جست‌وجوی استاد و پژوهشگر

• Inverse Problems
• Geostatistics
• Seismology
• +۴ مورد دیگر

Klaus Mosegaard is a Professor at the University of Copenhagen's Niels Bohr Institute within the Physics of Ice, Climate and Earth department. His academic affiliation spans decades of research in computational geophysics and inverse problems methodology. His research focuses on computational aspects of inverse problems and geostatistics, with applications across seismology, planetary geophysics, geothermal investigations, and geomagnetism. Key contributions include development of algorithms for nonlinear inversion, particularly through Markov Chain Monte Carlo methods. His work bridges theoretical advances with practical applications in planetary seismology (including Mars interior studies), deep seismic lithosphere analysis, resource exploration, and thermal studies in Greenland ice shelves. Analysis of his 110+ publications reveals consistent innovation in probabilistic inversion techniques, with recent work integrating machine learning (particularly generative adversarial networks) with traditional geostatistical methods for reservoir characterization and facies modeling. His 2023-2025 publications demonstrate ongoing leadership in addressing dimensionality challenges in large-scale inverse problems. Best Paper Award Computers and Geosciences 2008 The Ole Rømer Grant for outstanding research in geophysics (1994) Teaching Award, Faculty of Science, University of Copenhagen (2000) Honorable Mention for Best Papers in Geophysics journal (2011) Mosegaard has delivered 25+ invited lectures on Bayesian inversion paradigms, Monte Carlo methods, and seismic inversion since 2020, including presentations at major international conferences. His research profile shows sustained external collaborations across planetary science and petroleum geophysics fields, with significant contributions to Mars interior modeling through InSight mission data analysis.

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +۳ مورد دیگر

Lennard Hilgendorf is a Researcher at the Department of Computer Science , University of Copenhagen . His work focuses on Machine Learning with applications in quantum computing, medical imaging, natural language processing, and environmental sustainability. Research Trends: His recent publications highlight interdisciplinary work at the intersection of quantum mechanics and machine learning, efficient AI architectures for environmental sustainability, explainable models for medical diagnostics, and multimodal approaches to ecological monitoring. Key keywords include Machine Learning , Quantum Computing , Medical AI , and Environmental Science . Labs & Collaborations: Affiliated with the SCIENCE AI Centre and the TreeSense Centre , which specialize in foundational machine learning research and remote sensing for global tree resources, respectively.

• Machine Learning
• Computational Biology
• Protein Engineering
• +۳ مورد دیگر

Richard Michael is a PhD Fellow and Guest Researcher at the Machine Learning section of the Department of Computer Science , University of Copenhagen. His work focuses on bridging machine learning with computational biology, particularly in protein engineering and optimization of discrete sequences. Research Interests High-dimensional Bayesian optimization for discrete sequences Regression models in biomolecular design Continuous relaxation techniques for discrete problems Fitness landscape analysis in protein engineering Gaussian processes for predictive modeling Publication Trends Recent work highlights interdisciplinary approaches combining machine learning with computational biology. Key areas include optimization of discrete sequences (e.g., proteins or DNA), systematic benchmarking of regression models for biomolecular tasks, and development of novel computational frameworks for high-dimensional problems. His publications span both conference proceedings (e.g., NeurIPS) and peer-reviewed journals (e.g., PLOS Computational Biology). Contact Email: richard.michael@di.ku.dk

Christian Fischer Pedersenمشاهده پروفایل

دانشیار

• Signal processing
• Machine learning
• Distributed systems

Christian Fischer Pedersen is an Associate Professor at the Department of Electrical and Computer Engineering , Aarhus University School of Engineering. His research focuses on signal processing, machine learning, and distributed systems, with applications in biomedical engineering and industrial diagnostics. Signal processing for healthcare and mechanical systems Machine learning applied to survival analysis and text mining Distributed systems for medical and industrial applications Recent publications highlight his work in explainable AI for pediatric arthritis detection, probabilistic neural networks for survival analysis, and MEMS sensor applications in mechanical diagnostics. His projects include European collaborations like the PRECISE fall-risk prevention initiative. Contact: cfp@ece.au.dk

Thomas Bakمشاهده پروفایل

استاد

• Robotics
• Control Systems
• Assistive Technology
• +۵ مورد دیگر

Thomas Bak is Professor and Dean of The Technical Faculty of IT and Design at Aalborg University, Denmark, where he maintains an active research profile while leading the faculty. His office is located at Fredrik Bajers Vej 1, A.2.23, 9220 Aalborg East. Professor Bak's research primarily focuses on robotics, particularly assistive robotics for individuals with disabilities. His work spans exoskeleton design, computer vision-based control systems, and rehabilitation engineering. Key research areas include human-robot interaction, control systems, and AI applications in assistive technologies. His research has significant practical applications, especially in developing technologies that improve quality of life for people with severe disabilities. Analysis of his recent publications reveals a strong emphasis on upper-limb exoskeletons for individuals with tetraplegia, with a focus on clinical validation, user-centered design, and semi-autonomous control systems. His work bridges engineering, computer science, and rehabilitation medicine, demonstrating interdisciplinary collaboration across multiple fields. Recent trends show increasing integration of AI and probabilistic methods in robotics applications. Professor Bak has secured significant research funding, including grants from the Louis-Hansen Foundation for rehabilitation robotics and AAU strategic grants for projects like EXOTIC (Assistive personal robotics platform with an exoskeleton using the tongue for intelligent control). He has collaborated extensively with researchers across multiple disciplines, including medical professionals and engineers. He serves as principal investigator or supervisor for numerous PhD students and research projects. His leadership extends to institutional roles, including chairing committees related to AI strategy at Aalborg University, as evidenced by media coverage of the 'Ny stor AI-satsning på Aalborg Universitet' (New major AI initiative at Aalborg University). Professor Bak is actively involved with robotics research centers and collaborates with institutions like FORCE Technology. His work on the Center for Rehabilitation Robotics demonstrates his commitment to translating research into practical applications that benefit individuals with disabilities through technologies like the EXOTIC exoskeleton platform.

• Forensic Genetics
• DNA Profiling
• STR Analysis
• +۲ مورد دیگر

Helle Smidt Mogensen is a Researcher and Forensic Geneticist at the Section of Forensic Genetics within the Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen. Employed since 1997, she has served as Laboratory Manager for the STR laboratory since 1998, overseeing critical forensic DNA analysis operations. Her academic credentials include: Master of Science (Cand. scient.) in Biochemistry with neuropharmacology specialization (1993) PhD thesis: "Quantitation of glutamate receptor subunit mRNAs in primary neuronal cultures: A comparative study of cultured mouse and rat cerebellar granule cells" (1997) Mogensen's research centers on STR typing of biological traces , interpretation of complex STR results , and next-generation sequencing applications in forensic genetics . Her work bridges molecular biology, statistical modeling, and practical forensic investigation, significantly enhancing DNA evidence reliability in criminal cases through rigorous methodological development. Analysis of her 2022-2026 publications reveals dominant trends in Y-STR paternal lineage identification, SNP genotyping via advanced statistical models, and optimization of DNA extraction from challenging samples like telogen hair. Her research consistently integrates computational approaches with laboratory validation, addressing critical gaps in forensic DNA interpretation for complex mixtures and low-template samples. Mogensen actively participates in international collaborative research and forensic laboratory proficiency initiatives. Her professional development includes extensive leadership training (2010-2014), specialized forensic genetics coursework, and technical certifications in DNA analysis instrumentation and software systems. As STR Laboratory Manager, she directs forensic casework analysis and reference sample processing while pioneering next-generation sequencing adoption. Her team maintains ISO/IEC 17025 accreditation standards and contributes to international forensic genetics guidelines through method validation studies and interlaboratory exercises.

• Machine Learning
• Causal Inference
• Artificial Intelligence
• +۲ مورد دیگر

Theo Rüter Würtzen serves as an Instructor in the Department of Computer Science at the University of Copenhagen, specializing in causal inference and machine learning methodologies. His research focuses on: Developing algorithms for causal structure learning Quantifying uncertainty in graphical models Advancing probabilistic reasoning frameworks Optimizing adjustment identification techniques His 2024 UAI conference publication introduces a novel distance metric for causal discovery, demonstrating significant contributions to machine learning theory with applications in high-dimensional data analysis. This work reflects current trends in interpretable AI and robust causal inference methods.

Harrison Bo Hua Zhuمشاهده پروفایل

استادیار

• Probabilistic Machine Learning
• Deep Learning
• Infectious Disease Modeling
• +۷ مورد دیگر

Harrison Bo Hua Zhu is an Assistant Professor at the Section for Health Data Science and AI within the Department of Public Health at the University of Copenhagen. He joined the university in November 2024 and is also a member of the Machine Learning and Global Health Network and part of the research group led by Samir Bhatt and David Duchêne. His educational background includes: PhD in Modern Statistics and Statistical Machine Learning from Imperial College London (2019-2023), supervised by Seth Flaxman and Yingzhen Li MSci in Mathematics from Imperial College London (2015-2019), with an exchange year at École Polytechnique Fédérale de Lausanne Dr. Zhu's research focuses on developing probabilistic machine learning methods for public health applications, particularly in infectious disease modeling and phylogenetics. His work bridges theoretical advances in Gaussian processes and Bayesian deep learning with practical epidemiological challenges. He specializes in creating scalable models that can handle multimodal data sources including time series, satellite imagery, and genomic sequences. His publication record shows a clear progression from foundational machine learning methodology to impactful public health applications, with significant contributions during the COVID-19 pandemic as part of the Imperial College London Response Team. Recent work demonstrates expertise in Markovian Gaussian Process Variational Autoencoders and multimodal learning approaches for health data science. Dr. Zhu actively supervises students at multiple levels: Current PhD students: Mathilde Marie Brünnich Sloth (life-course epidemiology) and Mariya Pavlova (Climate Change AI) Previous students: Alexander Pondaven (MEng thesis on diffusion models), Qing Pan (MSc on meta-learning), and Gengjian Hu (MSc on climate data compression) He maintains strong industry connections through past work at Fano (2023-2024) and an internship at Amazon, demonstrating the practical applicability of his research. His GitHub activity shows active development of machine learning tools, including implementations of neural processes and Gaussian process models.

Teddy Grovesمشاهده پروفایل

پژوهشگر

• Metabolic Networks
• Bayesian Regression
• Cell Metabolism
• +۱۲ مورد دیگر

Teddy Groves is a Tenure Track Researcher at the Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark (DTU), focusing on quantitative modeling of cell metabolism and computational methods. His work bridges systems biology, machine learning, and biotechnology. Research Themes : Quantitative cell metabolism, neurovascular coupling, CRISPR-based apoptosis resistance, and data-driven bioreactor optimization. Supervision : Actively supervises PhD students in projects related to human glycolysis, multiscale modeling, and cyanobacteria enzyme allocation. Key Contributions : Developments in Bayesian regression, high-dimensional pathway visualization, and CRISPR knockouts in CHO cells.

Mahya Mohammadi Kashaniمشاهده پروفایل

پژوهشگر

• Underwater Robotics
• Risk Assessment
• Probabilistic Planning
• +۳ مورد دیگر

Mahya Mohammadi Kashani is a Researcher in Software Engineering at the IT University of Copenhagen, specializing in risk assessment for underwater robotics. She serves as an Early Stage Researcher in the REMARO (Reliable AI for Marine Robotics) network, a European Commission-funded project advancing trustworthy AI for marine applications. Her academic background includes: PhD in Computer Science (2021-2024), IT University of Copenhagen: Focused on statistical assessment of plans via probabilistic optimization of reliability. MSc in Artificial Intelligence and Robotics (2016-2019), Shahid Rajaee Teacher Training University: Researched search-based image annotation using deep models. BSc in Software Engineering (2010-2014): Developed filter drivers for disk access control at low-level driver hierarchies. Her research develops Bayesian-inference-based methods for risk-averse decision-making in underwater robotics, creating probabilistic models to assess operational risks and select reliable robot plans. Previously, she investigated pattern recognition algorithms, sparse reconstruction/coding techniques, and search space reduction for automatic image annotation systems. Her publications (2022-2025) establish a cohesive framework for risk-aware marine robotics, addressing fault recovery, risk quantification, and plan assessment through probabilistic optimization and belief-based planning models. No scientific awards were documented in the source material. Funded by the REMARO project (2020-2025), she contributes to European Commission research on trustworthy AI, active learning, and image segmentation for underwater robotics as a co-investigator. Her collaborative work spans risk-averse planning methodologies and reliability engineering for autonomous marine systems. Within the REMARO network, she collaborates on advancing reliable AI for marine robotics through active learning techniques and image segmentation solutions for challenging underwater environments.

Tommy Sonne Alstrømمشاهده پروفایل

دانشیار

• Machine Learning
• Signal Processing
• Nanotechnology
• +۳ مورد دیگر

Tommy Sonne Alstrøm is an Associate Professor at the Department of Applied Mathematics and Computer Science, DTU Compute, Danmarks Tekniske Universitet (DTU). His research focuses on machine learning, signal processing, and nanotechnology-based sensor systems. Key research areas include: Explainable AI for time series and spectroscopy Diffusion models for speech enhancement Federated learning optimization Probabilistic modeling of biosignals His recent publications demonstrate expertise in neural network geometry, federated learning privacy, and spectroscopic data analysis. Current work involves self-supervised learning for variable-channel time series and road condition modeling using LiRA-CD dataset. Contact: tsal@dtu.dk

• Probabilistic Machine Learning
• Deep Probabilistic Programming
• Statistical Structural Bioinformatics
• +۵ مورد دیگر

Thomas Wim Hamelryck is a Professor at the Department of Computer Science (Programming Languages and Theory of Computation) and the Department of Biology (Computational and RNA Biology) at the University of Copenhagen . With a PhD in Protein Crystallography from the Free University of Brussels (VUB), he specializes in Bayesian modeling , probabilistic machine learning , and statistical structural bioinformatics , focusing on protein structure prediction, evolution, and non-Euclidean data representation.

• Machine Learning
• Medical Image Analysis
• Computer Vision
• +۲ مورد دیگر

Nicklas Boserup serves as an Instructor at the Department of Computer Science (DIKU) at the University of Copenhagen, located at Universitetsparken 1 in Copenhagen Ø. His academic appointment places him within the institution's research and teaching structure, contributing to computer science education and research activities. Boserup's research interests center on machine learning applications in medical imaging, with particular focus on self-supervised learning techniques for histopathology image analysis. His work bridges computer vision and healthcare applications, developing algorithms that can process and segment medical images without requiring extensive labeled training data. This research direction addresses critical challenges in digital pathology where annotation by medical experts is time-consuming and costly. Analysis of Boserup's publication record shows a consistent focus on contrastive learning approaches for medical image segmentation, with his 2022-2023 work establishing foundational techniques in patch-based contrastive learning for histopathology. His 2024 publication represents an expansion into parameter inference methods using differentiable diffusion models, indicating a broadening research scope into statistical computing and probabilistic modeling. The trajectory suggests increasing sophistication in handling complex medical imaging data through advanced machine learning techniques. Boserup maintains an active research profile with publications appearing in both preprint repositories and conference proceedings, demonstrating engagement with the academic community. His email contact (nibos@di.ku.dk) provides a direct channel for academic collaboration and professional communication within the university framework.

• Programming Languages
• Theory of Computation
• Formal Verification
• +۳ مورد دیگر

Atta Samiullah serves as an Instructor in the Department of Computer Science at the University of Copenhagen, affiliated with the Programming Languages and Theory of Computation (PLTC) section. His role encompasses teaching responsibilities across undergraduate and graduate computer science programs including BSc in Computer Science, Computer Science and Economics, Machine Learning and Data Science, and Communication & IT. His research profile aligns with PLTC's core focus areas: Programming language technology and formal verification methodologies Theory of computation with applications in distributed ledger systems Security and privacy preservation techniques in software systems Financial technology applications and blockchain infrastructure Compiler technology and high-performance computing architectures Quantum programming language design The PLTC section operates through specialized research groups including Decentralized Systems, Security & Privacy, HOT Lab, FUTHARK (Functional Technology for Modern Architectures), and Quantum Programming. These groups maintain active industry collaborations in fintech and blockchain sectors while developing open-source tools like Moscow ML and MLKit compilers. Research output spans probabilistic programming frameworks, Stein variational inference techniques, and applications in bioinformatics and vaccine design. No scientific awards or student supervision activities are documented in current institutional records. His work contributes to the department's mission in advancing programming language theory with practical implementations in secure financial systems and distributed computing environments.

Ana Alina Tudoranمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Behavior Analysis
• +۷ مورد دیگر

Ana Alina Tudoran is an Associate Professor at Aarhus University, specializing in quantitative methods within business intelligence. Her academic work bridges machine learning, data mining, and consumer behavior analysis. Primary affiliation: Department of Economics Research focus areas: Artificial Intelligence, Customer Lifetime Value modeling, and Hybrid Intelligence systems Notable projects include pandemic-era consumer behavior studies and IoT privacy reviews Her recent publications span supply chain optimization, organizational text analytics, and responsible AI deployment. Tudoran actively contributes to methodological advancements through hybrid PLS-SEM/machine learning frameworks and is a frequent speaker at management conferences.