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Olga SaukhView profile

Associate Professor

• Efficient Machine Learning
• Deep Learning
• Embedded Systems
• +12 more

Olga Saukh is an Associate Professor at the Institute of Technical Informatics, Graz University of Technology (TU Graz), and a Faculty member at the Complexity Science Hub Vienna (CSH). She leads the Embedded Learning and Sensing Systems research group, which operates across both institutions, focusing on the design and deployment of efficient AI-based systems on edge and mobile platforms. Her work bridges deep learning and embedded systems, with applications in environmental monitoring, precision agriculture, and digital health. Ph.D. in Computer Science, University of Bonn (2009) Habilitation in Embedded Systems, TU Graz (2020) Postdoctoral Training, ETH Zurich (2010–2016) B.Sc. in Applied Mathematics, Taras Shevchenko National University of Kyiv (2002) M.Sc. in Applied Computer Science, University of Freiburg (2004) Her research centers on efficient machine learning, particularly model optimization, neural network pruning, and contrastive learning for resource-constrained devices. She is deeply engaged in solving real-world challenges in IoT, sensor networks, and cyber-physical systems. Her work emphasizes data privacy, sustainability, and practical deployment of AI at the edge. The 15 most recent publications highlight a strong trend in efficient deep learning, including model compression, pruning, and transfer learning, applied to diverse domains such as environmental sensing (air quality, pollution tracking), digital agriculture (cattle farming), and embedded AI (sensor calibration, on-demand sensing). Her work frequently appears in top-tier venues like NeurIPS, ICLR, and IEEE/ACM IPSN, reflecting her leadership at the intersection of machine learning and embedded systems. Scientific awards include: CONET Ph.D. Academic Award (2010) Multiple Best Paper Awards at IEEE PerCom, ACM/IEEE IPSN, IEEE ICPADS, IEEE SECON, and UrbCom Spotlight and Oral presentations at ICML and CoLLAs workshops Ph.D. scholarship from IPVS, University of Stuttgart (2004–2005) Prizes in Ukrainian national mathematics competitions (1996–1998) Olga Saukh actively serves on program committees of leading international conferences in machine learning and embedded systems. She has advised multiple students and leads a collaborative research group spanning TU Graz and CSH Vienna. Her group develops practical AI systems for real-world deployment, with a focus on sustainability and privacy. She co-organizes the public EfficientML reading group and has secured recognition through numerous grants and awards. Her future work continues to explore the theoretical and practical challenges of deploying efficient, trustworthy AI in mobile and embedded environments. Her research group, Embedded Learning and Sensing Systems, operates jointly between TU Graz and CSH Vienna, fostering interdisciplinary collaboration across institutions. The team develops AI solutions for edge computing, sensor networks, and cyber-physical systems, with a strong emphasis on environmental sustainability and data privacy. Members work on joint challenges using advanced collaboration tools, reflecting the distributed nature of modern academic research.

Per Christian HansenView profile

Professor

• Numerical Analysis
• Numerical Linear Algebra
• Iterative Reconstruction Methods
• +8 more

Per Christian Hansen is a Professor at the Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark (DTU), where he leads the Section for Scientific Computing. He is a VILLUM Investigator and heads the CUQI (Computational Uncertainty Quantification for Inverse Problems) research initiative, aiming to develop accessible computational platforms for uncertainty quantification in inverse problems. His expertise lies in numerical analysis, numerical linear algebra, iterative reconstruction methods, and computational inverse problems, with applications in tomography, signal analysis, and plasma physics. His research integrates theoretical analysis—such as perturbation and convergence analysis—with the development of robust, adaptive, and efficient computational methods. He has co-authored five books, over 100 scientific papers, and several widely used MATLAB software packages, including IR Tools and Regularization Tools. His recent work (2023–2025) emphasizes uncertainty quantification, Bayesian inversion, and high-dimensional tomography in fusion plasmas, reflecting a strong trend toward probabilistic and robust modeling in inverse problems. He is a SIAM Fellow (2015) for his contributions to computational methods for rank-deficient and discrete ill-posed problems and regularization techniques. His scientific leadership is evident in both theoretical advances and practical software implementations. He actively collaborates across disciplines, particularly in nuclear fusion and medical imaging, and continues to supervise PhD students and publish in top-tier journals such as Inverse Problems , SIAM Journal on Scientific Computing , and Nuclear Fusion . SIAM Fellow (2015) VILLUM Investigator He advises PhD and Master’s students in computational mathematics and inverse problems, and his research is supported by major grants, including the VILLUM Investigator award. He leads the CUQI team, which develops open-source tools for non-experts to apply uncertainty quantification in inverse problems. His lab focuses on creating modeling frameworks that bridge theory, computation, and real-world applications in materials science, imaging, and plasma diagnostics.

• Machine Learning
• Pattern Recognition
• Classification
• +6 more

Christian Igel is a Professor at the Department of Computer Science, University of Copenhagen, and serves as director of the SCIENCE AI Centre . He is also a co-lead of the Pioneer Centre for Artificial Intelligence in Denmark. His academic journey includes a Doctoral degree from Bielefeld University (2002) and a Habilitation degree from Ruhr-University Bochum (2010). Igel is a Juniorprofessor (2002–2010) and has held editorial roles at journals like KI - Künstliche Intelligenz and Artificial Intelligence Journal . Doctoral degree: Faculty of Technology, Bielefeld University, Germany (2002) Habilitation degree: Department of Electrical Engineering and Information Sciences, Ruhr-University Bochum, Germany (2010) His research spans Machine Learning , focusing on Support Vector Machines , Evolution Strategies , Reinforcement Learning , Deep Neural Networks , and PAC-Bayesian Analysis . He applies these methods to Environmental Monitoring , Medical Diagnostics , and Climate Research . Recent publications highlight work on adversarial machine learning , environmentally sustainable AI , and tree resource mapping using deep learning. His scientific awards include being a ELLIS Fellow . Igel’s software tools like Shark , woody , and Multi-Planar UNet are widely used in research and industry. Notable grants and collaborations involve projects with European Lab for Learning and Intelligent Systems (ELLIS) , SCIENCE AI Centre , and international teams in Denmark , Germany , and France . His lab leadership emphasizes open-source frameworks and reproducible research. Editorial Roles: German Journal on Artificial Intelligence , Evolutionary Computation Journal , Artificial Intelligence Journal Software Projects: Shark , woody , Multi-Planar UNet , U-Time Collaborations: SCIENCE AI Centre , Pioneer Centre for Artificial Intelligence , European Lab for Learning and Intelligent Systems

Ankit KariryaaView profile

Assistant Professor

• Machine Learning
• Remote Sensing
• Geospatial Analysis
• +4 more

Ankit Kariryaa is a Tenure Track Assistant Professor at the Department of Computer Science and Department of Geosciences and Natural Resource Management , University of Copenhagen. His work bridges Machine Learning and Environmental Informatics , focusing on remote sensing, geospatial analysis, and ecological modeling. University of Copenhagen, Denmark Machine Learning Section, Department of Computer Science Geography, Land, Environment and Society, Department of Geosciences Kariryaa specializes in applying deep learning and computer vision to environmental challenges. His research includes: Automated tree detection and biomass estimation via satellite imagery Multi-modal geospatial representation learning Monitoring farmland tree decline and carbon sequestration potential Agroforestry system mapping using AI Developing AI tools for climate policy and sustainability Recent work trends show a focus on quantum-inspired machine learning , environmental monitoring , and cross-cultural AI applications . His 15 most recent publications span topics in remote sensing , ecological modeling , and AI ethics , with methods ranging from neural networks to tensor-based learning. He collaborates across disciplines, notably with researchers in ecology , climate science , and quantum computing . His outreach includes seminars on AI in agroforestry and ecosystem management , while his team contributes to global tree resource databases like TreeSense.

• Machine Learning
• Medical Image Analysis
• Deep Learning
• +5 more

Erik Bjørnager Dam is a Professor in the Machine Learning section at the Department of Computer Science, University of Copenhagen (UCPH). His research spans theoretical foundations of machine learning to practical applications in medical data analysis, sustainability, and materials science. His key research interests include: Small-scale and resource-efficient deep learning Medical image analysis and segmentation Sustainable and environmentally conscious AI development Graph neural networks for materials science Resource-constrained AI systems Professor Dam's recent publications demonstrate a strong focus on making AI more accessible and sustainable while maintaining high performance standards. His work on 'Performance Per Resource Unit' metrics addresses critical challenges in deploying AI in resource-limited environments, particularly in healthcare applications. His research bridges theoretical machine learning with practical implementations across multiple domains. His notable professional activities include: Co-founding Cerebriu A/S (since 2018) Co-founding Biomediq A/S (since 2008) Delivering lectures on AI's role in green transition (April 24, 2023) Media contributions on deep learning applications in plant research (September 13, 2018) With 74 documented research outputs, Professor Dam maintains an active research profile with significant contributions in 2023-2025 across medical imaging, sustainable AI, and materials science applications.

• Computer Vision
• Image Analysis
• Machine Learning
• +5 more

Serge Belongie is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, where he holds dual affiliations with the Pioneer AI research section and the Image Analysis, Computational Modelling, and Geometry section. His academic position places him at the forefront of interdisciplinary research connecting computer vision with language models, geospatial analysis, and cultural understanding. Professor Belongie's research program encompasses several critical domains in modern artificial intelligence: Advanced computer vision and image analysis techniques Vision-language model integration and multimodal systems 3D point cloud processing and semantic segmentation Geospatial representation learning for environmental applications Fine-grained object recognition and detection Cultural context understanding in AI systems His recent publication record reveals a sophisticated trajectory toward developing precise control mechanisms for vision-language models, with applications spanning forensic analysis, cultural heritage preservation, and social media understanding. The research demonstrates increasing sophistication in handling cultural context and enabling fine-grained manipulation of visual content through natural language interfaces. Professor Belongie maintains an active research group producing significant scholarly output, with over 280 research publications documented in his academic profile. His work is supported by research funding that enables cutting-edge exploration in multimodal AI systems with practical societal impact. He plays a key role in the Pioneer AI center at the University of Copenhagen, which focuses on advancing artificial intelligence through interdisciplinary collaboration and innovative research approaches that bridge theoretical computer science with real-world applications.

Borja Valverde PérezView profile

Associate Professor

• Microalgae Biotechnology
• Resource Recovery
• Life Cycle Assessment
• +3 more

Borja Valverde Pérez is an Associate Professor in the Department of Environmental and Resource Engineering at the Technical University of Denmark (DTU). His research advances sustainable water resource recovery through biotechnology innovations. Research focuses on microalgae cultivation, microbial protein production, and bioelectrochemical systems for water treatment. Key projects explore nutrient recovery from waste streams and greenhouse gas mitigation in biological processes. Recent publications demonstrate innovative approaches to wastewater valorization, including microalgae-bacteria consortia for pollutant degradation and inorganic bioelectric systems for low-temperature denitrification. Work integrates experimental validation with life cycle assessment. He coordinates projects on industrial symbiosis and circular economy applications in water systems. Current initiatives investigate decarbonization pathways for water resource recovery facilities through energy-flexible operation.

Jes FrellsenView profile

Associate Professor

• Statistical Machine Learning
• Generative AI
• Deep Learning
• +5 more

Jes Frellsen is an Associate Professor at the Department of Applied Mathematics and Computer Science (DTU) since 2016. Previously, he held academic positions at the IT University of Copenhagen (2016-2019), postdoctoral roles at University of Cambridge (2013-2016) and University of Copenhagen (2011-2013). Education: PhD in Bioinformatics (2011), University of Copenhagen MSc in Bioinformatics (2007), University of Copenhagen BSc in Mathematics and Computer Science (2005), University of Copenhagen EAP Exchange at University of California, Santa Cruz (2004-2005) Research Focus Jes Frellsen specializes in statistical machine learning , particularly generative AI and deep generative models with applications in bioinformatics . His work integrates Bayesian inference , directional statistics , and Markov chain Monte Carlo methods to address challenges in macromolecular structure prediction and missing data imputation . Recent efforts explore uncertainty quantification in image segmentation and generative modeling for materials science. Advising & Collaborations He actively supervises PhD students and postdoctoral researchers in projects spanning news recommendation systems , medical imaging , and 3D structure generation . Collaborations include work with Zoubin Ghahramani (Cambridge) and Thomas Hamelryck (Copenhagen), with contributions to protein structure prediction and statistical methods in structural bioinformatics .

• Machine Learning
• Remote Sensing
• Geospatial Representation Learning
• +3 more

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 .

Ruben PauwelsView profile

Associate Professor

• Artificial Intelligence
• Medical Physics
• Radiation Protection
• +4 more

Ruben Pauwels is an Associate Professor in the Department of Dentistry and Oral Health at Aarhus University, Denmark. As the strategic research coordinator of the 'Intelligent Systems' research theme, his work focuses on integrating artificial intelligence (AI) and deep learning into clinical workflows, particularly in medical imaging, radiation protection, and dental data science. His research spans applications like image enhancement, automated segmentation, lesion detection, and risk assessment for treatment planning, with a strong emphasis on cone-beam computed tomography (CBCT) and medical physics in dentistry. His educational background includes a PhD and MSc, and he actively contributes to interdisciplinary research involving multiple departments. Teaching activities align with his expertise, covering digital workflows and novel technologies in dental practice. Key research areas include biomedical image processing, machine learning in odontology, and biophysics. He has authored 118 publications, with recent work focusing on AI-driven medical imaging solutions and radiation protection standards. Notable contributions include the European consensus on patient contact shielding and organ-specific deep learning models for radiation dose calculation. Received awards such as the Bagger-Sørensen Young Researcher Award (2024) and ECMP Best Radiation Protection Presentation (2022). Active in professional networks: EMRA, EFOMP, and ITU/WHO/WIPO initiatives on AI in healthcare. Supervised students like B. N. de Freitas and R. J. Gonçalves da Motta in projects involving digital twin technology and mandibular canal labeling.

Xiao ChenView profile

Researcher

• Composite Structures
• Wind Energy Systems
• Structural Integrity
• +7 more

Xiao Chen is a Researcher at the Technical University of Denmark (DTU), specializing in advanced testing and digitalization of composite and offshore steel structures for wind energy systems. With a PhD from Nagoya University (2011), his work focuses on structural integrity, fatigue analysis, and Digital Twins for wind turbine blades. PhD in Engineering, Nagoya University (2011) Senior Researcher (2019–present) and Researcher (2017–2018) at DTU Associate Professor (2016–2017) and Assistant Professor (2013–2015) at Chinese Academy of Sciences His research explores nonlinear buckling, fracture mechanics, and Industry 4.0 technologies for structural health monitoring. Recent publications highlight AI-driven damage detection, thermographic analysis, and finite element modeling of composites. He leads projects like QualiDrone and AQUADA-GO, funded by EUDP and VILLUM FONDEN. Key article trends include composite fatigue , digital twins , drone-based inspection , and machine learning for structural monitoring. He received the 2022 Best Presentation Award at an international conference. Projects: Villum Experiment Project DiscoverBlaDE AQUADA-GO QualiDrone DARWIN RELIABLADE RELIfe

• Image Registration
• Image Segmentation
• Image Classification
• +6 more

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.

• Medical Image Analysis
• Image Segmentation
• Image Registration
• +3 more

Jens Petersen is an Associate Professor at the Department of Computer Science , University of Copenhagen, specializing in medical image analysis. He works within the Image Analysis, Computational Modelling, and Geometry research section. Education: B.Sc. (2005), M.Sc. (2010) in Computer Science from University of Copenhagen; Ph.D. (2014) in Medical Image Analysis from University of Copenhagen with visiting researcher experience at University College London. Research Interests: Focus on medical image analysis techniques for segmentation and statistical analysis of tubular/branching structures like airways and carotid arteries. Key methodologies involve graph cuts, optimal surface methods, and computational modeling. Current research includes AI-guided tumor delineation, longitudinal image synthesis, and physics-based deformable registration. Publications Trends: Recent work spans AI applications in oncology, computational methods for cardiovascular imaging, and algorithm development for radiation therapy. Collaborations include international institutions and cross-disciplinary teams in machine learning and medical imaging.

• Mathematical Image Analysis
• Differential Geometry
• Variational Methods
• +7 more

Francois Lauze is an Associate Professor at the Department of Computer Science , University of Copenhagen, affiliated with the Image Analysis, Computational Modelling and Geometry research group. His work bridges mathematical rigor and practical applications in image processing and shape analysis. Research Focus: Mathematical Image Analysis (variational/PDE methods) Differential and Riemannian geometry for shape statistics Applications: image inpainting, motion estimation, segmentation, medical imaging Contact: Email: francois@di.ku.dk Phone: +4535335671, +4521553933 Location: Universitetsparken 1, 2100 Copenhagen Ø Recent publications highlight advancements in SE(3) group CNNs for diffusion imaging, locally orderless networks for efficient processing, and refractive multi-view stereo techniques. His work integrates geometric modeling with computational implementations, emphasizing medical and video applications.

Anders Bjorholm DahlView profile

Professor

• Medical Imaging
• Computer Vision
• Biomedical Engineering
• +6 more

Anders Bjorholm Dahl is a Professor at the Department of Applied Mathematics and Computer Science , DTU Compute , Technical University of Denmark (DTU). His research focuses on medical imaging, computer vision, and biomedical engineering. He leads projects in ultrasound imaging, AI-driven medical diagnostics, and advanced imaging technologies for healthcare applications. Education: Ph.D. in Computer Science (Image Analysis and Computer Vision), DTU (2005–2009) Forestry, Royal Veterinary and Agricultural University (1997–2004) Research Interests: Combines machine learning and advanced imaging techniques to address challenges in medical diagnostics, including ultrasound super-resolution, stenosis detection in coronary angiographies, and material anisotropy analysis. His work bridges anatomy and histology using X-ray tomography and explores AI applications in healthcare. Key Projects: Crowd Counting through Remote Sensing (2025–2027) AI for Extreme Super-Resolution CT (2024–2026) Fighting Cancer with Generative AI (2024–2027) Labs/Teams: Leads the UltraSound and Biomechanics Visual Computing Center for Fast Ultrasound Imaging , focusing on real-time medical imaging solutions.