جستجوی استاد راهنمای دانشگاهی

Jes Frellsenمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Generative AI
• Deep Learning
• +۵ مورد دیگر

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

• Population Genetics
• Medical Genetics
• Statistical Genomics
• +۵ مورد دیگر

Ida Moltke is an Associate Professor in the Department of Biology at the University of Copenhagen's Faculty of Science, specializing in Computational and RNA Biology. She leads a research group focused on developing and applying statistical and computational methods to genomic data. Her work spans both population genetics and medical genetics, with particular emphasis on human evolution, population history, and identifying genetic variants associated with diseases like type 2 diabetes. Current position: Associate Professor (Promotion Programme), Department of Biology, University of Copenhagen (2024-present) Previous positions: Associate Professor (2020-2024), Assistant Professor (2015-2019) Education: PhD in Biology (2011), MSc in Bioinformatics (2007), BSc in Computer Science and Math (2005), all from University of Copenhagen Dr. Moltke's research focuses on developing novel bioinformatics tools to analyze genomic data, with applications in understanding human evolution and identifying genetic factors in diseases. Her group has made significant contributions to studying Greenlandic populations and their unique genetic architecture. She has developed several widely used software packages including NgsRelate, localNgsRelate, and RelateAdmix for analyzing relatedness from low-coverage sequencing data. Her recent publications span diverse topics from human population history to conservation genomics of endangered species. Notable work includes studies on genetic architecture in Greenlandic populations, analysis of the saola genome, and research on diabetes genetics. Her research has been published in high-impact journals including Nature, Cell, and Nature Communications. Carlsberg Foundation Young Researcher Fellowship (2021-2025) ERC Starting Grant (2019-2023) Villum Young Investigator Award (2019-2024) L'Oréal-UNESCO For Women in Science Award (2016) SSSD Young Investigators Award (2015) Sapere Aude: DFF Young Elite Researcher Award (2012) Dr. Moltke actively mentors students, with her PhD student Frederik winning multiple awards including the Charles J. Epstein Award. She serves on PhD committees at multiple institutions and has organized numerous scientific meetings including EMBO conferences. Her group has developed multiple bioinformatics tools that are widely used in the field for analyzing relatedness from low-coverage sequencing data.

• Evolutionary Genetics
• Population Genetics
• Ancient DNA Analysis
• +۴ مورد دیگر

Rasmus Nielsen is a Professor at the University of Copenhagen, specifically affiliated with the Globe Institute and the Section for Geogenetics. His research spans evolutionary genetics, population genetics, and computational genomics with a focus on both human and non-human species. He maintains an active research profile with numerous high-impact publications in leading scientific journals. Professor Nielsen's research interests center around evolutionary and population genetics, with particular expertise in ancient DNA analysis, statistical methods for genomic data, and phylogenetics. His work bridges computational biology with empirical data from diverse species, including humans, plants, and other organisms. He has made significant contributions to understanding human evolutionary history, population structure, and the genetic basis of adaptation. His research also extends to medical genomics, particularly in understanding the genetic architecture of complex traits and diseases. His recent publications demonstrate a strong focus on methodological development in genomic analysis, with papers appearing in top-tier journals like Nature , Science , and Nature Reviews Genetics . His work shows consistent engagement with both theoretical and applied aspects of genetics, spanning human evolutionary history, medical genomics, and plant evolutionary biology. The research output reveals a collaborative approach, with frequent co-authorship across multiple institutions and disciplines. As a Professor at the University of Copenhagen's Globe Institute, Nielsen leads research within the Section for Geogenetics, which focuses on evolutionary and population genetics using cutting-edge genomic approaches. The section maintains strong connections with both computational and empirical research groups, facilitating interdisciplinary work that spans from methodological development to application in diverse biological contexts.

• Population Genetics
• Bioinformatics
• Genomic Data Analysis
• +۶ مورد دیگر

Anders Albrechtsen is a Professor at the Bioinformatics Centre, Department of Biology, University of Copenhagen, specializing in computational and RNA biology. He leads research in statistical and computational methods for genomic data analysis with a focus on population genetics, particularly in isolated populations like the Greenlandic Inuit. Education: Natural Science Basic Education, Roskilde University Centre, Denmark (2000-2002) BSc in Molecular Biology, Roskilde University Centre, Denmark (2003) Mathematics, Roskilde University, Denmark (2002-2004) MSc in Bioinformatics, Copenhagen University, Denmark (2006) PhD from the Department of Biostatistics, Copenhagen University, Denmark (2009) Albrechtsen's research focuses on developing statistical and computational methods for genomic data analysis, with particular emphasis on multi-loci association studies, population structure analysis, and next-generation sequencing data. His work bridges computational methods development with applied population genetics, contributing significantly to understanding human and wildlife genomics. His research group develops open-source software available at www.popgen.dk/software. His recent publications demonstrate expertise across population genetics, genomic methods development, and medical applications, with numerous high-impact papers in journals like Nature, Cell, and Nature Communications. His work often involves large-scale genomic studies of human populations (particularly Greenlandic Inuit) and wildlife species, addressing questions related to population history, adaptation, and disease genetics. Scientific Recognition: Lundbeck Fellow (10M DKK, 2016-2021) Novo Ascending Investigator (10M DKK, 2021-) Villum Young Investigator Programme (2.3M DKK, 2011-2015) Albrechtsen actively supervises students and researchers, currently serving as main supervisor for 6 PhD students (with 8 completed), 2 postdocs, and 1 master's student (with 25 completed). He has secured significant research funding as PI and co-PI, including large collaborative grants like LuCamp (60M DKK) and the UCPH Excellence Programme (36M DKK). His research group maintains strong international collaborations, particularly with institutions in the United States. He serves as a journal reviewer for prestigious publications including Nature Genetics, Genome Research, and American Journal of Human Genetics, contributing to the scientific community through peer review and mentorship.

Michael Sørensenمشاهده پروفایل

استاد

• Statistical Inference for Stochastic Processes
• Stochastic Differential Equations
• Financial Mathematics
• +۳ مورد دیگر

Michael Sørensen is a Professor at the Department of Mathematical Sciences, University of Copenhagen. His primary research focuses on statistical inference for stochastic processes, particularly stochastic differential equations and jump processes, with applications in finance, physics (e.g., wind-blown sand dynamics), and biology. He has authored/co-authored influential books such as Exponential Families of Stochastic Processes and edited volumes on empirical process techniques and statistical methods for stochastic differential equations. His work bridges theoretical statistics with applied problems in natural sciences and finance. Research interests include modeling turbulence, sand transport dynamics, and protein structure evolution. Collaborations with earth scientists like Keld Rømer Rasmussen have advanced understanding of aeolian processes. His methodologies emphasize likelihood-based inference and estimating functions, with contributions to high-frequency data analysis and diffusion bridge simulations. A comprehensive CV and full publication list are available on his profile. Key contributions span stochastic modeling in physics (e.g., sand dune dynamics), financial econometrics, and computational statistics. He has pioneered techniques for analyzing multi-modal diffusions and developed frameworks for mixed-effects stochastic differential equations. His work is widely cited in both theoretical and applied statistical literature.

Adelina Rogowska-Wrzesinskaمشاهده پروفایل

دانشیار

• Proteomics
• Mass Spectrometry
• Oxidative Stress
• +۳ مورد دیگر

Adelina Rogowska-Wrzesinska is an Associate Professor at the Department of Biochemistry and Molecular Biology, University of Southern Denmark. Specializing in Biomedical Mass Spectrometry and Systems Biology , her research spans proteomics, oxidative stress, protein oxidation, and 3D cell culture models. Affiliation : Department of Biochemistry and Molecular Biology, University of Southern Denmark Academic Role : Teaching and research leadership Her research interests focus on: Redox biology and protein oxidative modifications 3D spheroid models for disease and toxicity studies Mass spectrometry-based proteomics and lipidomics Oxidative stress in cellular senescence and disease Plant stress responses and proteomics Scientific Trends : Recent publications highlight advancements in 3D culture modeling (NAFLD, genotoxicity), redox proteomics (glutathionylation, disulfide bonds), and multi-omics integration (chromatin proteomics, lipidome mapping). Teaching and Supervision : She has supervised multiple PhD students (e.g., Christina Erika Hagensen, Helle Frandsen) and taught courses like Basic Biochemistry (BMB530) and Fundamental Molecular Biology (BMB504) .

Svante Eriksenمشاهده پروفایل

دانشیار

• Statistics
• Bayesian Networks
• Graphical Models
• +۷ مورد دیگر

Svante Eriksen is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His research spans statistics, forensic genetics, and computational modeling, with a focus on Bayesian networks, graphical models, and statistical methods for forensic DNA analysis. He is actively involved in interdisciplinary research and software development for probabilistic genotyping and large-scale inference. Research Interests: Bayesian Networks and Graphical Models Statistical Methods in Forensic Genetics SNP and Y-STR Genotyping Data Mining and Knowledge Discovery Model Selection and Context-Specific Independence Software Development for Statistical Inference Recent Publication Trends (2024–2025): His recent work focuses on improving SNP genotyping accuracy using logistic regression models, developing efficient software (jti and sparta) for Bayesian network inference, and advancing forensic DNA analysis through dynamic SNP selection and probabilistic modeling of Y-STR databases. These contributions reflect a strong integration of statistical theory, computational efficiency, and real-world forensic applications. Scientific Contributions: Principal contributor to software packages for Bayesian network prediction. Developer of statistical models for forensic DNA data interpretation. Collaborator on projects involving digital learning analytics and student retention. Advising and Grants: While specific student names are not listed, the profile indicates involvement in PhD supervision (4 cases). He has participated in multiple externally funded research projects, including those supported by Novo Nordisk and Danish research councils, focusing on forensic DNA analysis, graphical models, and educational data mining. Research Groups and Collaborations: He is part of a strong research network in forensic genetics and statistical modeling at Aalborg University, collaborating with leading researchers such as N. Morling, M. M. Andersen, and T. Tvedebrink. His work is closely tied to the development and application of statistical software in both forensic and educational domains.

• Gene regulation
• Bioinformatics
• RNA Biology
• +۶ مورد دیگر

Albin Gustav Sandelin is a Professor in the Department of Biology at the University of Copenhagen, specializing in bioinformatics and RNA biology. With a PhD in Functional Genomics from Karolinska Institute (2004) and an MSc in Molecular Biology from Stockholm University (2000), his career spans roles at institutions including RIKEN Yokohama Institute and Tokyo Medical and Dental University. His research focuses on Gene regulation mechanisms Transcription start site dynamics RNA sequencing methodologies Promoter architecture analysis Cancer microenvironment interactions . Recent publications highlight his work in pancreatic cancer adaptation to acidic environments (2024-2025), TLDR-seq for RNA analysis, and JASPAR database development. He supervises PhD students including Eivind Valen and Eric Man-Hung, with a history of teaching bioinformatics courses at Karolinska Institute and Copenhagen University. Key collaborations involve institutions in Japan, Canada, and Sweden, with ongoing contributions to open-access genomic resources like JASPAR.

• Machine Learning
• Natural Language Processing
• Information Retrieval
• +۳ مورد دیگر

David Robert Shannon is an Instructor at the Department of Computer Science, University of Copenhagen. He contributes to teaching and research within the Machine Learning section, which participates in the SCIENCE AI Centre. University: University of Copenhagen Department: Department of Computer Science Section: Machine Learning His research interests span theoretical and applied machine learning, focusing on natural language processing, information retrieval, medical image analysis, computational biology, and quantum computing applications. He utilizes the department's powerful compute cluster for projects involving AI's environmental impact, quantum algorithms, and biomedical data modeling. Recent publications highlight work in quantum-inspired neural networks, sustainable AI, medical diagnostics, and cross-cultural computational frameworks. Key themes include ethical considerations in AI, hybrid quantum-classical systems, and multimodal data analysis. David collaborates with the Machine Learning section and SCIENCE AI Centre, leveraging resources like TreeSense for remote sensing and deep learning of global tree resources. The section's activities range from foundational research to applications in sustainability and biological data modeling.

Andres Masegosaمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Networks
• Data Streaming
• +۴ مورد دیگر

Andres Masegosa is an Associate Professor at the Department of Computer Science, Aalborg University (AAU), within The Technical Faculty of IT and Design. He is actively involved in the DarkScience project (2022–present), focusing on metagenomic binning and microbial dark matter analysis. His research interests span Bayesian networks, machine learning, probabilistic graphical models, and educational methodologies in computer science instruction. Key research contributions include advancements in PAC-Bayes theory, genome representation learning, and cold posterior effects in Bayesian models. He has published extensively in top venues like Advances in Neural Information Processing Systems and Transactions on Machine Learning Research. His work often bridges theoretical contributions with practical applications in genomics and education. Masegosa leads the development of tools like InferPy for probabilistic modeling and has contributed to open-source projects such as the AMIDST toolbox. His educational research explores learning styles and active learning strategies, emphasizing live coding and programming exercises. Collaborations include interdisciplinary projects with microbiologists and data scientists, reflecting his expertise in computational methods for complex biological systems. His research portfolio demonstrates a strong focus on scalable probabilistic methods and their real-world applications.

Søren Haubergمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Inference
• Geometric Deep Learning
• +۱۱ 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

• Ancient DNA
• Population Genomics
• Bioinformatics
• +۴ مورد دیگر

Rasmus Henrik Amund Henriksen is a Guest Researcher at the Globe Institute, Section for Geogenetics, University of Copenhagen. His research focuses on ancient DNA analysis, population genomics, and computational biology methodologies. He maintains an active research profile with publications spanning multiple high-impact journals including Nature and Molecular Ecology Resources. His research interests include Ancient DNA analysis, Population Genomics, Bioinformatics, Computational Genomics, Molecular Evolution, Conservation Genetics, and Circular DNA Research. Dr. Henriksen's work bridges computational methods with biological applications, developing tools like NGSNGS for next-generation sequencing data simulation while applying these techniques to evolutionary questions. His publication record shows a strong focus on ancient DNA methodologies and population history, with recent work examining post-glacial western Eurasia, Neolithic Denmark population turnovers, and Cuban hutias species diversity. His research demonstrates expertise in both computational tool development and biological applications of genomic technologies. Dr. Henriksen has contributed to several high-impact publications in Nature with significant media attention, including work that was picked up by 278 news outlets and referenced in 24 Wikipedia pages. His research has generated substantial academic interest with numerous citations and Mendeley readers. His work demonstrates strong collaborative networks across international research teams, with co-authorship on major studies involving dozens of researchers from multiple institutions worldwide. This collaborative approach has enabled large-scale genomic studies requiring diverse expertise.

Silja Heilmannمشاهده پروفایل

استادیار

• Bioinformatics
• Cancer Biology
• Stem Cell Biology
• +۵ مورد دیگر

Silja Heilmann is an Assistant Professor at the Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, where she leads research in biomedical data science with a focus on single-cell resolution live imaging data. She is also affiliated with the Morphogenesis and Differentiation Program at the same institution. Her educational background includes a Ph.D. in Biophysics from the Niels Bohr Institute (University of Copenhagen), an M.S. in Biophysics, and a B.Sc. in Physics, all from the University of Copenhagen. Her research career began with theoretical work on microbial ecosystems during her PhD, then evolved through postdoctoral positions at Memorial Sloan Kettering Cancer Center and the Novo Nordisk Foundation Center for Stem Cell Biology. Dr. Heilmann's research focuses on leveraging AI and computer vision for impactful research in Cancer, Stem cell, and Developmental Biology. Her work explores how spherically symmetric stem cell aggregates self-organize into complex structures and how cancer progression disrupts organ structure and symmetry. She addresses these questions through AI-powered image analysis, mathematical/statistical tools, and finite element modeling. Her current projects include detecting and characterizing metastatic cells in breast and colorectal cancer populations, and studying morphogenesis and cell differentiation in the developing pancreas. Her publication record shows a strong trajectory from microbial ecology toward cancer and developmental biology, with increasing focus on pancreatic development and cancer metastasis using advanced image analysis techniques and computational modeling approaches. The Danish Council for Independent Research, Medical Sciences, Postdoc Fellowship (2017-2019) Lundbeck Foundation Postdoc Fellowship (2016-2018) Presidents Innovator award (2015) Cover paper in Cancer Research (2015) James S. MacDonnell Foundation Complex Systems Postdoctoral Fellowship Award (2012-2015) Dr. Heilmann has supervised multiple students across disciplines, including computer science master's and undergraduate students working on pancreatic imaging analysis, and medical students studying melanoma metastasis. Her research has been supported by prestigious fellowships from Danish research foundations and has resulted in publications in high-impact journals with significant citation counts. She actively participates in the Morphogenesis and Differentiation Program at the University of Copenhagen, maintaining strong collaborations between computational and experimental biologists to translate advanced analytical approaches into biomedical discoveries.

Lys Sanz Moretaمشاهده پروفایل

پژوهشگر ارشد

• Molecular Biology
• Machine Learning
• Probabilistic Programming
• +۳ مورد دیگر

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.

Jan Stanstrupمشاهده پروفایل

استادیار

• Metabolomics
• Nutritional Science
• Biomarker Discovery
• +۵ مورد دیگر

Jan Stanstrup is an Assistant Professor at the Department of Nutrition, Exercise and Sports (Nutrition and Health section) within the Faculty of Science at the University of Copenhagen. His research focuses on metabolomics, biomarker discovery, and nutritional science, with expertise in LC-MS and NMR spectroscopy. Dr. Stanstrup's educational background includes a PhD in Human Nutrition from the University of Copenhagen (2010-2013), a Master of Science in Pharmacy (2009), and a Bachelor of Science in Pharmacy (2007), both also from the University of Copenhagen. His master's thesis focused on "Exploring chemodiversity in Dovyalis caffra and alkaloids in Laureliopsis Philippiana." His research interests span metabolomics data analysis, biomarker discovery for nutritional assessment, structure elucidation, and the application of advanced statistical methods to metabolomics data. Dr. Stanstrup has developed tools for metabolomics data annotation in R and has contributed significantly to quality control methods in metabolomics research. His work bridges the gap between analytical chemistry and nutritional science, with a particular focus on how metabolomics can be used to assess dietary intake and nutritional status. Dr. Stanstrup has published extensively in high-impact journals including Nature Protocols, Nature Metabolism, Analytical Chemistry, and Frontiers in Chemistry. His recent work has focused on developing frameworks for metabolomics quality control, statistical analysis of molecular networking data, and applying metabolomics to precision nutrition approaches. He has received significant attention for his work, with multiple publications picked up by news outlets, blogged about, and widely shared on social media platforms like X (formerly Twitter). His research has garnered substantial readership on academic platforms like Mendeley. Dr. Stanstrup collaborates extensively with researchers across various institutions globally, working at the intersection of nutrition, metabolomics, and data science to advance our understanding of how diet affects human health at the molecular level.