جستجوی استاد راهنمای دانشگاهی

Lys Sanz Moretaمشاهده پروفایل

پژوهشگر ارشد

• Molecular Biology
• Machine Learning
• Probabilistic Programming
• +۳ مورد دیگر

Lys Sanz Moreta is a Research Fellow at the University of Copenhagen, holding dual appointments as a Postdoc in the Department of Cellular and Molecular Medicine (Faculty of Health and Medical Sciences) and a Guest Researcher in the Department of Computer Science's Programming Languages and Theory of Computation group. Her work bridges molecular biology and advanced machine learning to solve complex biomedical problems. Education PhD in Computer Science, University of Copenhagen, 2022 Dr. Moreta specializes in interdisciplinary research, applying Bayesian modeling and deep learning to molecular biology challenges. Her expertise spans protein structure prediction, viral epitope analysis, and probabilistic programming, enabling cross-disciplinary collaborations between computational and biomedical fields. She emphasizes optimizing programming and mathematical skills to connect diverse knowledge domains. Her 2019-2025 publications reveal a consistent focus on integrating machine learning with structural biology and immunology, particularly through variational autoencoders and probabilistic frameworks for protein superposition, ancestral sequence resurrection, and viral T-cell epitope prediction. This research has significant implications for drug design and molecular evolution studies. Scientific Awards No major scientific awards are documented in the provided profile. While no student advising or grant details are listed, her active roles in the Glycomics Program and Programming Languages research group suggest ongoing collaborative projects. Her interdisciplinary approach facilitates knowledge transfer between computer science and biomedical research teams. She maintains affiliations with the Glycomics Program (Department of Cellular and Molecular Medicine) and the Programming Languages and Theory of Computation group (Department of Computer Science), driving innovation at the computational-biology interface.

Verena Untietمشاهده پروفایل

استادیار

• Astrocyte Biology
• Ion Homeostasis
• Neurophysiology
• +۳ مورد دیگر

Verena Untiet is an Assistant Professor and group leader at the Center for Translational Neuromedicine within the Faculty of Health and Medical Sciences at the University of Copenhagen. Her research focuses on how astrocytes influence brain function and neuronal activity through modulation of ion concentrations, with particular emphasis on chloride homeostasis. Dr. Untiet's research program investigates astrocytes as primary homeostatic cells in the central nervous system, with groundbreaking work demonstrating their role as dynamic reservoirs for chloride ions that significantly modulate inhibitory neurotransmission. She discovered that astrocytic intracellular chloride concentration is brain state-dependent, maintaining high and stable levels during sleep while exhibiting dynamic fluctuations during wakefulness. These chloride dynamics directly influence the excitation/inhibition balance in both healthy and diseased brains, with implications for understanding sleep architecture, epilepsy, and other neurological conditions. Analysis of Dr. Untiet's publication record reveals a consistent research trajectory centered on astrocyte-mediated ion homeostasis, particularly chloride dynamics. Her work bridges fundamental neurophysiology with translational applications, utilizing advanced imaging techniques like fluorescence lifetime imaging microscopy and genetically encoded bioluminescent oxygen indicators. Recent publications demonstrate increasing methodological sophistication and expanding scope, with significant recognition as evidenced by media coverage of her Science paper on oxygen imaging, which was picked up by 126 news outlets and shared by 578 X users. No specific scientific awards are explicitly mentioned in the available information As an academic mentor, Dr. Untiet supervises graduate students including Emma, who successfully defended her Master's thesis in April 2025 after just six months of productive research. Her research group maintains active collaborations with leading neuroscience institutions worldwide, reflected in her extensive co-authorship network with prominent scientists including Maiken Nedergaard, Alexei Verkhratsky, and Hajime Hirase. Dr. Untiet leads a dynamic research laboratory at the Center for Translational Neuromedicine, where her team investigates the implications of modulating astrocytic chloride on brain function. The lab actively participates in international scientific discourse, organizing seminars like the NAD//MEMBRANES//NEURO event in Aarhus and attending major conferences including the Symposium on Astrocytes in Brain Function and Disease at the Nobel Forum in Stockholm.

Milad Zamaniمشاهده پروفایل

استادیار

• Integrated Circuits
• Biomedical Engineering
• Wireless Power Systems
• +۳ مورد دیگر

Milad Zamani is a Tenure Track Assistant Professor in the Department of Electrical and Computer Engineering at Aarhus University. His research focuses on integrated circuits, biomedical engineering, and wireless power systems with applications in neural engineering and sensor systems. He leads projects such as DONUT (European Doctoral Network for Neural Prostheses and Brain Research) and CorroSense (Self-powered corrosion monitoring). His work spans topics like bio-impedance sensing for wearables, ultrasonic energy transfer for implants, and high-precision analog circuits. Recent projects include thermally-powered leakage detection systems and optogenetic implantable devices. Zamani collaborates on advanced sensor technologies, neuromorphic hardware, and biomedical device miniaturization. His lab develops low-power circuits for medical applications, combining signal processing with cutting-edge microelectronic design.

• Design Research
• Computer Supported Cooperative Work
• Participatory Design
• +۳ مورد دیگر

Tariq Osman Andersen is an Associate Professor at the Software, Data, People & Society section of the Department of Computer Science, University of Copenhagen. His research spans Design Research , Computer Supported Cooperative Work , and Participatory Design with focus on healthcare applications. Research interests include: Human-Computer Interaction for clinical contexts AI integration in radiology practices Behavioral analytics via wearables Science Technology and Society perspectives Cardiovascular event prediction Patient-centered digital health systems Recent publications demonstrate interdisciplinary focus on: AI support for radiology (Global South/North comparison) Wearable-based risk profiling for arrhythmias Clinician-AI collaboration challenges Patient-generated health data integration

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +۳ مورد دیگر

Ahmad Raeisi is a Guest Researcher in the Machine Learning section at the Department of Computer Science, University of Copenhagen. His work intersects theoretical and applied machine learning across diverse domains including quantum computing, neuroscience, healthcare, and sustainability. Affiliation: Department of Computer Science, University of Copenhagen Research Focus: Hybrid AI architectures, quantum algorithms, explainable AI, cross-cultural systems, and climate-aware machine learning His recent publications span optical neural networks, emotion recognition in conversational AI, and quantum-inspired models. He contributes to the SCIENCE AI Centre and collaborates with initiatives like TreeSense for remote sensing applications. Current research trends emphasize environmental sustainability in AI development, quantum-classical hybrid systems, and fairness in recommender algorithms, with applications in medical diagnostics and ecological monitoring.

Birger Andersenمشاهده پروفایل

استاد

• Internet of Things
• Cybersecurity
• Wireless Communication
• +۴ مورد دیگر

Birger Andersen is a Professor in the Department of Engineering Technology and Didactics at the Technical University of Denmark (DTU), specializing in Energy Technology and Computer Science. His research actively contributes to UN Sustainable Development Goals through IoT security, wireless communication, and machine learning applications. He leads multiple projects including Machine Learning for Beehives Monitoring and Towards The 6G Authentication Protocol. Andersen's research spans post-quantum cryptography for IoT devices, security vulnerabilities in autonomous vehicle infrastructures, and wireless protocol analysis (LoRaWAN/NB-IoT). His work integrates machine learning for environmental monitoring and addresses jamming attacks on critical communication systems. Key focus areas include cryptographic algorithm implementation on microcontrollers and securing next-generation 6G networks. Recent publications reveal strong trends toward hybrid post-quantum cryptography solutions for resource-constrained IoT devices and 6G authentication systems. His research emphasizes practical security implementations in wireless communication protocols, with significant attention to LoRa-based systems and autonomous vehicle infrastructures. The work consistently bridges theoretical cryptography with real-world hardware constraints. Andersen supervises PhD student Turnip, T. N. (6G Authentication Protocol) and Master's student You, L. (Nature Assessment via Remote Sensing). His research is funded through projects like Machine Learning for Beehives Monitoring (2023-2026) and Jamming Against Critical Wireless Communication (2022-2023), with industry collaborations through InnoTech - TaskForce and F2D2: The Community for Dynamic Data. He is integral to DTU's Cybersecurity Labs and contributes to the Summer School in Cyber Security. His F2D2 project builds smart city cybersecurity frameworks through the Community for Dynamic Data initiative, addressing urban security challenges while advancing green transition technologies.

Rasmus Løvenstein Olsenمشاهده پروفایل

دانشیار

• Smart Grids
• Distribution Grid Modeling
• Cybersecurity in Energy Systems
• +۵ مورد دیگر

Rasmus Løvenstein Olsen is an Associate Professor at Aalborg University’s Department of Electronic Systems, part of The Technical Faculty of IT and Design. His research focuses on smart grids, energy systems, and cybersecurity in distribution networks. Key areas include distribution grid modeling, smart meter optimization, and anomaly detection in electrical systems. Education: Not explicitly detailed in the provided text, but his academic role implies advanced degrees in Electrical Engineering. His research interests revolve around enhancing grid resilience, improving state estimation techniques, and leveraging data analytics for energy systems. He collaborates on projects like Data Analytics in Smart Distribution Grids and ReDistXAI , addressing challenges in renewable integration and cyber-physical security. Publications span anomaly detection algorithms, cyber-physical control systems, and stochastic network modeling. His work emphasizes practical applications, such as optimizing smart meter deployment and securing grid communication networks. Grants/Projects: 12+ projects, including EU-funded initiatives like Net2DG and RemoteGRID . Awards: None explicitly listed in the text. Labs/Teams: Involved in the Wireless Communication Networks and Green Digital Energy Systems research groups.

Esben Lindgaardمشاهده پروفایل

استاد

• Composite Materials
• Wind Turbine Engineering
• Fracture Mechanics
• +۳ مورد دیگر

Esben Lindgaard is a Professor at the Department of Materials and Production, Faculty of Engineering and Science, Aalborg University. His research focuses on composite materials, fracture mechanics, and fatigue-driven damage in wind turbine structures. Projects: CIRCWIND (Circular material solutions for wind turbines), MulFat (Multiscale fatigue modeling), MADE4WIND (Innovative floating wind components). Research: Specializes in delamination behavior, crack propagation, and computational modeling of composite structures under static and fatigue loading. His recent work explores deep learning for delamination traction fields, cohesive element fatigue modeling, and binder-stabilized fabric forming simulations. Collaborates extensively with industry partners and contributes to open-access datasets and software tools. Awards: AAU Talent Project (2018), honored for wind turbine blade defect research (2020). Expertise: Composite material failure, multiscale modeling, industrial validation, and wind energy structural integrity.

Mehdi Shafiei Joudمشاهده پروفایل

استادیار

• Satellite Geodesy
• Machine Learning Applications
• Geophysical Modeling
• +۷ مورد دیگر

Dr. Mehdi Shafiei Joud is an Assistant Professor in the Department of Sustainability and Planning at Aalborg University , affiliated with the Technical Faculty of IT and Design. His research focuses on integrating satellite geodesy, machine learning, and remote sensing to advance environmental monitoring and Earth system dynamics analysis. Key projects include developing the Real-Time Earth Monitoring and Prediction Systems (REMPS) to assess climate-related risks like land subsidence and sea-level rise. Education : PhD in Satellite Geodesy (KTH Royal Institute of Technology, 2018) MSc in Geographical Information Systems (K.N. Toosi University of Technology, 2007) MSc in Geodesy (K.N. Toosi University of Technology, 2005) BSc in Surveying Engineer and Geoinformatics (K.N. Toosi University of Technology, 2002) His work emphasizes post-glacial rebound modeling , satellite gravity missions (GRACE/GRACE-FO) , and machine learning-driven data assimilation . Research outputs span interdisciplinary topics such as hydrological monitoring, crustal stress prediction, and climate resilience infrastructure. He actively contributes to UN Sustainable Development Goals related to environmental sustainability and education. Grants & Advising : No explicit grants or advisees listed in the text. Labs/Teams : None explicitly mentioned, though his REMPS initiative implies collaborative computational and geospatial teams.

Christian Vedelمشاهده پروفایل

استادیار

• Economic History
• Climate Change
• Natural Disasters
• +۳ مورد دیگر

Christian Vedel is an Assistant Professor in Economic History at the Department of Economics, University of Southern Denmark. His research focuses on how changing geographies shape living conditions, particularly in the context of climate change and natural disasters. He employs causal inference techniques on historical data to explore these dynamics. Vedel also held an external position with the Municipality of Copenhagen from March 2018 to June 2019. His research interests span economic history, business history, and the application of statistical methods to historical datasets. Notable projects include comparative studies of Danish and Irish creameries, analyses of firm-level productivity under energy shocks, and the impact of geographic factors on economic development. He has contributed to datasets like the HISCO codes for Danish Census data. Vedel has received the New Researcher Prize in 2023. His teaching includes courses on Microeconomics and Business History. He actively engages in academic activities, delivering lectures on topics like railways and historical occupational standardization at multiple conferences in 2024.

Anja Bechmannمشاهده پروفایل

استاد

• Digital Media Studies
• Social Media Communication
• Computational Social Science
• +۵ مورد دیگر

Anja Bechmann is a Professor in Media Studies and Director of DATALAB – Center for Digital Social Research at Aarhus University. Her work focuses on digital and social media communication, collective behavior, and algorithmic platforms, leveraging computational social science and machine learning. She leads major research initiatives like the Independent Research Fund Denmark’s 'Social Media Influence' project and contributed to EU projects like NORDIS and EDMO. Recognized as a member of the Royal Danish Academy of Sciences and Letters and the Academy of Technical Sciences, she advises governments and international bodies on digital policy. Her research bridges academia and policy, addressing misinformation, platform governance, and AI ethics. Education background: Ph.D. in Media Studies (implied by role and publications). Key affiliations include DATALAB, EDMO (executive board member), and advisory roles at Jyllands-Posten and Nordjyske Holding. She has disclosed no direct industry funding but engaged with tech companies via data grants and policy dialogues, emphasizing independent research. Her work spans interdisciplinary methods, including API data collection, ethnography, and survey design adapted to digital contexts. Research interests emphasize algorithmic accountability, democratic impacts of big tech, and disinformation dynamics. Recent projects analyze misinformation exposure across European media systems, AI-generated content detection, and gender representation in voice assistants. She has published extensively on social media infrastructure, digital governance, and ethical AI frameworks. Awards highlight her contributions to Danish and European scientific communities, including the 2019 Thinker in Residence honor by the Royal Flemish Academy. Grants and collaborations include funding from Danish and EU agencies (e.g., Horizon 2020), and she co-founded the European Information and Media Fund (EMIF) to support independent research. Her advising spans policy drafts for the European Parliament, Ministry of Defense, and cultural agencies, reflecting her role as a bridge between academia and public policy. Labs/Teams: Leads DATALAB and collaborates with EDMO and NGI Forward initiatives. Current projects (e.g., OPINION, SOMA) address automated opinion analysis, disinformation, and digital media observatories. Her work often involves international partnerships, emphasizing Nordic and EU-wide research networks.

Lei Youمشاهده پروفایل

استادیار

• Mathematical Optimization
• Distributed Data Analytics
• Data-Driven Optimization
• +۹ مورد دیگر

Lei You is an Assistant Professor in Applied Mathematics at the Department of Engineering Technology, Technical University of Denmark (DTU), located in Ballerup, Denmark. With a Ph.D. in Computer Science specializing in Mathematical Optimization from Uppsala University (2019), Dr. You has established expertise in distributed data analytics, data-driven optimization, and machine learning interpretability. Dr. You's research focuses on the theories of interpretability and efficiency of machine learning models toward On-Device AI. Key areas of investigation include: Strategies to streamline complex models without performance loss Unraveling the intricate mechanisms of decision-making models Understanding the synergy between model simplification and explainability Distributed data analytics and federated learning systems Optimization techniques for wireless communication and edge computing Dr. You's recent publications demonstrate a strong focus on counterfactual explanations, optimal transport theory, neural network pruning, and multi-objective optimization for federated learning. The research spans applications from explainable AI to 6G-satellite systems and energy-efficient base station management. A notable trend is the integration of causal inference and distributional analysis in machine learning optimization problems, with significant contributions to federated learning efficiency and robustness. Scientific awards and recognition: Doctoral Dissertation Award for Operations Research in Telecommunications and Network Analytics awarded by INFORMS in 2020 Dr. You actively supervises student projects and research activities, with recent supervisions including work on remote sensing assessment, network pruning techniques, Microsoft Graph API optimization, and applications of large language models in business contexts. Current research projects include 'Machine Learning for Beehives Monitoring' and 'InnoTech - TaskForce,' focusing on practical applications of machine learning in environmental monitoring and digital transformation. Dr. You maintains active industry connections through previous roles at Bolt, Wolt (Doordash), and The Boston Consulting Group, ensuring research relevance to real-world challenges.

Morten Rieger Hannemoseمشاهده پروفایل

استادیار

• Computer Vision
• Medical Imaging
• 3D Reconstruction
• +۳ مورد دیگر

Morten Rieger Hannemose is an Assistant Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His research focuses on computer vision, medical imaging, 3D reconstruction, and AI-driven healthcare solutions. He is actively involved in projects like 'Fighting Cancer with Generative AI' and 'Decision support AI for Skin Lesions,' supervising multiple PhD students in these domains. His work contributes to UN Sustainable Development Goals related to health and innovation. Research interests include neural networks, medical image analysis, and computational imaging. Notable achievements include developing methods for digitizing material appearances and estimating diagnostic difficulty in skin lesion diagnosis. He collaborates internationally on topics such as monocular 3D pose estimation and multimodal data fusion. Morten has supervised PhD students in areas like crowd counting through remote sensing, generative AI for cancer diagnostics, and diffusion models for image segmentation. His projects often bridge theory and application, such as creating the 'Sportspose' 3D sports pose dataset. His email is mohan@dtu.dk , and his website is mortenhannemose.github.io .

Morten Mørupمشاهده پروفایل

استاد

• Machine Learning
• Tensor Decomposition
• Bayesian Inference
• +۲ مورد دیگر

Morten Mørup is Professor at DTU Compute, Technical University of Denmark. His research develops machine learning methods for life sciences, focusing on tensor decompositions, Bayesian inference, and complex network analysis. Education: PhD from DTU Informatics (2008) with research visits to Stanford and UC Berkeley. Research Expertise: Unsupervised learning, neuroimaging data analysis, and statistical network modeling applied to neuroscience and educational analytics. Awards: EliteForsk travel scholarship (2006), Lundbeck Foundation Fellowship (2012), and Ingeborg og Leo Dannins Scholarship (2021). Recent Publications focus on graph representations, educational data mining, and speech separation models.

Yuan Liمشاهده پروفایل

پژوهشگر ارشد

• Predictive Control
• Power Electronics
• Microgrid
• +۴ مورد دیگر

Yuan Li is a Postdoctoral Researcher at the Applied Power Electronic Systems group within the Faculty of Engineering and Science at Aalborg University, Denmark, working under the supervision of Professor Frede Blaabjerg. Her research focuses on advanced control methodologies for power electronic systems and microgrids. Her primary research interests include: Predictive Control Power Electronics Microgrid Battery Energy Storage Model Predictive Control DC/DC Converters Transients Dr. Li specializes in model predictive control (MPC) applications for DC microgrids and power converters, with recent work emphasizing transient response optimization, prediction horizon design, and integration of neuromorphic computing for grid resilience. Her publications reveal a strong trend toward machine learning-enhanced control systems and hardware-efficient implementations for renewable energy integration. She serves as Principal Investigator for the project "Dynamic Stabilization of DC Microgrids Based on Model Predictive Control of Point-of-Load Converters" (2021-2024), securing significant research funding to develop MPC algorithms for DC microgrid stabilization. Her collaborative network includes prominent researchers like Frede Blaabjerg, Sahoo, and Dragicevic across multiple high-impact publications.