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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.

Mikkel N. SchmidtView profile

Associate Professor

• Statistical Modeling
• Machine Learning
• Bayesian Methods
• +3 more

Mikkel N. Schmidt is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on statistical modeling, Bayesian methods, and their applications in science and industry. He has held visiting roles at Columbia University (2007) and Cambridge University (2008-2009). His work integrates probabilistic modeling with computational inference to address complex problems in diverse fields such as molecular discovery, optical communication, and brain connectivity analysis. Education highlights include visiting scholar and postdoctoral experiences at top-tier institutions. Research interests span statistical methodology development, machine learning applications, and interdisciplinary problem-solving. Current projects involve Bayesian neural networks for molecular discovery and federated learning optimization. Advising efforts include supervising multiple PhD students in areas like molecular discovery and denoising diffusion models. Notable collaborations involve work on materials science, quantum communication, and medical signal processing. His contributions bridge theoretical advancements with practical industrial applications, emphasizing interdisciplinary innovation.

Asmus Skar ChristiansenView profile

Associate Professor

• Pavement Engineering
• Geotechnical Engineering
• Sensing Technologies
• +4 more

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).

Andreas KugiView profile

Professor

• Control Theory
• Automatic Control
• Complex Dynamical Systems
• +6 more

Andreas Kugi is the Scientific Director at the AIT Austrian Institute of Technology and a full professor of Complex Dynamical Systems at TU Wien (Vienna University of Technology) in the Faculty of Electrical Engineering and Information Technology, Institute of Automation and Control. He has held significant academic and leadership roles across Europe, including professorships at Saarland University and offers from TU Dresden and KIT. His research focuses on the modeling, control, and optimization of complex dynamical systems , with strong applications in mechatronics, robotics, and industrial automation . He has led major research centers such as the Christian Doppler Laboratory for Model-Based Process Control in the Steel Industry and the Center for Vision, Automation & Control at AIT. His work bridges theoretical control design and real-world industrial implementation. The recent publications reflect a consistent focus on nonlinear, hybrid, and distributed parameter systems , with applications in robotics, manufacturing, energy, and process industries. His research integrates advanced control theory with practical engineering challenges, emphasizing real-time optimization, robustness, and system efficiency. Scientific Awards: Mechatronic Systems Outstanding Investigator Award (IFAC, 2022) Goldene Stefan-Ehrenmedaille (OVE, 2023) 16 best paper awards Andreas Kugi has supervised over 50 completed PhD dissertations and has been deeply involved in research leadership, including serving as Editor-in-Chief of Control Engineering Practice (2010–2017) and Vice President of the OVE Austrian Electrotechnical Association (2017–2023). He has secured and led numerous research grants, particularly through industrial collaborations in automation and process control. He leads and contributes to major research initiatives, including the Center for Vision, Automation & Control at AIT and the Christian Doppler Laboratory , fostering interdisciplinary teams focused on industrial digitalization and smart systems.

Martin Skovgaard AndersenView profile

Associate Professor

• Optimization
• Signal Processing
• Machine Learning
• +12 more

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.

Morten S. RisagerView profile

Professor

• Analytic Number Theory
• Spectral Theory for Automorphic Forms
• Spectral Deformations
• +7 more

Morten S. Risager is a Professor in the Department of Mathematical Sciences at the University of Copenhagen. His research focuses on intersections of analytic number theory , automorphic forms , and mathematical physics , particularly in quantum chaos and hyperbolic geometry . He has held positions at Aarhus University and the Institute for Advanced Study, Princeton. Education: Ph.D. in Mathematics (2003, University of Aarhus), M.Sc. in Mathematics (2001, University of Aarhus). Research interests include modular symbols , Eisenstein series , lattice counting problems , and Selberg's conjecture . His recent work analyzes equidistribution in hyperbolic groups and spectral deformations. Publications (2014–2025) cover topics like shifted convolution sums , iterated integrals of modular forms , and hyperbolic circle problems . Collaborations with Petridis and Nordentoft highlight trends in spectral theory and number theory. Scientific awards : Steno Research Fellow (2005–2008) at Aarhus University. Teaching areas encompass linear algebra , analysis , elliptic curves , and analytic number theory .

Marco PizzolatoView profile

Associate Professor

• Magnetic Resonance Imaging (MRI)
• Diffusion MRI
• Image and Signal Denoising
• +10 more

Marco Pizzolato is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in Visual Computing with a focus on Magnetic Resonance Imaging (MRI), particularly diffusion MRI and biophysical modeling. He is also affiliated with the inter-departmental Microstructure & Plasticity (MAP) research group and has held visiting positions at the University of Verona, EPFL, and DRCMR. His educational background includes a PhD in Signal and Image Processing from INRIA Sophia Antipolis, a Master’s in Bioengineering from the University of Padua, and a Bachelor’s in Biomedical Engineering from the same institution. He previously served as an Assistant Professor at DTU and was a postdoctoral researcher under the Marie Curie COFUND Eurotech programme. Dr. Pizzolato's research centers on image and signal denoising, inverse problems, optimization, diffusion MRI, tractography, and Monte Carlo simulations. He actively contributes to the development of microstructural models for brain imaging, with applications in neurodegenerative diseases and brain connectivity. His work aligns with UN Sustainable Development Goals, particularly in advancing education and health through imaging technology. The recent publications reflect a strong trend in advancing diffusion MRI techniques, including ACID imaging, microscopic propagator modeling, myelin integrity mapping, and multi-scale white matter organization. These works emphasize biophysical accuracy, model validation, and integration across imaging modalities and species. Magna Cum Laude , ISMRM 2020 Magna Cum Laude , ISMRM 2022 First Place , Macaque Validation Challenge at ISBI 2018 First Place (Overall and HCP) , IronTrack Challenge 2019 (MICCAI) MICCAI Student Travel Award 2015 He has supervised PhD students such as Thøgersen, T. L. and Corral Bolaños, M. in projects related to microstructure MR imaging and myelin mapping. He has also been involved in significant grants and collaborative projects, including the Multimodal Microstructure-Informed Connectivity (MMINCARAV) initiative between Inria and EPFL, and the Sinergia consortium for Brain Communication Pathways . He co-organized multiple international events, including the MICCAI CDMRI workshops and challenges (2019–2021), and the ESMRMB Leaps in Microstructure Imaging workshop (2024). Dr. Pizzolato is an active member of the scientific community, serving as an editor for MICCAI workshop proceedings, a reviewer for major journals and conferences, and an invited speaker at ISMRM 2025. He leads and participates in several ongoing research projects at DTU focused on quantitative imaging, myelin mapping, and MRI-based connectivity, demonstrating sustained research leadership and external funding success.

Andreas Asp BockView profile

Researcher

• Scientific Computing
• Numerical Linear Algebra
• Bayesian Inversion
• +3 more

Andreas Asp Bock is a postdoctoral researcher at the Department of Applied Mathematics and Computer Science within the Technical University of Denmark . His work focuses on scientific computing and numerical linear algebra, with particular emphasis on matrix approximation techniques, preconditioning strategies, and optimization methods. Current affiliation: Technical University of Denmark (College of Engineering) Academic role: Researcher (postdoctoral level) Research interests include: Matrix factorization and truncation Bregman divergence applications Baysian inversion frameworks Curve registration algorithms High-dimensional data analysis Preconditioning for iterative solvers Recent publications demonstrate expertise in improving approximate factorization preconditioners, geometric curve registration, and divergence-based preconditioner design. Collaborations with Martin S. Andersen and others indicate ongoing contributions to sparse linear algebra and computational statistics. Supervision : Currently mentoring J. V. Galvão da Mata in a PhD project focused on Optimization methods for data-sparse models , active from 2022-2025.

Darko ZibarView profile

Professor

• Machine Learning in Photonic Systems
• Optical Communication
• Quantum Communication
• +3 more

Darko Zibar is a Professor at the Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU), and leads the Machine Learning in Photonics Systems (MLiPS) group. He holds a M.Sc. in Telecommunication and a Ph.D. in Optical Communications from DTU (2004, 2007). As a Visiting Professor, he has contributed to research at Politecnico di Torino, Friedrich Alexander University of Erlangen, University of California Santa Barbara, and University of Colorado, Boulder. Research Interests: Machine learning applications in optical communication systems Digital signal processing for classical and quantum photonics Optimization of Raman amplifiers and frequency combs Nonlinear fiber optic transmission modeling Photonic reservoir computing with silicon microring resonators Scientific Contributions: His work includes record-breaking achievements in optical phase noise measurement (approaching quantum limits) and programmable gain Raman amplifier design (S+C+L band). He has received prestigious awards such as the ERC Consolidator Grant (2017), Humboldt Bessel Research Award (2021), and Villum Investigator Award (2023).

Lasse Riis ØstergaardView profile

Associate Professor

• Medical Imaging
• Biomedical Engineering
• Image Processing and Computer Vision
• +4 more

Lasse Riis Østergaard is an Associate Professor at the Department of Health Science and Technology, Aalborg University, affiliated with The Faculty of Medicine. His research focuses on medical imaging, AI applications in healthcare, and computational methods for analyzing medical images, particularly in CT and MRI. He leads projects such as 'Modelling and quantification of the cerebral cortex' and contributes to initiatives like the Danish Centre for Health Informatics and AI for the People. Key research areas include: Medical Image Analysis: Specializing in segmentation, artifact reduction, and quantitative measurements in CT/MRI AI in Surgery: Developing deep learning models for surgical action recognition and robotic skill assessment Clinical Applications: Investigating body composition via CT, lung function correlations, and placental vascular imaging He has authored over 120 publications and supervised 5 PhD students. Notable collaborations include work with the Virtual Brain project and NemoMed. Recent studies explore ethnic differences in body composition and AI-driven surgical training. Awards: None explicitly listed, but his work has received media attention including coverage in Surgical Endoscopy and Advances in Respiratory Medicine .

Jacob Birk JensenView profile

Associate Professor

• Hydrology
• Groundwater Modeling
• Urban Hydrology
• +7 more

Jacob Birk Jensen is an External Associate Professor in the Department of Construction, Urban and Environmental Engineering at Aalborg University, Faculty of Engineering and Science, Denmark. His work bridges hydrology, environmental engineering, and stochastic modeling, with a focus on groundwater systems and urban hydrology. Department: Department of Construction, Urban and Environmental Engineering School: Faculty of Engineering and Science University: Aalborg University Position: External Associate Professor His research interests include groundwater modeling, inverse modeling, stochastic methods, eco-hydrology, and urban drainage systems. He applies reliability theory and probabilistic modeling to assess hydrological risks and environmental impacts, particularly in groundwater-dependent ecosystems and agricultural landscapes. The recent publications show a strong trend in integrated modeling approaches, such as coupling SWAT and MODFLOW to evaluate streamflow responses to groundwater abstraction. His work spans environmental sustainability, agricultural hydrology, and coastal engineering, reflecting a multidisciplinary approach to water resource challenges. Keywords across his research include hydrology, groundwater abstraction, eco-hydrology, reliability methods, and urban drainage. He has contributed to significant research projects including: Application of Reliability Theoretical Methods in Hydrological Simulations Eco-hydrological Modeling of Stream Valleys Impact of Rising Groundwater Tables on Agricultural Land Use Rotating Wave Energy Absorber Development Jacob Birk Jensen has advised PhD students such as O. Johansen and M. L. Fejerskov. While no formal scientific awards are listed, his publications are well-cited, particularly in the areas of groundwater abstraction and fen ecosystem responses. He has also delivered lectures at international conferences, including the International Multidisciplinary Conference on Hydrology and Ecology. His research group, the Urban Hydrology section, focuses on infrastructure, hydraulic engineering, and environmental technology, contributing to sustainable urban water management and climate resilience.

Jasper Marijn EverinkView profile

Researcher

• Bayesian Inference
• Regularization
• Gaussian Distribution
• +7 more

Jasper Marijn Everink is a Postdoc researcher at the Department of Applied Mathematics and Computer Science, Technical University of Denmark, specializing in computational uncertainty quantification and inverse problems. His work bridges statistical learning, regularization techniques, and imaging applications. Research Interests: Everink focuses on Bayesian inference, sparse modeling, and spatial regularization methods. His research develops hierarchical frameworks for uncertainty quantification, leverages level-set techniques in electrical impedance tomography, and explores implicit prior modeling for inverse problems. Publication Trends: Recent work emphasizes conformal prediction for imaging (2025), level-set regularization in tomography (2024), and sparsity-promoting priors (2024). Earlier contributions include projected density Bayesian methods (2023) and regularized Gaussian distributions for sparse inference (2023). Collaborations: Collaborates across mathematics, computer science, and engineering disciplines, with applications in imaging and computational physics.

Kim KnudsenView profile

Professor

• Inverse Problems
• Partial Differential Equations
• Numerical Analysis
• +3 more

Kim Knudsen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark. His research focuses on inverse problems for partial differential equations, numerical computations, and tomography. He leads projects in hybrid inverse problems and acousto-electric tomography. PhD in Mathematics, Aalborg University (2002) Cand.Scient. in Mathematics and Computer Science, Aalborg University (1999) Postdoc at University of Washington, Seattle (2004-2005) His work spans mathematical modeling, uncertainty quantification, and imaging techniques like electrical impedance tomography. Recent projects address regularization methods, level-set algorithms, and hybrid data tomography. Kim Knudsen supervises PhD students including Asmund Rasmussen, Henrik Schlüter, and Astrid Kirkeby. Collaborations include Tarvainen, Garnier, and Adesokan. Publications highlight Bayesian approaches, spatial regularization, and tomography advancements.

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
• 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.