جستجوی استاد راهنمای دانشگاهی

Nico Langمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Remote Sensing
• +۴ مورد دیگر

Nico Lang is an Assistant Professor at the University of Copenhagen's Department of Computer Science, associated with the Pioneer Centre for AI and Global Wetland Centre. He holds a PhD from ETH Zurich where he developed methods for global forest structure mapping. His research bridges computer vision, machine learning, and remote sensing to address environmental challenges like deforestation monitoring and climate change mitigation. Lang's research focuses on: Developing probabilistic deep learning models for global canopy height estimation Advancing open-set recognition under adversarial conditions Creating multi-modal representation learning frameworks for geospatial data Applying computer vision to biodiversity monitoring and conservation His work frequently appears in top venues like Nature, CVPR, and ECCV. His publications show strong emphasis on: environmental applications of AI, uncertainty-aware deep learning, and global-scale geospatial analysis. Recent work explores vision-language models and fine-grained open-set recognition. Awards & Honors: Culmann Prize for outstanding doctoral thesis (2023) Outstanding Reviewer for CVPR 2023 U.V. Helava Award for best paper in ISPRS Journal (2019) Associate PhD Fellow at Max Planck ETH Center (2018) Collaborations & Labs: Directs research at the intersection of computer vision and environmental science. Key affiliations include NASA GEDI mission, Swiss Federal Institute for Forest, Snow and Landscape Research, and the Pioneer Centre for AI. Organizes workshops like FGVC at CVPR and SSL4EO summer schools.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +۳ مورد دیگر

William Henrich Due serves as a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, within the Machine Learning section. His work intersects with the SCIENCE AI Centre and leverages the department's high-performance compute cluster for research in quantum computing, sustainable AI, and medical applications. Research focuses span quantum machine learning (biomolecular simulations, photonic processors), sustainable AI systems (energy efficiency, climate impact), and clinical applications (EEG analysis, medical imaging). His recent publications reveal strong activity in quantum-classical hybrid systems, with 8/15 recent papers addressing quantum computing challenges. The work emphasizes practical implementations in medical imaging and resource-constrained environments. His research aligns with DIKU's Machine Learning section priorities including medical imaging biomarkers and sustainable computing. Key infrastructure includes TreeSense for remote sensing and the department's dedicated compute cluster. No scientific awards were explicitly documented in the provided materials. Due contributes to DIKU's teaching mission as a Lecturer while engaging with the SCIENCE AI Centre's interdisciplinary initiatives. His work connects with medical imaging applications and quantum computing infrastructure development. Active in the Machine Learning section's research ecosystem, his work intersects with medical imaging analysis and quantum computing applications, utilizing specialized resources like TreeSense for environmental monitoring.

Daniel-Ioan Stroeمشاهده پروفایل

دانشیار

• Energy Storage Systems
• Lithium-ion Batteries
• Battery Testing
• +۵ مورد دیگر

Daniel-Ioan Stroe is an Associate Professor at Aalborg University's Department of Energy, part of The Faculty of Engineering and Science. He leads the Batteries Research Group and has held visiting roles at RWTH Aachen and Czech Technical University. His research focuses on energy storage systems, lithium-ion battery modeling, and diagnostics, with over 250 peer-reviewed publications. He earned his PhD in 2014 from Aalborg University, specializing in battery lifetime modeling for virtual power plants. Education: Dipl.-Ing. in Automatics, Transilvania University of Brasov, Romania (2008) M.Sc. in Wind Power Systems, Aalborg University, Denmark (2010) PhD in Energy Engineering, Aalborg University (2014) Research Interests: Dr. Stroe's work centers on advancing energy storage technologies for grid and e-mobility applications. Key areas include lithium-ion battery performance, lifetime modeling, state estimation, and diagnostics. His projects address challenges like fast-charging protocols, impedance-based health monitoring, and sustainable battery lifecycle analysis. Awards & Grants: Best Poster Award (2024) for collaborative work on battery diagnostics Best Paper on Ecological Vehicles (2019) Lead on funded projects such as Listen2Battery (Villum Foundation), DeBatT (Innovation Fund Denmark), and BattMaxLife (EU) Labs & Teams: He directs the Batteries Research Group, collaborating internationally on initiatives like non-woven sodium-ion batteries (NOWOS) and acoustic-based battery health monitoring. His team emphasizes interdisciplinary approaches to tackle energy storage challenges.

Edlira Dushkuمشاهده پروفایل

استادیار

• Cybersecurity
• Internet of Things (IoT) Security
• Remote Attestation
• +۵ مورد دیگر

Edlira Dushku is a Tenure Track Assistant Professor at the Department of Electronic Systems, Aalborg University (Denmark), affiliated with the Cyber Security Group. She holds a PhD in Computer Science from Sapienza University of Rome and has held postdoctoral positions at DTU Compute, Denmark. Her research focuses on IoT Security, Cybersecurity, Blockchain, and Fog Computing, particularly in remote attestation and hardware security. Education: 2023: University Pedagogical Programme, Aalborg University 2016–2020: PhD in Computer Science, Sapienza University of Rome 2013: MSc in Computer Science, University of Tirana 2010: BSc in Computer Science, University of Tirana Research Interests: Dr. Dushku specializes in securing IoT devices and distributed systems through remote attestation, blockchain applications, and machine learning-based security. She explores challenges in fog computing, PUF protection, and adversarial defense mechanisms. Her work emphasizes trust management and public verifiability in IoT ecosystems. Teaching: She teaches courses like Security in IoT and Cloud Architectures (MSc) and Computer System Security (BSc) at Aalborg University. Previously, she was an academic supervisor for students with special needs at DTU. Grants & Projects: Coordinator of the EUopStart proposal for MSCA (2024) Guest editor for MDPI Sensors Special Issue on IoT Security Program chairs for multiple conferences (e.g., FMEC 2021) Lab & Collaborations: Collaborates with industry partners like Terma and Develco Products, focusing on applied IoT security solutions. Her work bridges academia and industry through co-supervised theses and real-world system implementations.

Ashutosh Dhar Dwivediمشاهده پروفایل

استادیار

• Cybersecurity
• Post-Quantum Cryptography
• Blockchain Security
• +۴ مورد دیگر

Ashutosh Dhar Dwivedi is an Assistant Professor in the Cybersecurity Group at Aalborg University, Copenhagen, Denmark. He specializes in blockchain security, applied cryptography, post-quantum cryptography, and advanced cybersecurity. His interdisciplinary research spans cryptography, IoT security, and AI-driven security analytics. Education: PhD in Cryptography, with postdoctoral research at institutions including the University of Waterloo, Technical University of Denmark, and the Polish Academy of Sciences. His pedagogical focus includes professional upskilling in cyber defense and post-quantum resilience. Research interests include post-quantum cryptographic protocols, privacy-preserving blockchain systems, and machine learning for security. His work has yielded over 50 peer-reviewed papers, including contributions to high-impact journals and conferences. Notable achievements: 2023 and 2024 Stanford University Top 2% Scientist ranking. Contributions: Editorial roles in international journals, program committees for premier conferences, and leadership in academic-industry collaborations like the Quantum Communication Infrastructure (QCI) consortium. Active in developing quantum-secure systems for national and industrial infrastructure.

Mieszko Jan Ferens Michalekمشاهده پروفایل

پژوهشگر

• Computer Science
• Artificial Intelligence
• Internet of Things
• +۵ مورد دیگر

Mieszko Jan Ferens Michalek is a Researcher at Aalborg University's Department of Electronic Systems within The Technical Faculty of IT and Design. His work focuses on cybersecurity, machine learning, and edge computing with applications in IoT and cloud-edge networks. Education: Master in Telecommunication Engineering (2022), University of Valladolid Bachelor in Telecommunication Engineering Technologies (2020), University of Valladolid Research Interests: Explores machine learning for secure IoT systems, PUF-based hardware authentication, adversarial attack modeling, and simulation environments for cloud-edge networks. Specializes in integrating quantum-resistant cryptography into IoT infrastructure through projects like QUCISAFE. Projects: QUCISAFE (2025-2027) : Develops post-quantum cryptography and QKD for critical infrastructure in cloud-edge-IoT systems. IoTalentum (2020-2025) : Advances IoT training and machine learning for secure IoT ecosystems. Key Contributions: Developed predictive adversarial systems to model attackers targeting PUFs, created the STEROCEN simulation tool for cloud-edge resource management, and proposed MetaLung architecture for secure metaverse healthcare systems.

Heather Christine Lentمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Machine Translation
• Low-resource Languages
• +۳ مورد دیگر

Heather Christine Lent is a Researcher in the Department of Computer Science at Aalborg University, affiliated with The Technical Faculty of IT and Design. Her work focuses on advancing natural language processing (NLP) for low-resource and marginalized languages, particularly Creoles, and addressing security challenges in multilingual systems. She leads the "Multilingual Modelling for Resource-Poor Languages" project (2022–2025), funded by Carlsbergfondet and Google. Her research integrates computational linguistics with ethical considerations, emphasizing equitable technology access. Notable contributions include the CreoleVal benchmark for multilingual evaluation and studies on NLP security in multilingual contexts. She collaborates internationally, including with institutions like George Mason University, and engages in community-driven dataset creation. Publications highlight themes like adversarial attacks on multilingual models, data quality audits, and cross-lingual transfer learning. She actively participates in workshops and tutorials aimed at fostering inclusive NLP practices, such as the "Connecting Ideas in Lower-Resource Scenarios" initiative.

• Machine Learning
• Adversarial Machine Learning
• Neural Network Compression
• +۳ مورد دیگر

Tong Chen is a Postdoc in the Machine Learning section at the Department of Computer Science, University of Copenhagen, focusing on theoretical and applied machine learning with applications in information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. His research spans adversarial machine learning, neural network compression, and robustness verification using polynomial and semialgebraic optimization techniques. Chen develops mathematically rigorous frameworks to enhance deep learning model efficiency, security, and reliability while addressing real-world implementation challenges. Chen's publication trajectory reveals increasing emphasis on formal verification methods and compression-robustness trade-offs, with recent work integrating semialgebraic geometry for neural network certification. This demonstrates a clear trend toward foundational approaches that bridge theoretical optimization and practical AI safety requirements. As part of the Machine Learning section, Chen contributes to the SCIENCE AI Centre and TreeSense initiative, utilizing the department's dedicated compute cluster for resource-intensive remote sensing and deep learning experiments in global environmental monitoring.

Andrea Paudiceمشاهده پروفایل

استادیار

• Machine Learning
• Optimization
• Robust Learning
• +۳ مورد دیگر

Andrea Paudice is an Assistant Professor in the Department of Computer Science at Aarhus University. Their primary affiliation is with the Department of Computer Science, located at Åbogade 34, Building 5335, Room 317 in Aarhus N, Denmark. Paudice's research focuses on theoretical and algorithmic aspects of machine learning, optimization under uncertainty, adversarial robustness, and clustering methods. Research interests include developing robust statistical learning frameworks for heavy-tailed distributions, designing optimization algorithms with provable guarantees in stochastic settings, and exploring active learning strategies for efficient label usage. Recent work emphasizes high-probability bounds for stochastic methods, median-of-means techniques, and zeroth-order optimization under budget constraints. Publications span topics like adversarial noise mitigation, margin-based active learning, and exact cluster recovery via oracle queries. While no specific grants or awards are listed, their work demonstrates contributions to foundational machine learning theory and algorithmic robustness. No lab affiliations or student advising information is included in the provided text.

Danny Bøgsted Poulsenمشاهده پروفایل

دانشیار

• Formal Methods
• Model Checking
• Cyber-Physical Systems
• +۳ مورد دیگر

Danny Bøgsted Poulsen is an Associate Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on formal methods, model checking, and cyber-physical systems with applications in security analysis and programming languages. He contributes to projects like BEO-COVID (2020–2020) and IDEA4CPS (2011–2015), which explore decision support tools and foundational cyber-physical systems. His academic background includes advanced work in formal verification and statistical model checking. Key research interests include: Formal methods for string constraints and SMT solvers Statistical analysis of security-critical systems Modelling protocols like DTLS and attack-defense scenarios Applications of formal methods in education and public health Recent work highlights include developing SMTQuery for benchmark analysis, statistical evaluation of bit-flip vulnerabilities, and leveraging large language models for educational feedback. His research outputs span over 36 publications across journals and conferences, with active contributions to UPPAAL tool development and open datasets for reproducibility. Poulsen collaborates internationally on projects involving formal verification of embedded systems and cybersecurity protocols. His work bridges theoretical computer science with practical applications in safety-critical systems.

Christophe Biscioمشاهده پروفایل

دانشیار

• Point Processes
• Statistics
• Mathematics
• +۳ مورد دیگر

Christophe Biscio is an Associate Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science. His research focuses on spatial statistics, point processes, and topological data analysis, with applications in materials science and engineering. He leads interdisciplinary projects like 'Deciphering Nanoporosity of Amorphous Materials using Topological Data Analysis,' exploring medium-range order structures in glasses and metal-organic frameworks. Key research interests include statistical learning for point processes, functional central limit theorems, and adversarial machine learning. His work bridges theoretical mathematics with practical challenges in wireless communication, radar sensing, and urban system modeling. Collaborations span materials scientists, computer scientists, and data analysts. Biscio has advised one PhD student and contributed to datasets on topological summaries for spatial processes. He actively organizes conferences on data science and participates in public outreach events like Earth Day climate change discussions.

Sajad Homayounمشاهده پروفایل

استادیار

• Cybersecurity
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Sajad Homayoun is a Tenure Track Assistant Professor at Aalborg University's Department of Electronic Systems within The Technical Faculty of IT and Design. His research focuses on integrating Artificial Intelligence with cybersecurity to develop advanced solutions for threat detection and mitigation. PhD in Computer Science Specialization in Cybersecurity & Machine Learning His technical expertise spans: AI-driven intrusion detection systems Adversarial machine learning (AML) Explainable AI (XAI) for security transparency Time series and graph analysis MLOps implementation with MLflow Python development using pandas, scikit-learn, and TensorFlow Research trends from his publications show strong emphasis on: Combating phishing attacks through ML Applying deep learning to crowdsensing trustworthiness Failure prediction in industrial systems Extracting behavioral patterns from ransomware Blockchain-based security applications He teaches Network Security courses during fall semesters and maintains active collaborations with researchers across Denmark and international institutions.

Søren Haubergمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Inference
• Geometric Deep Learning
• +۱۱ مورد دیگر

Søren Hauberg is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the Cognitive Systems group. His research lies at the intersection of machine learning, geometry, and probabilistic modeling, with applications in life sciences and computer vision. Department: Applied Mathematics and Computer Science Research Group: Cognitive Systems Institution: Technical University of Denmark (DTU) His research interests center on geometric deep learning , Bayesian inference , and manifold-based modeling . He explores how stochastic and geometric structures can improve machine learning models, particularly in modeling complex data such as biological sequences and medical images. His work integrates Riemannian geometry, Gaussian processes, and energy-based models to build more robust and interpretable AI systems. The recent publications reveal a strong trend toward geometric representation learning , fairness in generative models , and computational methods for biological data . His team investigates latent space geometries, develops efficient GPU-based numerical algorithms, and applies foundational models to protein sequences. There is a clear emphasis on both theoretical rigor and practical implementation. Søren Hauberg actively supervises multiple PhD students and leads key projects in machine learning for life sciences. He is a project manager in the Center for Basic Machine Learning Research in Life Science , indicating leadership in interdisciplinary research. He also contributes to high-performance computing applications in statistical modeling. He is involved in several active research projects, including: Geometric Bayesian Deep Learning Stochastic Riemannian Geometry with Applications to Data Modelling The geometry of protein representations AI-driven Electron Tracking for High-Energy Radiation Detection

• Machine Learning
• Distributed Learning
• Byzantine Fault Tolerance
• +۳ مورد دیگر

Nirupam Gupta is an Assistant Professor - Tenure Track at the Department of Computer Science , University of Copenhagen , specializing in Machine Learning , Distributed Learning , and Byzantine Fault Tolerance . His research focuses on robustness in federated and decentralized systems, privacy-preserving optimization, and adversarial client mitigation. Research Areas: Byzantine-Robust Distributed Learning Federated Learning with Heterogeneous Clients Data Poisoning and Security Privacy in Multi-Agent Systems Contact: nigu@di.ku.dk Scientific Contributions: Published extensively on Byzantine fault tolerance in distributed SGD Developed frameworks for privacy-robustness trade-offs Investigated redundancy mechanisms for resilient optimization Explored preconditioning techniques for gradient descent

Vedrana Andersen Dahlمشاهده پروفایل

دانشیار

• Geometry Processing
• Image Analysis
• Computer Vision
• +۳ مورد دیگر

Vedrana Andersen Dahl is an Associate Professor at the Technical University of Denmark (DTU), Department of Applied Mathematics and Computer Science. Her research focuses on image analysis and geometry processing, including geometric texture analysis, deformable models, and tomographic data processing. She holds a PhD in Image Analysis and Computer Graphics from DTU (2007–2011), an MSc in IT from the IT University of Copenhagen (2005–2007), and a Diploma in Mathematics and Physics from the University of Zagreb (1992–1999). PhD: Technical University of Denmark (2007–2011) MSc: IT University of Copenhagen (2005–2007) Diploma: University of Zagreb (1992–1999) Her research interests span geometric texture analysis, deformable models, OCT data processing, and medical imaging applications. She has contributed to projects such as the Villum Experiment Grant (2022) and collaborates internationally on topics like placental tomography and fibrous composite analysis. Dahl supervises multiple PhD students in areas like medical image segmentation and X-ray imaging techniques. She has authored over 100 publications, with recent work focusing on 3D tomography, medical imaging, and machine learning applications. Her datasets, including BugNIST and STXAE, are widely used in academic and industrial research. Dahl also serves on review committees at institutions like the University of Copenhagen and University College Copenhagen.