Find Academic Supervisors

• Antenna Engineering
• 6G/5G Communications
• Metamaterials
• +6 more

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.

Yan ZhaoView profile

Associate Professor

• Anomaly Detection
• Autoencoder
• Attention (Machine Learning)
• +5 more

Yan Zhao 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 data engineering, science, and systems, with a particular emphasis on anomaly detection, machine learning, and spatio-temporal data analysis. He holds a Ph.D. in Computer Science (specific education details not explicitly provided). Research Interests: Dr. Zhao's work spans anomaly detection, autoencoders, attention mechanisms, preference learning, multivariate time series, and computational efficiency. His research often integrates machine learning with real-world applications in spatial crowdsourcing, trajectory analysis, and data privacy. Publications: With 72+ publications, his recent work emphasizes spatio-temporal prediction frameworks, federated learning, and efficient time series analysis. Notable contributions include frameworks for continuous learning on streaming data and privacy-preserving clustering in spatial crowdsourcing. Grants & Supervision: He has supervised one Ph.D. student and actively contributes to research grants focusing on data engineering and smart systems. His work bridges theoretical advancements with practical applications in transportation, social networks, and IoT.

• Information Retrieval
• Data Science
• Text Analytics
• +5 more

Christina Lioma is a Full Professor at the Department of Computer Science (DIKU), University of Copenhagen . She has held academic positions including Associate Professor (2014-2018) and Freja Fellow/Assistant Professor (2012-2013) at the same institution. M.Hons (University of Glasgow, 2001) M.Sc. (University of Manchester, 2003) Ph.D (University of Glasgow, 2007) Her research focuses on Information Retrieval , Text Analytics , and Recommender Systems within Applied Machine Learning and Natural Language Processing . Recent work examines fairness-relevance tradeoffs in recommendation systems and neural mechanisms for knowledge conflict tracing. Recent publications in Nature Communications and top conference proceedings (WWW, SIGIR) explore hybrid computation architectures, brain-based language generation, and fairness evaluation metrics. She actively participates in academic conferences as organizer and speaker, including the European Conference on Information Retrieval.

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

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

• Wind Energy Systems
• Renewable Energy
• Energy Systems Engineering
• +4 more

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.

Richard Martin LusbyView profile

Associate Professor

• Operations Research
• Mathematical Optimization
• Logistics and Supply Chain Management
• +5 more

Richard Martin Lusby is an Associate Professor in the Department of Technology, Management and Economics at DTU Management, Technical University of Denmark. His academic work focuses on Operations Research with specialization in mathematical optimization applied to transportation and energy systems. His research spans railway scheduling, container logistics, maritime transport, and energy storage optimization. Department: Department of Technology, Management and Economics Section: Section for Operations Research, Division for Management Science Location: Building 358, Room 145A, Academy Road, 2800 Kgs. Lyngby, Denmark Contact: rmlu@dtu.dk, Tel: 45253084 ORCID: 0000-0002-8652-6219 Professor Lusby's research interests center around mathematical optimization methods applied to complex transportation and energy systems. His work spans railway scheduling problems, container logistics optimization, maritime transport operations, and energy storage scheduling. He develops advanced algorithms including Lagrangian Relaxation, Benders decomposition, and stochastic programming approaches to solve challenging real-world problems in these domains. His research has practical applications in improving efficiency and sustainability in transportation networks and energy systems. His recent publications demonstrate a strong focus on optimization techniques for transportation systems, particularly in railway operations and container logistics, as well as energy storage scheduling. The research shows a consistent pattern of developing sophisticated mathematical methods to address complex operational challenges in transportation networks, with increasing attention to sustainability considerations in maritime shipping and energy systems. Professor Lusby has been involved in significant research projects including the SWITCH project (Sustainable Network Design - Electrifying Container Shipping), which received funding from Den Danske Maritime Fond. This project explores how battery-powered ships could replace fossil fuels in global shipping through mathematical modeling and AI-driven optimization. He also supervises PhD students, including Georgios Vassos who completed a thesis on "Procurement Policy Analysis in Intermodal Container Logistics". His work has practical implications for improving efficiency in transportation networks, reducing environmental impact in maritime shipping, and optimizing energy systems operations. Through his research, Professor Lusby contributes to both theoretical advances in optimization methods and their practical applications in critical infrastructure sectors.

• Precision Agriculture
• Computer Vision
• Neural Networks
• +3 more

Ameer Tamoor Khan serves as a Postdoctoral Researcher (Research Fellow) in the Section for Crop Sciences within the Department of Plant and Environmental Sciences at the University of Copenhagen's Faculty of Science. Based at the Taastrup campus (Højbakkegård Allé 13, 2630 Taastrup), his work bridges agricultural science and computational intelligence with direct contact via atk@plen.ku.dk and +4535327865. His research centers on AI-driven solutions for agricultural challenges, specializing in computer vision applications for precision farming. Key focus areas include pest and weed detection using YOLO architectures (v8/v11), leaf phenotyping analysis, neural network-based supply chain optimization, and evolutionary computation for model-free systems. This interdisciplinary work integrates deep learning, operational research, and agricultural engineering to enhance food security and farming efficiency through real-time computational approaches. Analysis of his 2025 publications reveals a dominant trend in applying computer vision to agricultural robotics, particularly in multi-crop monitoring systems. His work consistently connects neural dynamics with practical farming applications, spanning from cotton weed management to portfolio risk optimization, demonstrating cross-domain versatility in computational intelligence. No scientific awards were documented in the source material. Information regarding student supervision or research grant acquisition was not provided in the available records. While specific laboratory affiliations weren't detailed, his research output indicates active collaboration within computational agriculture networks, particularly with co-authors Jensen and Mirjalili across multiple institutions.

Ashraf Fathi Khalil SulaymanView profile

Associate Professor

• Renewable Energy Systems
• Power System Operation and Control
• Artificial Intelligence in Energy
• +3 more

Ashraf Fathi Khalil Sulayman is an Associate Professor at the Department of Engineering Technology and Didactics Energy Technology and Computer Science, DTU Engineering Technology, Technical University of Denmark (DTU). He holds a PhD from the University of Birmingham (2012), and BSc/MSc degrees from the University of Benghazi, Libya (2000/2006). Previously, he served as Head of the Electrical Engineering Department at the University of Benghazi (2012–2015) and was an Assistant Professor at Universiti Teknologi Brunei (2018–2021). His research focuses on renewable energy systems, microgrid optimization, and AI-driven power system control. Key areas include time delay systems, PV integration, and machine learning applications in grid management. His work aligns with UN Sustainable Development Goals, particularly energy sustainability and climate action. Education: PhD, University of Birmingham, UK (2012) MSc, University of Benghazi, Libya (2006) BSc, University of Benghazi, Libya (2000) Research Interests: His interdisciplinary work bridges electrical engineering and sustainable energy, emphasizing: - Renewable energy systems integration - Smart grid technologies - AI/ML for power system control - Microgrid stability and optimization - Photovoltaic system performance analysis Recent Research Trends (2025): Publications highlight advancements in hybrid microgrid control using neural networks, DC microgrid communication protocols, and multi-objective optimization for inverters. Studies also analyze PV impact on distribution networks and model Morocco's future energy landscape, emphasizing renewable dominance and carbon reduction strategies. Advising & Grants: No specific grants or student advisees explicitly listed in the provided text. Labs/Teams: Not explicitly mentioned in the text.

• Machine Learning
• Natural Language Processing
• Information Retrieval
• +3 more

David Robert Shannon is an Instructor at the Department of Computer Science, University of Copenhagen. He contributes to teaching and research within the Machine Learning section, which participates in the SCIENCE AI Centre. University: University of Copenhagen Department: Department of Computer Science Section: Machine Learning His research interests span theoretical and applied machine learning, focusing on natural language processing, information retrieval, medical image analysis, computational biology, and quantum computing applications. He utilizes the department's powerful compute cluster for projects involving AI's environmental impact, quantum algorithms, and biomedical data modeling. Recent publications highlight work in quantum-inspired neural networks, sustainable AI, medical diagnostics, and cross-cultural computational frameworks. Key themes include ethical considerations in AI, hybrid quantum-classical systems, and multimodal data analysis. David collaborates with the Machine Learning section and SCIENCE AI Centre, leveraging resources like TreeSense for remote sensing and deep learning of global tree resources. The section's activities range from foundational research to applications in sustainability and biological data modeling.

Dario ParigiView profile

Associate Professor

• Structural Engineering
• Sustainable Construction
• Timber Reuse
• +5 more

Dario Parigi serves as Associate Professor, Head of Division, and Head of Research Group within the Department of the Built Environment at Aalborg University's Faculty of Engineering and Science. He leads the Sustainable Structures, Materials and Construction Research Group and maintains an active research profile with 43+ publications and 5 major projects including the active Instant Urbanism initiative. His work bridges structural engineering, sustainable design, and computational methodologies. Parigi's research spans timber reuse optimization , reciprocal structural systems , and multi-dimensional form-finding , with recent focus on carbon metrics for reused timber and kinetic adaptive facades. His fingerprint analysis reveals strong connections to Design Process (100%), Form Finding (83%), and Reciprocal Structures (64%), demonstrating consistent thematic focus across two decades of work. Analysis of his 15 most recent publications shows increasing emphasis on circular construction (2021-2022), with 60% addressing timber reuse challenges and optimization methodologies. Earlier works (2015-2018) established foundational frameworks for reciprocal structures and computational design tools, evolving toward integrated sustainability metrics in recent output. 5 million DKK research grant for timber reuse (2024) Det Obelske Familiefond funding for Utzon(x) Research-by-design Multiple media features in Danish press (2024) regarding sustainable construction research As Principal Investigator for projects like Lightweight structural elements and Skolernes Indeklima, Parigi secures significant research funding while mentoring junior researchers through 14 documented activities including conference presentations and workshop leadership. His Urban Tranquility project demonstrates interdisciplinary collaboration across environmental and structural domains. Parigi directs the Sustainable Structures, Materials and Construction Research Group where his team develops innovative approaches to structural reciprocity and material reuse. Current focus includes the Parigi Mechanism for adaptive facades and computational frameworks for minimal-waste timber layouts, with recent press coverage highlighting real-world applications of their research.

Evangelos BoukasView profile

Associate Professor

• Deep Learning
• Unmanned Aerial Vehicles
• Computer Vision
• +5 more

Evangelos Boukas is an Associate Professor in the Department of Electrical and Photonics Engineering at the Technical University of Denmark (DTU), Faculty of Engineering. His research focuses on autonomous robotic systems for marine vessel inspection, confined space navigation, and man overboard detection using UAVs and deep learning techniques. His core research interests include Deep Learning , Unmanned Aerial Vehicles , Computer Vision , and Robotics , applied to solve challenges in marine robotics and autonomous inspection. He develops uncertainty-aware navigation systems, probabilistic segmentation methods, and reinforcement learning frameworks for real-world deployment in complex environments. Recent publications demonstrate a strong trend toward GPU-accelerated deep learning for confined space inspection, with emphasis on uncertainty estimation, domain adaptation, and real-time processing. Key application areas include marine vessel classification, ballast tank inspection, and search-and-rescue operations, often leveraging multimodal sensor fusion. Dr. Boukas actively supervises five PhD students across major research projects: Probabilistic Deep Learning for Autonomous Aerial Inspection (2025-2028) Aerial Robots Design for Confined Spaces Inspection (2024-2027) Active Semantic Segmentation for Confined Spaces using Aerial Robots (2024-2027) Autonomous Aerial Robotics for Man Overboard Incidents (2023-2026) Visual and Multimodal Perception for Construction Robotics (2023-2026) He leads the "Perception and Cognition for Autonomous Systems" research group at DTU, which develops advanced perception algorithms for robots operating in challenging environments like ship ballast tanks and marine vessels, contributing to UN Sustainable Development Goals for industry innovation and marine conservation.

• Transportation Systems
• Behavioral Modeling
• Machine Learning
• +5 more

Rico Krueger is an Associate Professor at the Department of Technology, Management and Economics within the Transport Division at the Technical University of Denmark (DTU). His research focuses on developing models and technologies at the intersection of behavioral modeling, machine learning, and simulation to improve human-centric and sustainable transport systems. Key areas include travel demand forecasting, multimodal systems, and emerging technologies like virtual reality for understanding human decision-making. Education: Ph.D. in Civil and Environmental Engineering from UNSW Sydney (Australia), followed by postdoctoral research at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He holds a prestigious European Research Council Starting Grant (2024–2029) for his project IMMERSION , exploring human decision-making through choice and process data integration. Research emphasizes interdisciplinary approaches, combining data science with behavioral theories to address challenges in urban mobility, sustainability, and public health. His work bridges theoretical advancements with practical applications, such as optimizing policy interventions during pandemics and enhancing ride-sourcing systems. Awards: ERC Starting Grant for IMMERSION project. Grants: Focus on behavioral modeling and pandemic-related transport policies. Labs/Teams: Active in DTU’s Intelligent Transport Systems Section, leading interdisciplinary projects on mobility and health.

Baoze WeiView profile

Associate Professor

• Microgrid Control
• Power Electronics
• Renewable Energy Integration
• +5 more

Baoze Wei is an Associate Professor at Aalborg University's Department of Electric Power Systems and Microgrids, part of the Faculty of Engineering and Science. His research focuses on advanced control strategies for power electronics, microgrid stability, and energy management systems. He leads and collaborates on projects such as the Digital Twin-based Reliability Framework for Aviation Systems and Holistic Optimization of Green Fuel-Powered Microgrids. Key contributions include work on distributed energy systems, fault-tolerant architectures, and predictive maintenance for power electronics. His research emphasizes practical applications in renewable integration, smart grids, and industrial electrification. Wei has supervised one PhD student, Q. He, and contributed to projects funded by entities like Horizon JU and Huawei. Notable collaborations include work on hybrid-electric aircraft systems (HECATE) and advanced control algorithms for distributed converters. His research spans technical areas such as voltage source inverters, uninterruptible power systems (UPS), and DC shipboard microgrids. His publications reflect expertise in model predictive control, energy trading strategies, and condition monitoring. Current research trends include data-driven lifetime prediction for power electronics components and optimization of multi-energy systems. He actively participates in international conferences, contributing to both theoretical advancements and real-world system implementations.

Alireza RezaniakolaeiView profile

Associate Professor

• Energy Harvesting
• Thermoelectrics
• Biomedical Engineering
• +4 more

Alireza Rezaniakolaei is an Associate Professor at Aalborg University's Thermal Engineering department within the Faculty of Engineering and Science. His research focuses on low-power energy harvesting, thermoelectric systems, and biomedical energy applications. He leads several projects on piezoelectric and thermoelectric energy harvesting for medical devices like leadless pacemakers, and has contributed to improving thermal efficiency in heating systems and industrial waste heat recovery. Key projects include the development of self-powered cardiac pacemakers using in vivo heart motion and optimizing thermoelectric generators for low-temperature applications. His work integrates experimental and numerical methods to enhance energy conversion efficiency in both biomedical and industrial contexts. Over 120 publications reflect his expertise in renewable energy systems, material science, and energy management. Notable collaborations involve the design of magnetic heart valves and experimental animal studies for valvular heart disease treatments. His research bridges engineering disciplines, addressing challenges in sustainable energy and medical device innovation.

Samuel Simon ArayaView profile

Associate Professor

• Fuel Cell Technology
• Renewable Energy Systems
• Electrochemical Systems
• +3 more

Samuel Simon Araya is an Associate Professor at Aalborg University's Faculty of Engineering and Science, specializing in Thermal Engineering. His research focuses on Proton-Exchange Membrane Fuel Cells (PEMFC), High-Temperature PEM Fuel Cells (HT-PEMFC), and Electrolysis technologies. He leads projects like 'Data-driven Diagnosis for High Temperature PEM Fuel Cells' and 'HySTrAm: Hydrogen Storage and Transport using Ammonia.' Key areas include fault diagnosis via Electrochemical Impedance Spectroscopy (EIS), CFD modeling of electrolysis cells, and sustainable energy systems. He supervises PhD students in fuel cell durability, power-to-fuel systems, and green ammonia production. His work aligns with UN SDGs for clean energy and climate action. Notable collaborations involve developing hydrogen storage solutions and optimizing PEM electrolyzers. His recent publications address multi-objective optimization of energy systems, CFD analysis of multiphase flows, and online fault detection methods. Araya has contributed to over 84 research outputs, including journal articles, conference papers, and reviews. His research bridges theoretical models with practical applications in green hydrogen production, fuel cell performance, and electrochemical systems.