Farshad Moradi is a Professor at the Department of Electrical and Computer Engineering at Aarhus University, specializing in neuromorphic engineering, spintronics, and biomedical device design. His work focuses on integrating advanced materials and circuits for applications in neural interfaces, energy-efficient computing, and wireless biomedical systems. Research Interests include: Spintronic-based neuromorphic computing architectures Ultra-low power analog/mixed-signal integrated circuits Ultrasonically powered implantable medical devices Neural signal processing and seizure detection systems Wireless energy transfer and structural health monitoring Key Projects (2016-2026): SPICE: Spintronic-Photonic Integrated Circuit Platform PHOTON-NeuroCom: Photonic-assisted Neuromorphic Computing Neuro-Sense: Flexible bioinspired neuroprostheses CorroSense: Self-powered corrosion monitoring HERMES: Hybrid Enhanced Regenerative Medicine Systems Recent innovations include: Ultrasonically powered optogenetic implants Low-power neural amplifiers for deep-brain interfaces Spin-torque nano-oscillator-based neuromorphic hardware Energy harvesting systems for structural monitoring
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نمایش ۱۶-۳۰ از ۳۲۸ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
Mehdi Mehraliمشاهده پروفایل
پژوهشگر
- Hydrogel
- Geopolymer Engineering
- Graphene
- +۴ مورد دیگر
Mehdi Mehrali serves as a Senior Researcher in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), Denmark. His research spans advanced materials engineering with focus on sustainable construction and biomedical applications. Research Focus: Dr. Mehrali's work centers on geopolymer engineering , hydrogel development , and nanomaterial reinforcement for 3D-printed construction. His fingerprint reveals expertise in biomaterials (39%), graphene (45%), and phase change materials (27%), contributing to UN Sustainable Development Goals through sustainable infrastructure solutions. Publication Trends: Recent work demonstrates convergence of civil engineering with AI-driven material design (e.g., machine learning for geopolymer extrusion) and biomedical applications (e.g., antibacterial hydrogels). His 2025 publications show strong emphasis on multifunctional composites with self-sensing capabilities and robotic integration. Awards & Recognition: 6 similar researcher profiles identified in global networks 5 Mendeley readers for recent work Featured in 3 X (Twitter) discussions Supervision & Grants: Actively supervises three PhD candidates on 3D-printed construction materials while collaborating on major projects including COOLBATTERY (€2.1M) and RESTORATIVE grid-scale energy storage. His research attracts significant downloads (321 for hydrogel review) and citations (30+). Laboratory Focus: Leads research in Materials and Surface Engineering at DTU's Produktionstorvet facility, specializing in printable geopolymers, hydrogel robotics, and cement-based smart materials for sustainable construction.
Shashi Raj Pandeyمشاهده پروفایل
استادیار
- Network Economics
- Game Theory
- Wireless Networks
- +۷ مورد دیگر
Shashi Raj Pandey serves as Assistant Professor in the Department of Electronic Systems at Aalborg University's Technical Faculty of IT and Design, Denmark. His research is anchored in the Connectivity section and Connectivity Classique-Center for Classical Communication in the Quantum Era, with office location at Fredrik Bajers Vej 7C, C1-111, 9220 Aalborg Øst. His core research spans Network Economics, Game Theory, and Wireless Networks, with specialization in Decentralized Machine Learning and Semantic/Goal-oriented Communications. Current work integrates Digital Twin technologies with 6G systems for industrial automation and earth observation, emphasizing resource-efficient protocols for Internet of Things and edge intelligence applications. Recent publications (2024-2025) reveal a clear trajectory toward AI-6G convergence, featuring semantic communications for satellite imaging, game-theoretic network resource allocation, and digital twin implementations for autonomous systems. Key themes include communication efficiency in distributed learning and physical-digital world integration. Notable recognitions include: Best PhD Thesis Nominee (2021) Excellent Paper at Korea Software Congress, KIISE 2021 Student Best Paper Award at APNOMS 2019 Best Paper at Korea Software Congress, KIISE, 2018 Brain Korea 21st Century Plus Fellowship Academic service includes external PhD examination for EU SNS projects and peer review for premier conferences (AAAI, ICLR, ICML). His lab work within the Connectivity Classique-Center explores classical communication frameworks applicable to quantum-era networks, with focus on semantic information theory and decentralized network architectures.
Mostafa Mohammadiمشاهده پروفایل
استادیار
Mostafa Mohammadi is an Assistant Professor at the Department of Health Science and Technology, Aalborg University, affiliated with the Faculty of Medicine and Center for Rehabilitation Robotics. His research focuses on neurorehabilitation robotics, human-computer interaction, and telerehabilitation technologies for individuals with disabilities. Ph.D. in Biomedical Engineering (Aalborg University, 2018-2022) M.Sc. in Biomedical Engineering (Polytechnic University of Milan, 2015-2017) B.Sc. in Mechanical Engineering (Sharif University of Technology, 2011-2015) His work spans exoskeleton design, assistive robotics, and innovative interfaces like tongue-computer interfacing. Recent projects include the eMotivo digital health solution funded by Innovation Fund Denmark and intelligent tendon-driven exoskeletons for severe disabilities. Research outputs (33 total) emphasize adaptive robotics, motor impairment solutions, and wearable technologies. Collaborations span robotics, biomedical engineering, and clinical disciplines. Teaching includes courses in rehabilitation robotics, human-computer interaction, and digital systems for biomedical engineering. Scientific activities align with UN Sustainable Development Goals for quality education and reduced inequalities. Notable contributions include advancements in myoelectric interfaces and neurorehabilitation technologies.
Tuukka Ruotsalo serves as Associate Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His research bridges human cognition with computational systems through brain-computer interfaces and physiological computing. As Academy Research Fellow at University of Helsinki (2019-2024), he maintained dual institutional affiliations while leading cutting-edge work in neuro-linguistic modeling and affective relevance. His research focuses on brain-computer interfaces for information retrieval , where he pioneers methods to decode cognitive states from neural signals to improve search systems. Key areas include affective relevance modeling that integrates emotional states into search algorithms, and neuro-linguistic reconstruction that translates brain activity into language. His work on fairness-relevance tradeoffs in recommender systems established Pareto frontier evaluation frameworks now widely adopted in ethical AI research. Recent publications demonstrate how physiological signals like EEG and galvanic skin response can create more adaptive human-information interaction systems. Ruotsalo's scientific recognition includes the prestigious Academy Research Fellow position. His publications in IEEE Transactions on Human-Machine Systems , Journal of the Association for Information Science and Technology , and Communications Biology reveal growing interdisciplinary impact. His advising spans cognitive neuroscience and machine learning students, with notable collaborations across the SCIENCE AI Centre. Current projects include the TreeSense initiative for remote sensing of global tree resources and development of quantum-inspired neural architectures. His lab leverages the department's powerful compute cluster for large-scale physiological data analysis.
Per Lynggaard is a Professor of Electronics at the Technical University of Denmark (DTU) , leading the B.Eng. program in Electronics. Previously, he held an Associate Professor role at Aalborg University, combining academic excellence with a robust industrial career in technical-scientific research and development. Education: M.Sc. in Electrical Engineering and Information Technology (EE and IT) Ph.D. in Electronics from Aalborg University Research Interests: Focus on Integrated Circuit Design, Wireless Sensor Networks (WSN), Machine Learning, IoT, and Smart City Technologies . His work emphasizes energy-efficient systems, cybersecurity in IoT, AI-driven interference mitigation, and sustainable energy harvesting solutions. He has contributed to UN Sustainable Development Goals through projects addressing smart infrastructure and environmental monitoring. Projects & Collaborations: Leads and participates in EU-funded initiatives such as InnoTech (2023–2025) for green transition solutions and TransportTech (2023–2026) for Industry 4.0 logistics. Active in cybersecurity research via projects like Jamming Against Critical Wireless Communication , aiming to protect critical infrastructure. Awards: Recognized with multiple honors and rewards during his industrial career, though specific names are not listed. His work has been cited widely, with notable impact in IoT security and energy-efficient systems. Advising & Grants: Supervises Turnip T.N. in a PhD project on 6G security protocols. Engaged in securing funding for projects like F2D2: The Community for Dynamic Data (2021–2030), focusing on dynamic data systems and cybersecurity. Labs & Teams: Collaborates in interdisciplinary teams such as the InnoTech TaskForce and F2D2 Community , advancing IoT and AI integration. His research bridges academia and industry, with outputs spanning smart cities, healthcare IoT, and sustainable energy systems.
Yan Kyaw Tunمشاهده پروفایل
استادیار
Yan Kyaw Tun is a Tenure Track Assistant Professor in the Department of Electronic Systems at Aalborg University's Technical Faculty of IT and Design, located in Copenhagen, Denmark. His research lies at the intersection of wireless communications, edge computing, and artificial intelligence, with a strong focus on next-generation networks (5G/6G), UAV-assisted systems, and intelligent resource management. His educational background includes a Ph.D. in Computer Engineering from Kyung Hee University, South Korea, where he was awarded the Best Ph.D. Thesis Award in 2021, and a Bachelor of Engineering in Marine Electrical Systems and Electronic Engineering from Myanmar Maritime University. Dr. Tun's research interests span Edge Computing , Multi-Access Edge Computing (MEC) , Resource Allocation , Unmanned Aerial Vehicles (UAVs) , Reinforcement Learning , Energy Efficiency , and Integrated Sensing and Communication (ISAC) . His work leverages AI and optimization techniques to enhance the performance of wireless networks, particularly in space-air-ground integrated systems and satellite-HAP environments. The recent publications highlight a clear trend toward intelligent and sustainable networking: the integration of STAR-RIS (Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces), Federated Learning for satellite-HAP systems, and AI-driven optimization for UAV trajectories and beamforming. These works are published in high-impact venues such as IEEE Transactions on Mobile Computing and IEEE ICC , showcasing his leadership in cutting-edge communication technologies. His scientific accolades include: IEEE ComSoc Outstanding Young Researcher Award for EMEA Region (2024) Best Ph.D. Thesis Award (2021) Student Best Paper Award at APNOMS 2019 Korea Network Operation and Management Conference Award (2020) Korea Computer Congress 2018 Award Dr. Tun is actively engaged in the academic community as an advisor and grant participant. Though no direct advisees are listed, his involvement in large collaborative projects—evidenced by co-authorship with senior researchers like Prof. Choong Seon Hong—indicates mentorship and team leadership. He has served on the editorial boards of IEEE Internet of Things Journal , IEEE Open Journal of the Communications Society , and IEEE Network , and has secured research support through participation in IEEE-organized workshops and special issues. He is a key organizer of upcoming workshops, including the 'Sustainable AI for Next-Generation Wireless Communications and Networking' at IEEE GLOBECOM 2025 and the 'Digital Twin Networks' workshop at IEEE/CIC International Communications in China 2025, reflecting his role in shaping future research directions in intelligent and green networking.
Naeem Ayoubمشاهده پروفایل
استادیار
- Machine Learning
- Computer Vision
- Robotics
- +۱۲ مورد دیگر
Naeem Ayoub is an Assistant Professor in the Department of Technology and Innovation at the University of Southern Denmark (SDU), affiliated with SDU Technology Entrepreneurship and Innovation. His research bridges computer science and engineering, focusing on intelligent systems and automation. Research Interests: His work spans machine learning, computer vision, robotics, and cyber-physical systems. He explores applications in autonomous drones, digital twins, power line inspection, and environmental monitoring. His research emphasizes real-time decision-making, energy efficiency, and anomaly detection in complex systems. The recent publications highlight a strong trend in deploying AI-driven robotics for industrial and environmental applications, particularly in infrastructure inspection and predictive maintenance. His work integrates neural networks, sensor networks, and autonomous navigation to solve practical engineering challenges. Scientific Contributions: Active contributor to 19 research outputs including journals and conference proceedings. Creator of an open-source dataset for pylon component and fault detection using machine learning. Involved in interdisciplinary collaborations across engineering, environmental science, and computer science. Advising and Grants: While no formal students are listed, he has participated in academic supervision as a censor in internal examinations. His projects suggest involvement in research grants related to autonomous systems and industrial digitalization, though specific funding details are not provided. Labs and Teams: He collaborates within research teams focused on robotics and intelligent systems at SDU, particularly in drone technology and cyber-physical systems. His work involves close collaboration with researchers in environmental monitoring, power systems, and industrial automation.
Andreas Bjerre-Nielsen is an Associate Professor at the Department of Economics and Copenhagen Center for Social Data Science (SODAS) within the Faculty of Social Sciences at the University of Copenhagen. His work bridges economics and data science to analyze education-related behavior and policies. Research Focus: School choice, digital technology in education, predictive analytics for interventions, and social network effects. Methodology: Combines econometrics with machine learning techniques to evaluate policy impacts. Research Trends: Recent publications emphasize algorithmic fairness in college admissions, socioeconomic impacts of school boundary policies, and behavioral insights from large-scale datasets. His 2025 Scientific Reports study reveals nation-scale social network dynamics. Awards and Grants: Tietgen Prize (2021) for young social science researchers 2024: Independent Research Fund Denmark grant for 'Coded Clues' project 2023: Major grant for school choice research Collaborations: Works with Danish Ministry of Children and Education through UDDanKvant unit, and collaborates with multidisciplinary researchers including Sune Lehmann and David Dreyer Lassen.
Stefan Oehmckeمشاهده پروفایل
استادیار
Stefan Oehmcke is an Assistant Professor at the Machine Learning Section of the Department of Computer Science , University of Copenhagen. His research focuses on applying machine learning techniques to environmental and geospatial analysis, particularly in forest ecology, tree monitoring, and climate impact studies. Research Trends: His recent publications emphasize deep learning for LiDAR data processing, multi-modal geospatial representation, and sustainable AI practices. Key Collaborations: Frequently collaborates with researchers in environmental science, remote sensing, and climate change (e.g., Martin Brandt, Christian Igel). Applications: Develops tools for forest biomass estimation, tree mortality mapping, and urban safety analysis using satellite imagery. While no specific educational background or scientific awards are mentioned in the provided texts, Oehmcke's work demonstrates technical innovation in AI explainability and environmental monitoring, with significant contributions to journals like Remote Sensing of Environment and Nature Communications .
Asmus Skar Christiansen is an Associate Professor in Pavement Engineering at the Department of Environmental and Resource Engineering, Technical University of Denmark (DTU Sustain). He serves as Head of Study for the Nordic Master in Cold Climate Engineering programme and lectures on pavement engineering, Arctic road construction, and foundation design. His academic career at DTU spans from Postdoc researcher (2017-2019) to Assistant Professor (2020-2023) and current Associate Professor position since 2023. His research centers on pavement technology and geotechnics with specialization in: Development of advanced testing and modeling techniques for pavements Integration of modern sensing technologies in civil infrastructure Computational mechanics for soil-structure interaction Sustainable materials for cold climate engineering Recent work demonstrates a clear shift toward IoT-enabled monitoring systems and data-driven pavement assessment, with 80% of 2023-2025 publications focusing on sensor integration and machine learning applications. Notable scientific contributions include: Creation of open-source datasets (LiRA-CD, RIVA) for road condition modeling Development of thermomechanical models for heated pavements Innovations in waste soil reuse for infrastructure He actively supervises PhD candidates across multiple projects including GREENPIPE (self-sensing pipe systems) and urban pavement analysis, while maintaining industry consultancy through COWI A/S collaborations. Christiansen also contributes to sustainable infrastructure through DTU's alignment with UN SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities).
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.
Eugene Simon Polzik is a Professor of Physics at the Niels Bohr Institute, University of Copenhagen , and the founder of the Quantum Optics Center (QUANTOP) . He currently leads the Copenhagen Center for Biomedical Quantum Sensing and has held significant roles including Head of the Quantum Optics and Atomic Physics Division (2012-2021). PhD and MSc from Leningrad University Research Interests: polzik specializes in quantum physics, focusing on quantum communication , quantum sensing , and quantum information technologies . His groundbreaking work includes: Quantum teleportation between material objects Quantum memory for light Optical radio wave detection using nanomechanical oscillators Measurements beyond Heisenberg uncertainty limits Recent Publications span quantum sensing, optomechanics, and biomedical applications. Key articles include hybrid quantum networks, squeezed light generation, and advanced magnetometry techniques. Awards: Herbert Walther Award (2020) ERC Advanced Grants (2011, 2018) Villum Investigator (2019) Knight of Dannebrog (2018) Scientific American Research Leadership (2007) Grants: polzik has secured major funding including 150 MDKK Novo Nordisk Center for Biomedical Quantum Sensing (2024-2030) and 125 MDKK for QUANTOP.
Zhanhao Zhangمشاهده پروفایل
پژوهشگر ارشد
Zhanhao Zhang is a Postdoc researcher at the Department of Applied Mathematics and Computer Science (DTU Compute) of the Technical University of Denmark, affiliated with the Scientific Computing Center for Energy Resources Engineering. His work centers on developing advanced control strategies for industrial processes with emphasis on sustainability and computational efficiency in energy-intensive sectors. His research specializes in Model Predictive Control (MPC) for time-delay systems, numerical discretization methods, and real-time implementation of cyber-physical control systems. Key application domains include zero-emission industrial processes, cement production blending operations, and oil/gas zero-discharge systems, where he bridges theoretical control algorithms with practical industrial constraints through close industry collaboration. Analysis of his 2022-2024 publications reveals consistent innovation in extending MPC methodologies to handle complex industrial realities like time delays and discontinuities while targeting environmental sustainability. His work demonstrates strong integration of computational mathematics with process engineering to solve critical challenges in carbon-intensive industries. Zhang completed his PhD project "Model Predictive Control for Zero Emission Industrial Processes" (2021-2024) under Professor John Bagterp Jørgensen at DTU, resulting in his doctoral thesis and multiple high-impact publications. This industry-collaborative project focused on developing control frameworks for decarbonizing industrial operations. As an active member of DTU's Scientific Computing Center for Energy Resources Engineering, he contributes to interdisciplinary research that applies high-performance computing and mathematical modeling to optimize energy resource extraction, processing, and utilization while reducing environmental impact.
Sune Darkner is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in the Image Analysis, Computational Modelling, and Geometry research section. His work focuses on medical image processing with particular emphasis on neuro-imaging data including MRI and PET scans. His primary research interests include Image Registration, Segmentation and Classification of Medical Image Data , with a specific focus on estimation of image similarity as his main research interest. Darkner strongly believes that the implementation of image processing algorithms should be thoroughly tested and reflect the theoretical properties as accurately as possible. His work primarily centers on neuro-imaging data such as MRI and PET. His recent publications (2024-2025) reveal a strong focus on medical image analysis, with particular emphasis on tumor volume delineation, deformable image registration with physics constraints, and applications of deep learning in medical imaging. His work spans both theoretical foundations of image processing and practical clinical applications. Darkner previously held a Post Doc position at the Technical University of Denmark from February 2009 to January 2010, demonstrating his longstanding engagement with image analysis research in the Danish academic community.