributes to the UN Sustainable Development Goals in health and well-being through innovative diagnostic tools. Publication Trends: Over the past several years, Kjer has consistently published in high-impact journals and conferences in biomedical engineering and imaging. His recent work emphasizes the validation of super-resolution ultrasound against micro-CT, realistic 3D blood flow simulation, and the application of AI in enhancing imaging resolution. These studies reflect a strong trend toward quantitative, reproducible, and clinically relevant imaging solutions. Scientific Contributions: While no specific awards are listed, his leadership in major research projects and frequent collaborations with leading experts in ultrasound (e.g., Jørgen Arendt Jensen) underscore his significant role in the field. Advising and Funding: Kjer serves as a supervisor and principal investigator in several funded research initiatives, including AI for Extreme Super-Resolution CT , 3DIM: 3D Imaging Center , and QIM: Center for Quantification of Imaging Data from Max IV . He mentors PhD students and collaborates across disciplines, contributing to both biomedical and materials science imaging projects. Laboratories and Teams: He is an integral member of the Center for Fast Ultrasound Imaging and the Visual Computing Center at DTU. These teams focus on cutting-edge ultrasound technologies, image processing algorithms, and multimodal imaging integration, positioning Kjer at the forefront of computational biomedical imaging in Denmark.