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Søren HaubergView profile

Professor

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

Søren Hauberg is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the Cognitive Systems group. His research lies at the intersection of machine learning, geometry, and probabilistic modeling, with applications in life sciences and computer vision. Department: Applied Mathematics and Computer Science Research Group: Cognitive Systems Institution: Technical University of Denmark (DTU) His research interests center on geometric deep learning , Bayesian inference , and manifold-based modeling . He explores how stochastic and geometric structures can improve machine learning models, particularly in modeling complex data such as biological sequences and medical images. His work integrates Riemannian geometry, Gaussian processes, and energy-based models to build more robust and interpretable AI systems. The recent publications reveal a strong trend toward geometric representation learning , fairness in generative models , and computational methods for biological data . His team investigates latent space geometries, develops efficient GPU-based numerical algorithms, and applies foundational models to protein sequences. There is a clear emphasis on both theoretical rigor and practical implementation. Søren Hauberg actively supervises multiple PhD students and leads key projects in machine learning for life sciences. He is a project manager in the Center for Basic Machine Learning Research in Life Science , indicating leadership in interdisciplinary research. He also contributes to high-performance computing applications in statistical modeling. He is involved in several active research projects, including: Geometric Bayesian Deep Learning Stochastic Riemannian Geometry with Applications to Data Modelling The geometry of protein representations AI-driven Electron Tracking for High-Energy Radiation Detection

Troels PedersenView profile

Associate Professor

• Wireless Communication
• Radio Channel Modeling
• MIMO Systems
• +5 more

Troels Pedersen is an Associate Professor at the Department of Electronic Systems, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on stochastic radio channel modeling, MIMO systems, radar signal processing, and positioning technologies. Key projects include WHERE2 and NEWCOM++, addressing wireless hybrid estimators and network excellence in communications. Expertise in reverberant channel modeling, propagation graphs, and Bayesian inference methods Developed models for in-room radio channels using point processes and Galton-Watson trees Contributions to MIMO radar tracking, clutter mitigation, and drone swarm micro-Doppler analysis Active in wireless positioning, cooperative networks, and IoT interference modeling with 99+ publications in journals/conferences. Supervised 1 PhD candidate and contributed to EU-funded research initiatives.

Mark PodolskijView profile

Professor

• Mathematical Statistics
• Probability Theory
• Stochastic Processes
• +3 more

Mark Podolskij is an Honorary Professor at the Department of Mathematics, Aarhus University. Since 2006, he has contributed over 70 research outputs, focusing on mathematical statistics, probability theory, and stochastic processes. His work emphasizes asymptotic theory, statistical inference, and applications in financial markets. He holds editorial roles in journals like Bernoulli and Scandinavian Journal of Statistics . Research Interests: His primary areas include stochastic processes, limit theorems, and high-dimensional statistical methods. Notable contributions involve quadratic variation analysis, heavy-tailed distributions, and Lévy processes. Scientific Awards: He secured prestigious grants, including the ERC Consolidator Grant (2018) and Villum Fonden funding (2015). These grants supported projects on statistical methods for diffusions and probabilistic inference in ambit fields. Grants/Editorial Roles: Podolskij has led or co-investigated research projects totaling over €10M. His editorial work spans multiple journals, reflecting his influence in academic publishing.

Lys Sanz MoretaView profile

Research Fellow

• Molecular Biology
• Machine Learning
• Probabilistic Programming
• +3 more

Lys Sanz Moreta is a Research Fellow at the University of Copenhagen, holding dual appointments as a Postdoc in the Department of Cellular and Molecular Medicine (Faculty of Health and Medical Sciences) and a Guest Researcher in the Department of Computer Science's Programming Languages and Theory of Computation group. Her work bridges molecular biology and advanced machine learning to solve complex biomedical problems. Education PhD in Computer Science, University of Copenhagen, 2022 Dr. Moreta specializes in interdisciplinary research, applying Bayesian modeling and deep learning to molecular biology challenges. Her expertise spans protein structure prediction, viral epitope analysis, and probabilistic programming, enabling cross-disciplinary collaborations between computational and biomedical fields. She emphasizes optimizing programming and mathematical skills to connect diverse knowledge domains. Her 2019-2025 publications reveal a consistent focus on integrating machine learning with structural biology and immunology, particularly through variational autoencoders and probabilistic frameworks for protein superposition, ancestral sequence resurrection, and viral T-cell epitope prediction. This research has significant implications for drug design and molecular evolution studies. Scientific Awards No major scientific awards are documented in the provided profile. While no student advising or grant details are listed, her active roles in the Glycomics Program and Programming Languages research group suggest ongoing collaborative projects. Her interdisciplinary approach facilitates knowledge transfer between computer science and biomedical research teams. She maintains affiliations with the Glycomics Program (Department of Cellular and Molecular Medicine) and the Programming Languages and Theory of Computation group (Department of Computer Science), driving innovation at the computational-biology interface.

Fernando ColcheroView profile

Associate Professor

• Statistical eco-demography
• Bayesian Survival Trajectory Analysis (BaSTA)
• Age-structured stochastic population modeling
• +13 more

Fernando Colchero is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he conducts research in statistical eco-demography. He is a key member of the CPop research group, focusing on demographic modeling in ecological and evolutionary contexts. His work integrates advanced statistical methods with biological questions to understand survival, fertility, and movement patterns in animal populations. His research interests include: Bayesian Survival Trajectory Analysis (BaSTA) Age-structured stochastic population modeling Inference on animal movement using state-space and Markov Chain Resource Selection (MCRS) models Life history theory and aging in wild populations Conservation applications of demographic models His recent publications reveal a strong focus on longevity, aging, and population dynamics across diverse taxa, including primates, orchids, marine mammals, and endangered species. The articles span conservation biology, ecological modeling, and evolutionary demography, often emphasizing the importance of age-specific data and the pitfalls of stage-structured approximations. His work increasingly bridges wild and captive populations, with applications for ex-situ conservation and species management. He has received the Cozzarelli Prize (2016) from the National Academy of Sciences, recognizing outstanding scientific excellence and originality. His research has been supported by projects including NIH funding and collaborations with Species360 and the Dian Fossey Gorilla Fund International. Colchero has supervised student projects and taught courses such as Mathematics for Biology, Mathematics and Statistics for Pharmacy, and Statistical Modelling. He is also active in peer review, editorial work, and public engagement, frequently presenting findings in international conferences and contributing to media discussions on animal longevity and conservation.

• Theoretical Physics
• Cosmology
• Observational Cosmology
• +5 more

Asta Heinesen is a Postdoctoral Fellow at the Niels Bohr International Academy (NBIA), affiliated with the University of Copenhagen . Her research focuses on theoretical cosmology, numerical relativity, and observational cosmology, particularly in areas such as redshift drift, cosmic averaging, dark energy, cosmic expansion, and relativistic effects in the local universe. She has published extensively on cosmological tensions, inhomogeneous universe modeling, and the implications of spacetime geometry for observational data. Her recent work includes contributions to the CosmoVerse White Paper , analyses of redshift drift using numerical relativity, and studies on the dipole and quadrupole anisotropies in the local Hubble flow. She has also explored the covariance of scalar averaging and backreaction in relativistic cosmology, and the role of structure formation in dynamical spatial curvature. Her publications highlight a strong emphasis on model-independent cosmological inference, cosmic drift signals, and the interplay between theoretical physics and observational astrophysics. Key themes include cosmography, dark energy probes, and resolving cosmological tensions through relativistic frameworks.

• International Trade
• Empirical Industrial Organization
• Applied Microeconometrics
• +3 more

Magnus Tolum Buus is an Assistant Professor at the Department of Economics, University of Copenhagen, specializing in international trade and empirical industrial organization. His research focuses on firm-level trade dynamics, network effects in export markets, and causal analysis of trade policies. Email: mb@econ.ku.dk Address: Øster Farimagsgade 5, Building 26, DK-1353 Copenhagen K Research Interests : His work combines applied microeconometrics with granular Danish firm-to-firm trade data to study how export promotion services and buyer networks influence trade growth. Current projects explore R&D employment transmission in multinational networks and scale economies in globalized markets. Latest Publication : In 2024, he co-authored a Review of Economics and Statistics study analyzing export support programs' impact on firm-level pricing, quality, and costs. The research leverages natural experiments to quantify network effects, showing existing buyer relationships drive new market entries.

Thomas PockView profile

Professor

• Computer Vision
• Image Processing
• Mathematical Optimization
• +7 more

Thomas Pock is a Professor of Computer Science at Graz University of Technology, holding the AIT Stiftungsprofessur for Mobile Computer Vision. He is affiliated with the Institute for Computer Graphics and Vision (ICG) within the Faculty of Computer Science and serves as a principal scientist at the Austrian Institute of Technology (AIT), Center for Vision, Automation & Control. He leads the Vision, Learning and Optimization (VLO) research group, which focuses on mathematical modeling and optimization in computer vision. His research interests lie at the intersection of computer vision, image processing, and mathematical optimization. Specifically, he develops mathematical models for computer vision and efficient convex and non-smooth optimization algorithms , particularly for mobile scenarios. His recent work increasingly integrates variational methods with deep learning, especially in solving inverse problems in imaging such as medical reconstruction and deblurring. The trends in his recent publications show a strong emphasis on deep learning for inverse problems , variational networks , and learned optimization . His group explores how to combine classical mathematical models with data-driven deep learning approaches to achieve stable, interpretable, and high-performance solutions in image reconstruction and processing. His scientific achievements have been recognized with several prestigious awards: START Prize, Austrian Science Fund (FWF), 2013 German Pattern Recognition Award, DAGM, 2013 ERC Starting Grant, European Research Council, 2014 Thomas Pock actively mentors students and leads a research group of 10 PhD students and 2 postdocs. He has secured significant research grants, including the ERC Starting Grant, which supports his foundational work. He is also engaged in scientific communication, giving invited talks at international venues such as SIAM and co-organizing the IMAGINE One World seminar series to foster global collaboration in imaging and inverse problems. He leads the Vision, Learning and Optimization (VLO) group at the Institute for Computer Graphics and Vision. The group develops mathematical models and efficient algorithms for computer vision and image processing, with a focus on mobile applications. The team includes multiple PhD students and postdoctoral researchers and has produced notable software and publications in top venues.

Thomas BakView profile

Professor

• Robotics
• Control Systems
• Assistive Technology
• +5 more

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.

David Alejandro Duchene GarzonView profile

Associate Professor

• Molecular Evolution
• Phylogenetics
• Phylodynamics
• +5 more

David Alejandro Duchene Garzon is an Associate Professor in the Department of Public Health, Section of Epidemiology at the University of Copenhagen's Faculty of Health and Medical Sciences. His work bridges evolutionary biology with public health, focusing on the application of genomic and computational approaches to understand disease dynamics. Education: PhD in Molecular Evolution, Australian National University (2017) Bachelor of Science with Honours in Marine Biology, James Cook University Queensland (2012) Duchene's research traverses multiple fields including phylogenetic modeling, comparative analysis, biogeography, epidemiology (phylodynamics), and machine learning in public health. His past work has focused on explaining molecular evolution in both animal and pathogen genes using diverse statistical approaches. He connects processes at macro scales (macroevolution, macroecology, phylogeography) with those at micro scales (molecular evolution), addressing fundamental questions about how and why novel living beings and pathogens emerge. Currently, he is exploring the intersection of animal movement (neuroscience), infection (epidemiology), and genomics, particularly in identifying infections in livestock and wildlife using computer vision techniques. His recent publications (2024-2025) demonstrate a strong focus on applying genomic approaches to epidemiology and evolutionary biology. These works span diverse topics including infectious disease modeling, molecular clock hypothesis testing, avian genomic evolution, and SARS-CoV-2 genomic surveillance. His research combines computational methods with biological insights to address complex questions in evolution and public health. Scientific Collaborations: Collaboration with Danish COVID-19 Genome Consortium (DCGC) International collaborations across multiple countries in genomic epidemiology Work with multiple genome-sequencing consortia Duchene has established himself as a key researcher bridging evolutionary biology and public health. His work on molecular evolution provides foundational insights that inform his current epidemiological research. He has developed strong collaborative networks across institutions and countries, particularly in genomic surveillance and evolutionary analysis. His current research direction toward computer vision applications for infection identification in wildlife represents an innovative interdisciplinary approach. Duchene maintains an active research program with significant impact, as evidenced by the broad dissemination of his work across news outlets, social media, and academic platforms. His research has been featured in major scientific journals including Nature and has influenced both academic discourse and public health practice.

• Molecular Ecology
• Evolutionary Biology
• Immunogenetics
• +3 more

Jacob Nordvang Roved serves as a Guest Researcher at the Globe Institute, specifically within the Section for Molecular Ecology and Evolution at the University of Copenhagen. His institutional affiliation places him within a prominent research environment focused on evolutionary and ecological studies at the molecular level. Dr. Roved's research interests center on molecular ecology and evolutionary biology with particular emphasis on immunogenetics, especially the Major Histocompatibility Complex (MHC) in avian species. His work bridges theoretical evolutionary concepts with practical applications in conservation and biodiversity studies. He investigates how genetic variation, particularly in immune-related genes, influences survival, reproduction, and adaptation in wild populations. Analysis of his recent publications reveals a strong focus on developing and applying computational tools for MHC analysis in non-model organisms, with particular attention to songbirds and house sparrows. His research spans multiple disciplines including evolutionary immunology, behavioral ecology, and conservation genetics. A notable trend is his development of the MHCtools R package, which has become an important resource for researchers studying immunogenetic variation in wildlife populations. His collaborative work extends across international boundaries, with research covering avian migration patterns across challenging geographical features like the Qinghai-Tibet Plateau, and investigating anthropogenic impacts such as pesticide exposure on pollinator species. This demonstrates both the breadth of his research interests and his ability to address complex ecological questions through interdisciplinary approaches.

• Statistical Genetics
• Population Genetics
• Bioinformatics
• +1 more

Jan van Waaij is a Research Fellow in the Department of Mathematical Sciences at the University of Copenhagen. His work bridges statistical theory and genetic data analysis within computational biology frameworks. Core research areas include Statistical Genetics, Population Genetics, Bioinformatics, and Computational Biology. He specializes in developing statistical methodologies for interpreting genetic variation, particularly through population structure modeling and covariance estimation from genomic datasets. Recent publications (2022-2023) demonstrate consistent focus on statistical genetics methodologies. Key contributions involve principal component analysis for population structure inference and covariance modeling of SNP allele frequencies, advancing analytical tools for genetic diversity studies and ancestry research in genomics.

Harrison Bo Hua ZhuView profile

Assistant Professor

• Probabilistic Machine Learning
• Deep Learning
• Infectious Disease Modeling
• +7 more

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.

Jakob Sauer JørgensenView profile

Researcher

• Image Reconstruction
• Sparsity
• Regularization
• +4 more

Jakob Sauer Jørgensen serves as a Senior Researcher in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), affiliated with the UltraSound and Biomechanics Scientific Computing group within the Center for Fast Ultrasound Imaging. His work spans computational mathematics, medical imaging, and uncertainty quantification. His research focuses on Image Reconstruction , Tomographic Reconstruction , and Uncertainty Quantification with emphasis on regularization techniques, sparsity, and Bayesian approaches. Key methodologies include total variation, inverse problems, and computational frameworks like CUQIpy for Python-based uncertainty quantification in inverse problems. His publication trends show strong emphasis on computational methods for medical imaging (2025), uncertainty quantification frameworks (2024-2025), and applications in nuclear fusion diagnostics (2024). Recent work integrates neural networks with traditional reconstruction methods and develops open-source tools for uncertainty modeling. He actively supervises PhD students across multiple projects including neural network-based X-ray tissue imaging, Mars meteorite analysis, and additive manufacturing modeling. Current projects include: Neural networks-based X-ray tissue imaging for robotic automation (2025-2028) Multi-modality reconstruction for Mars meteorite analysis (2024-2027) Modeling of volumetric additive manufacturing (2023-2026) Uncertainty quantification for tomographic reconstruction (2020-2024)

Kim Rose OlsenView profile

Professor

• Applied Econometrics
• Health Economics
• Primary Health Care
• +7 more

Kim Rose Olsen is a Professor at the Department of Public Health, University of Southern Denmark, and a key member of the Danish Centre for Health Economics (DaCHE). Her research centers on applied econometrics in primary healthcare, focusing on remuneration models, care quality, and resource utilization. She actively contributes to health policy discourse through academic research and public media engagement. Research Interests: Her work investigates organizational and behavioral dynamics in primary care, particularly how payment systems affect general practitioner behavior, the relationship between primary and secondary care utilization, and the economic impact of multi-morbidity. She employs advanced econometric and machine learning methods to analyze large-scale healthcare data. Recent Research Trends: Her most recent publications (2024–2025) reflect a growing integration of machine learning in health economics, especially in diabetes management, alongside continued focus on rehabilitation equity, dizziness outcomes, and vertebral fracture mortality. These works demonstrate a strong emphasis on register-based cohort studies and policy-relevant findings. Scientific Engagement: While no formal awards are listed, her research has been widely recognized through media contributions and interdisciplinary collaborations. She has led and co-led major research projects with European and national impact. Teaching and Supervision: Olsen has taught courses such as 'Fra Data til Evidens' (From Data to Evidence), contributing to health data literacy. Though specific students are not named, she has supervised academic work, including PhD theses and conference presentations. Laboratories and Research Groups: She is affiliated with DaCHE, a leading center for health economics research in Denmark, where she collaborates on projects like PINCH and MSCA-ETN IQCE, focusing on improving healthcare quality and societal impact of research.