Find Academic Supervisors

• Quantum Algorithms
• Quantum Mathematics
• Materials Characterization
• +3 more

Elias David Michael Theil is a PhD Fellow at the Department of Mathematical Sciences, University of Copenhagen, specializing in quantum mathematics and computational materials science. His research interests include: Quantum Algorithms Quantum Mathematics Materials Characterization Texture Analysis Metal Forming Processes Computational Mechanics Dr. Theil's work bridges theoretical mathematics with industrial applications, particularly in materials characterization and metal processing. His recent publications demonstrate strong interdisciplinary collaboration between mathematics and materials science, with papers appearing in Materials Characterization and Crystals . His research shows increasing impact, with his 2024 paper on metal forming modeling receiving 4 Scopus citations and his 2025 clustering algorithm paper gaining early attention in the field. Dr. Theil is based at Universitetsparken 5, Copenhagen Ø (DK-2100) and can be contacted at edmt@math.ku.dk.

Xenofon FafoutisView profile

Professor

• Internet of Things (IoT)
• Wireless Sensor Networks
• Digital Health
• +5 more

Xenofon Fafoutis is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on the Internet of Things (IoT), wireless sensor networks, digital health, and embedded systems. He leads several projects including the DIREC Digital Community Building initiative and the eAI: Embedded AI initiative. His work emphasizes energy-efficient communication protocols, security in IoT, and the application of machine learning in edge computing. Key research interests include: IoT and Industrial IoT Wireless embedded systems and protocols Machine learning for edge devices Cybersecurity for sensor networks Wearable health monitoring systems Recent publications highlight his contributions to reinforcement learning-based scheduling, energy-efficient IoT protocols, and TinyML applications. He supervises PhD students in areas like embedded AI toolchains and predictive maintenance systems. His work aligns with UN SDG goals related to sustainable infrastructure and innovation.

Anne Marie SvaneView profile

Associate Professor

• Topological Data Analysis
• Probability Theory
• Mathematical Statistics
• +3 more

Anne Marie Svane is an Associate Professor at the Department of Mathematical Sciences, Aalborg University, specializing in topological data analysis, stochastic geometry, and probability theory. Her research bridges theoretical mathematics with applied statistics, focusing on geometric functionals and computational methods. Education: PhD in Mathematics from Aarhus University. Her recent work explores cobordism obstructions, Gibbs processes, and applications in climate science and medical research. Key contributions include: Advancing kernel persistence methods for topological data analysis. Developing central limit theorems for spatial point processes. Modeling moisture dynamics in building physics with climate data. Investigating the interplay between digital algorithms and geometric structures. She collaborates on interdisciplinary projects like AI-Aalborg Intelligence and contributes to educational initiatives such as Girls' Day in Science.

Markus KiderlenView profile

Associate Professor

• Integral Geometry
• Convex Geometry
• Stochastic Geometry
• +2 more

Markus Kiderlen is an Associate Professor at the Department of Mathematics, Aarhus University, specializing in the intersection of geometry, probability theory, and statistics. His research spans integral geometry, convex geometry, stochastic geometry, geometric tomography, and spatial statistics. Primary affiliation: Department of Mathematics, Aarhus University Academic rank: Associate Professor His work in stochastic geometry involves integral geometric relations, Minkowski tensors for quantifying random particles, and uniqueness/inference problems in geometric tomography (e.g., solving Hammer's X-ray problem). Another key focus is spatial sampling methods, including Wicksell's corpuscle problem and Cavalieri volume estimation improvements. Recent research trends include geometric reconstruction algorithms, stereological techniques for nonconvex particles, and rotational integral formulae for convex bodies. He has also explored polarizations in rearrangement processes and their applications. Markus Kiderlen has presented at international conferences such as MCQMC (2022), Stochastic Geometry Workshops (2019-2022), and the WIASABISS workshop (2018). His publications reflect collaborations with researchers like M. Stehr, R. Eriksen, and D. Hug.

Muhammad RajabinasabView profile

Researcher

• Data Science
• Machine Learning
• Computer Science
• +7 more

Muhammad Rajabinasab is a PhD student at the University of Southern Denmark , affiliated with the Faculty of Science and the Department of Mathematics and Computer Science . His research focuses on data science and machine learning, with a particular emphasis on feature selection, synthetic data analysis, and approximate neighbor search algorithms. Current affiliation: PhD student, Department of Mathematics and Computer Science Research areas: Data Science, Machine Learning, Feature Selection, Synthetic Data, k-Nearest Neighbor Algorithms Muhammad’s research explores dynamic evaluation metrics for feature selection, inter-dataset similarity in synthetic data, and randomized PCA forests for efficient neighbor search. His work addresses both theoretical foundations and practical applications in data mining and machine learning. Recent publications demonstrate expertise in feature extraction , dimensionality reduction , and computational efficiency . Collaborations span institutions like IMADA (SDU) and researchers in Denmark and Finland.

• Quantum Physics
• Superconductivity
• Nanowire Devices
• +3 more

Yu Liu is a researcher at the Center for Quantum Devices within the Niels Bohr Institute at the University of Copenhagen. The Center for Quantum Devices focuses on cutting-edge research in quantum physics, particularly in areas related to spin qubits, topological quantum systems, novel devices, superconducting qubits, and quantum materials. Dr. Liu's research spans multiple disciplines within quantum physics and condensed matter. His primary focus appears to be on quantum devices, particularly investigating superconductivity in hybrid nanowire systems with ferromagnetic components. His work explores spin-split superconductivity, supercurrent transport, and spin-polarized bound states in semiconductor-superconductor-ferromagnetic-insulator hybrid structures. Beyond quantum physics, Dr. Liu has also published work in computer vision, mathematics, particle physics, transportation studies, and medical imaging, demonstrating remarkable interdisciplinary breadth. His recent publications (2021-2025) show a strong focus on quantum transport phenomena in hybrid nanostructures, particularly examining the interplay between superconductivity, ferromagnetism, and spin-orbit coupling in nanowire systems. These investigations have important implications for the development of topological quantum computing platforms and novel quantum devices. The observed phenomena include supercurrent reversal near coercive fields of ferromagnetic insulators, spin-splitting exceeding induced superconducting gaps, and percolative supercurrent flow in bilayer systems. Dr. Liu has collaborated extensively with researchers at the Center for Quantum Devices, including P. Krogstrup, S. Vaitiekėnas, C. M. Marcus, M. Leijnse, and R. Seoane Souto. His work often involves experimental measurements combined with theoretical modeling to explain observed phenomena in quantum transport systems, bridging the gap between fundamental physics and potential quantum technology applications.

Anders Nymark ChristensenView profile

Associate Professor

• Image Analysis
• Computer Vision
• Medical Imaging
• +7 more

Anders Nymark Christensen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in Visual Computing. His work bridges computer vision, medical imaging, and materials science, with a strong focus on explainable AI in clinical applications and structural analysis of complex materials. Institution: Technical University of Denmark (DTU) School: Department of Applied Mathematics and Computer Science Department: Visual Computing Role: Associate Professor Christensen’s research interests include image analysis, computer vision, explainable AI, ultrasound imaging, fetal medicine, and fiber orientation in composites. He applies advanced computational techniques to solve real-world problems in healthcare and materials engineering, particularly through structure tensor analysis and deep learning. His recent publications highlight a strong trend in developing AI tools for clinical decision support in fetal medicine, structural analysis of food and composite materials, and ultrasound imaging. These works span journals such as Scientific Reports , Food Structure , and IEEE Access , reflecting interdisciplinary innovation. Clinical validation of explainable AI for fetal growth scans Characterization of anisotropy in mozzarella cheese Determining fetal orientations from ultrasound video Structure tensor analysis in composites He supervises several PhD students in projects related to AI in skin lesion analysis, cancer detection, and ultrasound imaging. His collaborative network includes key researchers at DTU such as Anders Bjorholm Dahl, Vedrana Andersen Dahl, and Mads Nielsen. He has been involved in significant research grants and is active in both medical and engineering domains, contributing to open datasets and reproducible research. Christensen leads and contributes to numerous active projects, including: Decision Support AI for Skin Lesions (2024–2027) Fighting Cancer with Generative AI (2024–2027) SONAI: Explainable AI in the Clinic (2022–2026) Deep learning for identifying biomarkers in medical images (2022–2025) His work supports UN Sustainable Development Goals related to health, innovation, and responsible consumption, particularly through advancements in medical diagnostics and sustainable materials.

Søren HaubergView profile

Professor

• Machine Learning
• Bayesian Inference
• Geometric Deep Learning
• +11 more

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

Tim Bjørn DyrbyView profile

Professor

• Medical imaging and analysis
• Magnetic Resonance Imaging
• Diffusion MRI
• +7 more

Tim Bjørn Dyrby is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in Visual Computing. His research focuses on medical imaging, particularly diffusion MRI, tractography, and biophysical modeling of the brain. His research interests lie at the intersection of neuroscience, medical imaging, and computational modeling. Key areas include Diffusion MRI , Tractography , Axonal modeling , Myelin integrity , and quantitative MRI . He applies advanced computational techniques to understand brain microstructure and connectivity in both health and disease. His recent publications highlight a strong focus on standardization and methodological advancements in preclinical diffusion MRI, particularly in vivo and ex vivo imaging protocols. The work emphasizes data processing, validation against microscopy, and the application of tractography for neural pathway mapping. There is a growing emphasis on linking MRI-derived biomarkers to neurophysiological properties such as conduction velocity. Tim Dyrby actively supervises PhD students and leads significant research projects. He is the Principal Investigator (PI) of the CoM-BraiN project, which aims to develop a non-invasive framework for mapping conduction velocity in brain networks. His collaborative network is extensive, involving international experts in MRI physics and neuroscience. He is affiliated with the Visual Computing section at DTU Compute, where he contributes to advancing computational methods for brain imaging analysis. His work is supported by active research grants and contributes to key UN Sustainable Development Goals in health and well-being.

Kumari PoojaView profile

Researcher

• Machine Learning
• Deep Learning
• Image Processing
• +4 more

Kumari Pooja is a Marie Curie Doctoral Researcher and PhD Student at the Technical University of Denmark (DTU), affiliated with the Department of Applied Mathematics and Computer Science Visual Computing. She contributes to open-source computational tools and geometric modeling. Research Interests: Machine Learning and Deep Learning for imaging 3D Visualization and Fiber Tracking Medical Imaging and Hyperspectral Data Analysis Optimizing Computational Efficiency in Image Reconstruction Scientific Contributions: Her work spans 3D Python tool development , deep unrolled neural networks , and hyperspectral classification architectures , with a focus on robustness to noise and multi-scale analysis. Awards: Marie Curie Doctoral Researcher Fellowship Contact: pooja@dtu.dk

Chao ZhangView profile

Research Fellow

• Scientific Computing
• Numerical Analysis
• Geophysics
• +5 more

Chao Zhang is a Research Fellow at the Department of Applied Mathematics and Computer Science , Technical University of Denmark. His work focuses on computational methods for geophysical and engineering applications. Research Areas: Inverse problems, uncertainty quantification, fluid dynamics, numerical analysis, and mathematical modeling Key Techniques: Level-set methods, regularization, finite element analysis, and simulation-based optimization Recent research highlights include novel approaches for rock permeability determination and uncertainty propagation in groundwater flow models. Publications span thermo-hydro-mechanical simulations, electrical impedance tomography, and thermal analysis of microelectronics. No formal academic awards or student mentoring information was found in the provided data.

• Machine Learning
• Quantum Machine Learning
• Neural Networks
• +6 more

Sebastian Bugge Loeschcke is a PhD Fellow at the Machine Learning Section of the Department of Computer Science (DIKU), University of Copenhagen . His research spans theoretical and applied machine learning with focus on quantum machine learning, language modeling, and sustainability. Current affiliation: Machine Learning Section, DIKU Key research areas: Quantum-classical hybrid models, neural language processing, geospatial analysis Collaborative initiatives: SCIENCE AI Centre, TreeSense Centre Loeschcke's recent work includes Coarse-To-Fine Tensor Trains for compact representations and LoQT: Low-Rank Adapters for Quantized Pretraining , reflecting his focus on efficient neural architectures and quantum-inspired methods. His publications address cross-disciplinary challenges in climate modeling, healthcare, and quantum computing. Scientific contributions include: 2024: Tensor train compression methods for visual representations 2024: Low-rank adapter techniques for quantized models 2025: Quantum computing applications in molecular binding energy calculation 2025: Ethical frameworks for sustainable AI development 2025: Quantum dot array simulation tools (QDarts) Loeschcke contributes to interdisciplinary projects involving: TreeSense (remote sensing of global tree resources) Quantum computing optimization with Danish research consortia

• Machine Learning
• Medical Data Analysis
• Quantum Computing
• +3 more

Simon Bartels is a researcher affiliated with the Department of Computer Science at the University of Copenhagen , contributing to the Machine Learning section. His work spans interdisciplinary applications of artificial intelligence, including quantum computing, healthcare diagnostics, and environmental modeling. Research activities at the department cover both theoretical and applied machine learning, with participation in the SCIENCE AI Centre . Key domains include medical data analysis , remote sensing , sustainability , and biological data modeling . Recent publications highlight contributions to quantum-inspired neural networks , geospatial biodiversity analysis , and energy-aware AI systems . Collaborations include rare disease research (e.g., MOSAIC framework ) and quantum computing optimizations.

• Machine Learning
• Quantum Computing
• Medical Data Analysis
• +3 more

Thomas Vecchiato is a Guest Researcher at the Department of Computer Science , University of Copenhagen , specializing in Machine Learning . His work intersects theoretical foundations with applications in quantum computing, sustainability, and biomedical data analysis. His research focuses on: Quantum-inspired machine learning architectures Ethical and sustainable AI development Medical imaging and clinical data analysis Language models for emotion recognition and healthcare applications Recent publications highlight advancements in: Optical neural network hardware Quantum biomolecular energy calculations Federated learning for personalized medicine Explainability techniques for AI models

• Algorithms
• Machine Learning
• Computational Complexity
• +2 more

Benjamin Bogø is a Research Fellow at the Department of Computer Science, University of Copenhagen. His work bridges algorithmic complexity, machine learning, and quantum computing, with a focus on sustainable AI and cross-cultural applications. He is affiliated with the SCIENCE AI Centre and contributes to the department's compute cluster initiatives. Benjamin's research interests include: Quantum computing applications in neural networks AI explainability and ethical frameworks Algorithm optimization for complex systems Climate-aware machine learning Interdisciplinary biomedical and cultural data analysis His recent publications analyze: Quantum-classical hybrid systems (15% of articles) Large language model interpretability (20% of articles) Medical and ecological applications (30% of articles) Quantum hardware optimization (25% of articles) Algorithmic fairness in recommender systems (10% of articles)