جستجوی استاد راهنمای دانشگاهی

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.

• Machine Learning
• Medical Image Analysis
• Deep Learning
• +۵ مورد دیگر

Raghavendra Selvan, an Assistant Professor (Tenure Track) at the University of Copenhagen, holds joint appointments in the Machine Learning Section (Department of Computer Science), Kiehn Lab (Department of Neuroscience), and the Data Science Laboratory. His academic journey includes a PhD in Medical Image Analysis (2018), MSc in Communication Engineering (2015), and BSc in Electronics and Communication Engineering (2009). PhD - Medical Image Analysis, University of Copenhagen (2018) MSc - Communication Engineering, Chalmers University (2015) BSc - Electronics and Communication Engineering, BMS Institute of Technology, India (2009) His research focuses on Bayesian Machine Learning with emphasis on Medical Image Analysis, Graph-based Learning, Tensor Networks, Approximate Inference, and Multi-Object Tracking Theory. Recent publications highlight his contributions to environmentally sustainable AI practices, efficient deep learning in medical imaging, and novel applications of tensor networks. Key research areas: Green AI and Environmental Sustainability Medical Image Analysis Graph Neural Networks Crystal Structure Prediction Model Compression Materials Science Applications

• Bayesian Machine Learning
• Medical Image Analysis
• Graph-based Learning
• +۳ مورد دیگر

Mark Bo Jensen is an Assistant Professor (tenure track) at the Department of Engineering Technology and Didactics, Technical University of Denmark (DTU), specializing in Energy Technology and Computer Science. His research is centered on Perception Engineering and Extended Reality technologies, particularly Virtual Reality (VR), with applications in human cognition, computer graphics, and scientific visualization. His research interests lie at the intersection of engineering and cognitive sciences, focusing on creating immersive and convincing extended reality experiences. Jensen applies his over 10 years of expertise in real-time computer graphics to advance VR systems for perception modeling, geometric data visualization, and material appearance simulation. His work contributes to fields such as medical diagnostics, 3D annotation, and photorealistic rendering. The recent publications highlight a strong trend in leveraging VR for scientific tasks, such as anatomical landmark annotation and visual field testing, as well as advancing core graphics techniques like meshlet optimization and diffusion-based stereo image generation. His research integrates computer vision, graphics algorithms, and human-centered design. While no scientific awards are currently listed, his active participation in research projects and consistent publication output indicate a growing academic profile. He has contributed to interdisciplinary collaborations involving medical, biological, and engineering domains. Jensen has been involved in advising and research projects, including serving as a PhD student in the 'Virtual Reality-Based Visualization of Geometric Data' project and currently as a project participant in 'AL-EYE: The Visual Aid'. These projects reflect his focus on applied VR solutions and data understanding. His work is conducted within the Energy Technology and Computer Science division at DTU, where he contributes to advancing perception-driven technologies and their practical implementation in scientific and medical contexts.

• Perception Engineering
• Extended Reality
• Virtual Reality
• +۷ مورد دیگر

Sonia Coriani is a Professor in Physical Chemistry at DTU Chemistry, Technical University of Denmark, since 2017. She leads a research group focused on theoretical chemistry and computational spectroscopy. Her academic journey includes a PhD from Aarhus University (2000), a permanent research scientist position at the University of Trieste (1999-2014), and associate professorship there since 2014. She held an adjunct associate professor position at the Centre for Theoretical and Computational Chemistry in Oslo (2007-2011) and was a Marie Curie IEF fellow (2010-2012) and AIAS-COFUND senior fellow (2015-2016). Education: Chemistry, University of Modena (1993) PhD in Theoretical Chemistry, Aarhus University (2000) Her research centers on developing quantum-chemical methodologies for static and dynamic molecular properties, particularly for systems with high dimensionality, complex environments, or novel spectroscopic phenomena. Key areas include non-linear optical experiments, magnetic circular dichroism, X-ray spectroscopies, and quantum computing applications in chemistry. Her recent publications highlight advancements in quantum linear response theory, polarizable embedding environments, X-ray absorption in water, and quantum algorithms for molecular properties. Collaborative work spans interdisciplinary projects with experimentalists, covering gas-phase molecules to biomolecular systems. Scientific Awards: Marie Curie IEF fellowship AIAS-COFUND senior fellowship Her work addresses challenges in ultrafast dynamics, photoionization, and the intersection of chemistry with physics and computational science, emphasizing accuracy and novel experimental guidance.

• Quantum Chemistry
• Computational Spectroscopy
• Molecular Properties
• +۷ مورد دیگر

Troels Henriksen is an Assistant Professor on Tenure Track at the Department of Computer Science (DIKU) at the University of Copenhagen, where he is affiliated with the Programming Languages and Theory of Computation research section. His research focuses on programming languages, particularly functional array programming languages, compiler design, and parallel computing. He maintains an active research profile with numerous publications in top-tier programming language conferences. Dr. Henriksen's research interests center around programming language theory and implementation, with particular emphasis on functional array programming languages. His work bridges theoretical foundations with practical high-performance computing applications. His research spans type systems, compiler optimizations, parallelism, and memory management in the context of array programming languages, contributing to both academic knowledge and practical language implementations. His recent publications reveal a strong focus on array programming language design and implementation. There is a clear trend toward optimizing functional array languages for high-performance computing environments, with significant work on fusion optimizations, parallelism, and memory management. His research often intersects with practical applications in scientific computing and machine learning, particularly through work on automatic differentiation for array languages. Dr. Henriksen is actively involved in the programming languages research community, regularly publishing in prestigious venues such as the ACM SIGPLAN conferences. His collaborations span multiple institutions, indicating an active research network in the programming languages field.

• Programming Languages
• Functional Programming
• Array Programming
• +۴ مورد دیگر

Professor Ira Assent is affiliated with the Department of Computer Science at Aarhus University. Their research focuses on machine learning, data mining, and visualization, with applications in climate science, medical informatics, and computer vision. Professor Assent leads projects such as Light-IoT (analytics on compressed IoT data), WallViz (interactive visualization for massive datasets), and eData (anomaly detection in e-science). Their work emphasizes scalable algorithms, explainable AI, and interdisciplinary applications. Recent publications address rainfall prediction using deep learning, entity summarization via knowledge graphs, and efficient clustering techniques. Projects like RainAI demonstrate contributions to weather modeling and satellite data analysis. Collaborative efforts span academic and industrial domains, with a strong emphasis on practical, user-centric solutions. Selected research contributions include advancements in density-based clustering (e.g., AnyDBC, DISCO), parallel algorithms optimized for GPUs (HUNIPU), and visualization frameworks (AVID). Their work bridges theoretical computer science with real-world challenges, such as improving decision-making through interactive visualizations and enhancing medical information retrieval systems. Ongoing projects aim to address computational efficiency in large-scale data analytics while maintaining interpretability. Key areas of innovation include explainable AI (e.g., InteDisUX), climate modeling (DROPP), and hardware-accelerated algorithms (GPU-FAST-PROCLUS). These efforts reflect a commitment to advancing both foundational methods and applied technologies that impact diverse fields from environmental science to healthcare.

• Machine Learning
• Data Mining
• Clustering Algorithms
• +۵ 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
• Bayesian Inference
• Geometric Deep Learning
• +۱۱ مورد دیگر

Troels Haugbølle is an Associate Professor in the Astrophysics and Planetary Research group at the Niels Bohr Institute, University of Copenhagen. His work focuses on computational astrophysics related to star and planet formation, with particular expertise in numerical modeling of protostellar systems, disk dynamics, and plasma physics applications in astrophysical contexts. Research Interests: AMR models of star formation Particle-in-cell models Plasma physics Dust dynamics in protostellar environments Astro-chemistry integration in hydrodynamics Forward modeling to link simulations with observations His recent research shows a strong focus on protoplanetary disk formation mechanisms, particularly through Bondi-Hoyle accretion, and the evolution of angular momentum in young stellar systems. He has developed advanced computational methods including GPU-accelerated MHD simulations and Monte Carlo approaches for plasma physics. His work increasingly integrates multi-scale modeling to connect large-scale molecular cloud dynamics with the formation of planetary systems. Professional Activities: Maintains the starformation.hpc.ku.dk research platform Active contributor to major astrophysics journals including Nature Astronomy, Astronomy & Astrophysics, and Monthly Notices of the Royal Astronomical Society Collaborates internationally with researchers across Europe and beyond Dr. Haugbølle's work has been featured in 14 news outlets, blogged about by 2 sources, and shared by multiple social media users, demonstrating the impact of his research on the broader scientific community and public understanding of star and planet formation processes.

• Star Formation
• Planet Formation
• Plasma Physics
• +۵ مورد دیگر

Rajmund Mokso is a Senior Researcher in the Department of Physics at the Technical University of Denmark (DTU), affiliated with the Materials Physics in Time and Space group, UltraSound and Biomechanics group, and Center for Fast Ultrasound Imaging. His work focuses on advanced X-ray imaging techniques for biological and materials science applications. Research Interests: Mokso specializes in phase contrast imaging, X-ray tomography, and microscale physics. His research integrates computational methods like deep learning with experimental techniques to study nanoparticle transport in plants, fibrotic liver tissue, and additive manufacturing processes. Key focus areas include high-speed imaging at microsecond frame rates and nano-holotomography reconstruction. Publication Trends: Recent works (2023-2025) demonstrate interdisciplinary applications: correlative X-ray imaging for plant nutrient transport, AI-driven analysis of liver fibrosis, and high-resolution imaging of insect compound eyes. His publications emphasize methodological innovations in X-ray microscopy with practical implementations across biology and engineering. Scientific Awards: No awards were documented in the source material. Advising and Grants: Principal Investigator for 3DIM: 3D imaging center (2016-2029), a major DTU initiative on tomography and fiber-reinforced composites Main Supervisor for PhD candidate Topic, A. (X-ray Microscopy for Life Sciences, 2025-2028) Main Supervisor for PhD candidate Kristensen, E. V. (nanoparticle transport in plants, 2022-2025) Examiner for two completed PhD projects in X-ray security applications and solar cell tomography Labs and Teams: Center for Fast Ultrasound Imaging (core member) UltraSound and Biomechanics research group Materials Physics in Time and Space experimental team

• Phase Contrast Imaging
• X-ray Tomography
• Microscale Physics
• +۴ مورد دیگر

Bernd Dammann is an Associate Professor in Scientific Computing at the Technical University of Denmark (DTU) , Department of Applied Mathematics and Computer Science. In parallel he serves as Scientific Lead & Architect in the DTU Computing Center (DCC) and co-founded DTU’s GPUlab in 2007, giving him dual roles in both academic research and institutional HPC services. Education Dipl.Phys. in Statistical Physics, University of Mainz, 1992 PhD in Physical Chemistry, DTU, 1996 Research interests Over almost three decades Dammann has focused on computational methods for large-scale scientific problems. His core expertise lies in: High-Performance Computing and parallel programming paradigms (OpenMP, MPI, GPU) Scientific GPU computing and many-core architectures Application tuning and performance optimization for heterogeneous systems Uncertainty quantification, Monte-Carlo methods, and real-time control applications in biomedicine (diabetes technology) Ultrasound beamforming and advanced imaging techniques Publication trends His 34 publications (1996-2025) trace a clear evolution from theoretical physics and surface-science simulations to GPU-accelerated engineering and biomedical systems. Recent work clusters around real-time biomedical control (artificial pancreas, virtual clinical trials) and high-resolution ultrasound imaging, all leveraging GPU acceleration and high-performance computing to achieve real-time performance. Scientific awards & recognition Contributor to 1 patent family and widely downloaded technical reports (>10 k downloads for OpenCL tutorial) Consistently high citation counts on key optimization and control papers (e.g., 93 citations for 2014 Management Science article) Teaching, supervision & grants He teaches or co-teaches multiple DTU courses: High-Performance Computing , Large-scale Computations , Python & HPC , Mathematical Software , and HPC with FORTRAN . While individual students are not named, his role in GPUlab and DCC implies ongoing PhD and MSc supervision, and his projects list six active collaborations including EU and national HPC infrastructure grants. Labs & teams Dammann co-founded GPUlab (2007) and presently leads the HPC architecture team in the DTU Computing Center , a central facility supplying cluster resources to the entire university.

• High-Performance Computing
• Scientific GPU Computing
• Parallel Programming
• +۳ مورد دیگر

Titus Theodorus Robroek is a Postdoctoral Researcher at IT University of Copenhagen, working within the Data, Systems, and Robotics section. His research focuses on resource-aware data systems and data-intensive systems and applications, with affiliations to both the Resource-Aware Data Systems group and the Center for Climate IT. Dr. Robroek's research interests span multiple areas in machine learning systems, including: Resource-aware Machine Learning Deep Learning Training Optimization GPU Computing and Collocation ML Benchmarking Frameworks Data Management for Machine Learning Scientific Visualization for Training Systems His recent publications demonstrate a strong focus on optimizing machine learning workflows, particularly in resource-constrained environments. His work addresses critical challenges in data selection, pipeline orchestration, and efficient utilization of hardware resources for deep learning training. The research shows a progression from foundational work on data management to more comprehensive frameworks for resource-aware machine learning. Dr. Robroek has received research funding through two major projects: MOTH: Machine Learning on Tiny Hardware (Novo Nordisk Foundation, 2023-2026) RAD: Extremely Parallel and Incredibly Diverse Data Processing on Many Heterogeneous Cores (Independent Research Foundation of Denmark, 2021-2025) His PhD thesis "Resourceful Learning: Training More Models with Fewer Resources" (2024) represents a significant contribution to the field of efficient machine learning systems.

• Machine Learning
• Deep Learning
• Resource-aware Computing
• +۴ مورد دیگر

Nicholas Bailey is an Associate Professor in the Department of Mathematics and Physics (IMFUFA) within Roskilde University's Department of Science and Environment, Denmark. His research focuses on computational statistical mechanics of glass-forming systems, with particular expertise in isomorph theory and molecular dynamics simulations of metallic glasses and ionic liquids. His primary research interests center on hidden scale invariance in liquids, density scaling phenomena, and the thermodynamic-structural relationships in glassy materials. Bailey employs advanced molecular dynamics techniques to investigate melting curves, phase transitions in binary alloys like Cu-Zr systems, and the rheological behavior of amorphous materials under shear deformation. His work bridges fundamental statistical mechanics with practical materials science applications. Recent publications demonstrate strong focus on Isomorph invariance in sheared glassy systems (2023) Density scaling exponents from pair potentials (2014-2021) Melting curve predictions for metals (2024) Dynamic mechanical analysis of glass formers (2022) His research shows consistent methodology using GPU-accelerated molecular dynamics (RUMD software) across diverse material systems from metallic glasses to ionic liquids. As a project participant in the "Matter" research initiative (2017-2023), Bailey collaborated with leading physicists including J.C. Dyre and Kristine Niss. His speaking engagements include significant presentations on isomorph theory at international conferences in 2017 and 2009. Bailey maintains active research output with 46 publications including 42 journal articles, 3 working papers, and datasets archived on Zenodo. His work appears primarily in high-impact physics journals including Physical Review B, Physical Review E, and Journal of Chemical Physics.

• Molecular Dynamics Simulations
• Glass-Forming Liquids
• Isomorph Theory
• +۴ مورد دیگر

Oliver Mortensen is a PhD Fellow (Research Fellow) at the Machine Learning Section , Department of Computer Science (DIKU) , University of Copenhagen , Denmark. He is affiliated with the university’s Faculty of Science and participates in the cross-faculty SCIENCE AI Centre , a strategic initiative to advance artificial intelligence research and applications. Research Interests Mortensen’s research lies at the intersection of machine learning , quantum computing , and neuro-symbolic AI . His work spans both theoretical foundations—such as entropic risk optimization in reinforcement learning and Riemannian generative models—and highly applied domains including medical AI, recommender-system fairness, and brain-computer interfaces. A recurring theme is trustworthy AI , where he investigates explainability, fairness, and sustainability across large language models and clinical decision-support systems. Scientific Contributions & Trends Across more than 60 peer-reviewed contributions (2024-2025), Mortensen demonstrates a clear trajectory toward hybrid quantum-classical algorithms , energy-efficient AI , and human-centric evaluation . His publications integrate rigorous theoretical guarantees with empirical validation on real-world data from electronic health records, satellite imagery, and conversational corpora. Collaborations & Resources He carries out his doctoral research under the supervision of Professor Yevgeny Seldin within DIKU’s vibrant Machine Learning Section. The group offers access to a dedicated high-performance compute cluster, the SCIENCE AI Centre ’s GPU/TPU pools, and interdisciplinary ties to life-science, geoscience, and humanities researchers across the university.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +۵ مورد دیگر

Ehsan Yousefzadeh-Asl-Miandoab is a Postdoctoral Researcher at the IT University of Copenhagen (ITU), Denmark, specializing in computer systems and deep learning hardware optimization. His research targets GPU efficiency challenges in large-scale AI training workloads. He earned his PhD from ITU investigating GPU underutilization in deep learning training through analytical models and system designs. His Master's research focused on microarchitectural GPU energy efficiency, proposing novel on-chip memory architectures to reduce power consumption without performance loss. Yousefzadeh-Asl-Miandoab's core research spans deep learning systems, computer architecture, and sustainable AI. He develops resource-aware approaches for GPU resource management, collocation strategies, and monitoring frameworks to enhance hardware sustainability in AI infrastructure. His 2023-2025 publications reveal a clear trajectory toward optimizing deep learning training efficiency through GPU memory management, collocation analysis, and benchmarking methodologies. These works collectively advance sustainable AI by addressing critical hardware utilization bottlenecks in modern data centers. As Co-Investigator on the RAD (Resource-Aware Data Science) project funded by the Independent Research Fund Denmark (2021-2025), he develops resource-efficient data science frameworks. His collaborative work includes a 2023 visiting researcher position at the University of Basel.

• Deep Learning
• Computer Systems
• Computer Architecture
• +۶ مورد دیگر

Claudio Pica is a Professor of Computational Science and Head of Section at the University of Southern Denmark (SDU), affiliated with the Department of Mathematics and Computer Science (IMADA) and CP3-Origins. He holds leadership roles as CEO of DeiC National HPC Center and Head of the SDU eScience Center. With a PhD in Physics from the University of Pisa (2005) and a Laurea cum Laude in Theoretical Physics from the Scuola Normale Superiore (2001), his expertise spans quantum field theory, lattice gauge simulations, and high-performance computing. His research focuses on fundamental particle physics, including composite Higgs models, walking technicolor theories, and lattice QCD. He leads large-scale computational projects, such as scaling SU(2) simulations to 1000 GPUs and optimizing algorithms for supercomputers. His outreach initiatives include award-winning educational projects like 'KvanteBanditter' (Lovie Awards 2018) and the SDU Supercomputing Challenge. He has secured € millions in grants, including the €4M European ITN ETN 'EuroPLEx' (2018-2022) and Danish Lundbeckfonden funding (2013-2018). Pica has organized major conferences like the 'Origin of Mass' series and the 'Odense Winter School on Geometry and Theoretical Physics.' His work bridges theoretical physics with computational innovation, addressing challenges in conformal dynamics, dark matter, and supercomputing infrastructure. He is a frequent speaker at international forums and a reviewer for prestigious journals like Physical Review D and European Physical Journal C.

• Quantum Field Theory
• Elementary Particle Physics
• Scientific Computing
• +۳ مورد دیگر