جست‌وجوی استاد و پژوهشگر

Jes Frellsenمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Computational Biology
• +۴ مورد دیگر

Jes Frellsen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), affiliated with the Cognitive Systems research group. His office is located at Richard Petersens Plads, Building 321, 2800 Kongens Lyngby. Research Focus: Dr. Frellsen's work spans machine learning theory and applications, with core expertise in: Probabilistic modeling (latent variable models, Gaussian processes) Computer vision (image segmentation, geometry-aware algorithms) Generative AI (energy-based models, neural representations) Scientific applications (protein structure prediction, news recommendation systems) His research emphasizes uncertainty quantification and handling incomplete data. Advising & Projects: Currently supervising 5 PhD students on projects including: Machine learning for single-cell sequencing analysis AI-driven natural product discovery Digital twins for 3D concrete printing Inverse catalyst design via deep generative models Implicit neural representations for incomplete data

Ute Hahnمشاهده پروفایل

دانشیار

• Statistics
• Biostatistics
• Neurodegenerative Diseases
• +۵ مورد دیگر

Ute Hahn is an Associate Professor at the Department of Mathematics, Aarhus University . Her research bridges Statistics, Biostatistics, and Neuroscience , with a focus on neurodegenerative diseases like ALS and advanced microscopy techniques . Research Interests: Statistical modeling in ALS and genetic research Stochastic geometry and spatio-temporal analysis Super-resolution microscopy (PALM, STED) Medical statistics and clinical epidemiology Functional data analysis and methodological innovations Selected Publications highlight her interdisciplinary work, including studies on ALS mouse models , microscopy artifacts , and spatio-temporal patterns in medical and environmental contexts . Collaborative Projects: Ute is actively involved in the MechanoGeometry research initiative, which began in 2022 and continues into 2023.

Trine Aalborgمشاهده پروفایل

پژوهشگر

• Biotechnology
• Genomics
• Bioinformatics
• +۷ مورد دیگر

Trine Aalborg is a Postdoctoral Researcher in the Department of Chemistry and Bioscience at Aalborg University's Faculty of Engineering and Science. Her research focuses on genomics, bioinformatics, and potato crop improvement, with particular expertise in tetraploid genetics and fungal systems. She maintains an active research profile with multiple publications and projects spanning from 2020 to 2025. Dr. Aalborg completed her MSc in Biotechnology with research focused on Introducing Molecular Methods for Genome Engineering in Apiospora Filamentous Fungi (2019-2021) Her primary research interests center on potato genomics and biotechnology, with specific expertise in tetraploid genetics, genomic selection, and CRISPR/Cas9 applications. She investigates secondary metabolites in filamentous fungi, particularly Apiospora, and applies structural bioinformatics to advance crop improvement strategies. Her work contributes to UN Sustainable Development Goals related to food security and sustainable agriculture. Analysis of her recent publications reveals a consistent focus on tetraploid potato genomics, with particular emphasis on how marker type and density affect genomic prediction accuracy and genome-wide association studies. Her research bridges computational genomics with practical agricultural applications, developing tools and resources for potato breeders. Dr. Aalborg has received notable recognition including: AAU Biotech Scholarship 2020-2021 (70,000 DKK) Karen Nordjysk Naturtalent award in Chemistry (1st place, 2,000 DKK) She serves as a Co-Investigator on multiple significant research projects including: Sekventering og vedligeholdelse af MASPot kartoffelpopulationen (2023-2025) RESPOT: Development of disease resistant potatoes (2020-2024) These projects focus on potato genetic resources, disease resistance breeding, and genomic prediction methodologies. Dr. Aalborg collaborates extensively with researchers across Aalborg University, particularly with Nielsen K.L., Sverrisdóttir E., and Sørensen T., forming part of a robust research team focused on potato genomics and improvement. Her work connects fundamental genomic research with practical agricultural applications through the MASPot population and related resources.

• Biophysics
• Materials Science
• Computational Biology
• +۳ مورد دیگر

Martin Cramer Pedersen is an Associate Professor at the Niels Bohr Institute, University of Copenhagen, affiliated with the Biocomplexity department and the Solid State Physics group. His research spans interdisciplinary topics at the intersection of biophysics, materials science, and computational biology. Specializes in structural characterization using small-angle scattering (SAXS/SANS) Develops computational methods for analyzing complex biological and soft matter systems Investigates active matter dynamics, membrane-bound protein interactions, and colloidal self-assembly Recent publications highlight his work on: Hyperbolic order in curved materials α-synuclein aggregation mechanisms Active particle-induced porous gel structures Advanced scattering data analysis tutorials Casein micelle structural heterogeneity Nematic order collapse on frictional substrates His collaborative research involves institutions across Europe and Australia, with applications in biophysics, nanotechnology, and soft matter physics.

Duccio Mediniمشاهده پروفایل

پژوهشگر ارشد

• Bioinformatics
• Biostatistics
• Vaccine Research and Development
• +۳ مورد دیگر

Duccio Medini is a Research Fellow at Novartis Vaccines and Diagnostics , specializing in integrating quantitative methodologies with genomics, preclinical, and clinical data to address global health challenges. His career spans over a decade in pharmaceutical R&D, with expertise in vaccine development, team leadership, and cross-functional international project management. Core Competences: Bioinformatics, Biostatistics, Vaccine R&D, Population Genomics Research Interests His work focuses on meningococcal vaccine development, immunogenicity prediction, and public health impact assessment. He has pioneered synthetic control methods for evaluating vaccine efficacy and advanced dynamic modeling techniques for field effectiveness estimation. Publication Trends Over 12 years, his research evolved from molecular antigen characterization (2010) to population-level vaccine impact analysis (2016-2021), emphasizing data-driven approaches in vaccine epidemiology and translational research. Leadership Roles He has built high-performing teams of quantitative scientists and statisticians while contributing to regulatory approval pathways for major vaccine programs through rigorous statistical and computational analysis.

• Machine Learning
• Computational Biology
• Biophysics
• +۲ مورد دیگر

Jacob Kæstel-Hansen is a Postdoc at the Department of Computer Science , University of Copenhagen , affiliated with the Machine Learning section under the Faculty of Science. His work bridges machine learning with computational biology, focusing on single-particle tracking and biological data analysis. Research Interests : Machine learning applications in biophysics Single-particle tracking and diffusion analysis Protein aggregation and intracellular dynamics Super-resolution microscopy Automated correlation between biological motion and function Publications : His recent contributions (2024-2025) include advanced machine learning techniques for single-particle dynamics, insulin analog trafficking, and defect-engineered nanocarriers for drug delivery. He collaborates extensively with researchers in biophysics and computational biology. Collaborations : Jacob works with institutions like the SCIENCE AI Centre and researchers in biophysics, chemistry, and computational biology, focusing on sustainability and medical data applications.

Sebastian Weichwaldمشاهده پروفایل

دانشیار

• Causal Modelling
• Machine Learning
• Structural Equation Models
• +۳ مورد دیگر

Sebastian Weichwald is an Associate Professor at the Department of Mathematical Sciences , University of Copenhagen. He leads the Copenhagen Causality Lab (CoCaLa) and co-leads the Causality and Explainability (CX) collaboratory at the Pioneer Centre for AI. His academic journey includes a PhD at ETH Zurich (2019) and postdoctoral work at the University of Copenhagen. Research Interests: Causal Modelling, Causal Discovery, Structural Equation Models, Time Series Analysis, Neuroimaging, Biomedical Data Analysis. Awards: Best student paper at CIP 2014, Winner of Causality 4 Climate NeurIPS 2019 competition. Collaborations: Active in interdisciplinary projects with applications in cardiology, neuroscience, and biomedical imaging. Software Tools: Developed open-source libraries including CausalDisco , coroICA , tidybench , and Pymanopt for causal discovery, signal processing, and manifold optimization.

• Population Genetics
• Ancient DNA Analysis
• Evolutionary Biology
• +۳ مورد دیگر

Alba Refoyo Martínez is a Postdoctoral Researcher at the Globe Institute, University of Copenhagen, holding positions in both the Section for Geogenetics and the Section for Molecular Ecology and Evolution. She also serves as an Academic employee in the Department of Public Health's Section for Health Data Science and AI. Her research spans population genetics, ancient DNA analysis, and evolutionary biology, with significant contributions to understanding human evolutionary history and genetic disease risk. Her research interests focus on population genomics , ancient DNA analysis , and evolutionary adaptation . Dr. Martínez has made significant contributions to understanding population turnovers in Neolithic Denmark, the genetic origins of multiple sclerosis risk, and methodological improvements in ancestry estimation. Her work combines computational approaches with ancient DNA analysis to reconstruct human evolutionary history across Eurasia. Her publication record demonstrates substantial impact, with multiple high-profile papers in Nature in 2024 examining ancient Eurasian populations and genetic disease risk. Her research has received widespread attention across academic and public spheres. Dr. Martínez has been involved in extensive collaborative research projects, working with large international teams across numerous institutions. Her work has been featured in hundreds of news outlets and social media platforms, indicating significant scientific and public impact. She maintains active research in both the Globe Institute and the Department of Public Health, bridging evolutionary genetics with modern health data science approaches. Her dual appointment reflects the interdisciplinary nature of her research, connecting ancient population history with contemporary health implications.

David Alejandro Duchene Garzonمشاهده پروفایل

دانشیار

• Molecular Evolution
• Phylogenetics
• Phylodynamics
• +۵ مورد دیگر

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.

• Statistical Genetics
• Population Genetics
• Bioinformatics
• +۱ مورد دیگر

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.

• Actuarial mathematics
• Financial mathematics
• Bioinformatics
• +۹ مورد دیگر

Jing Qin is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where she contributes to research and teaching in mathematical statistics and applied probability. She is also affiliated with the SDU Climate Cluster, reflecting her interdisciplinary engagement in climate and health modeling. Research Interests: Her work spans actuarial mathematics, financial mathematics, bioinformatics, and mathematical modeling. She specializes in extreme value theory, conditional risk measures, tail dependence, and statistical methods for RNA folding. Her research integrates rigorous theoretical development with practical applications in insurance, finance, and biomedical data analysis. The recent publications indicate a strong trend in developing robust, nonparametric estimators for conditional extremes, particularly in bivariate and covariate-adjusted settings. Her work often involves asymptotic analysis and simulation studies, with applications in reinsurance, epidemiology, and genomics. Collaborative research with Y. Goegebeur and A. Guillou is prominent. Scientific Awards: No specific awards listed in the provided text. Advising and Grants: Jing Qin supervises BA, MA, and PhD students in mathematical and statistical research. She is actively involved in externally funded projects, including as Principal Investigator (PI) on a research project analyzing the impact of extreme weather on emergency hospital demand. She has participated in projects funded by the Novo Nordisk Foundation, Carlsberg Foundation, and the Danish Research Council, covering areas from intellectual disability genetics to RNA bioinformatics. Labs and Teams: She is part of collaborative networks in statistical extremes and bioinformatics, and contributes to the SDU Climate Cluster, suggesting active participation in interdisciplinary research teams focused on climate, health, and data science.

• Computational Biology
• RNA Biology
• Population Genetics
• +۴ مورد دیگر

Rasmus Heller is an Associate Professor at the Department of Biology , University of Copenhagen , affiliated with the Computational and RNA Biology research group. His work integrates genomics , evolutionary biology , and computational methods to study population dynamics and genetic adaptation. Research Interests: Heller focuses on population genetics , conservation genomics , and bioinformatics , with particular interest in species boundaries , admixture events , and molecular clock modeling . His studies span both wildlife (e.g., giraffes, wildebeests) and domesticated species (e.g., cattle). Article Trends: Recent publications analyze genomic patterns across diverse species, including antelopes , musk deer , and endangered saola . Common themes include evolutionary adaptation , human impact on biodiversity , and genetic integrity under environmental pressures . Contact: Email: rheller@bio.ku.dk

Robin Anderssonمشاهده پروفایل

دانشیار

• Transcriptional regulation
• Enhancers
• Promoters
• +۴ مورد دیگر

Robin Andersson is an Associate Professor at the Department of Biology, University of Copenhagen , leading the Andersson Lab focused on modeling gene regulation to understand how enhancer and promoter dysregulation contribute to disease risk. His interdisciplinary research combines machine learning , statistical learning , genetics , and molecular biology to determine which noncoding sequences act as enhancers, predict regulatory activity from DNA sequences, and characterize mechanisms controlling gene expression variation. His research has produced significant insights into enhancer-promoter interactions , epigenetic inheritance , and transcriptional robustness . He has developed computational tools like ChromTransfer and hyperTRIBER for analyzing chromatin accessibility and RNA editing . His work bridges genomic mechanisms of disease with practical applications in noncoding variant interpretation and therapeutic target identification . Robin Andersson has received prestigious awards including the ERC Horizon 2020 Starting Grant , Sapere Aude Research Leader award , and Hallas-Møller Ascending Investigator award . He has supervised 17 MSc and 5 PhD students while contributing to academic service as PhD coordinator and strategic research board member. The lab maintains affiliations with the Novo Nordisk Foundation Center for Genomic Mechanisms of Disease and FANTOM consortium , with recent grants focusing on single-cell regulatory mapping and disease variant interpretation .

• Chemometrics
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Rasmus Bro is a Professor at the University of Copenhagen , Faculty of Sciences , and leads the Design and Consumer Behavior section within the Department of Food Science . He is a world-renowned expert in chemometrics and machine learning applied to analytical chemistry and food supply chain optimization . Research Focus : Chemometrics, Process Analytical Technology (PAT), multi-way analysis, fluorescence spectroscopy, and data-driven modeling of biological systems Teaching : Director of the International MSc course Advanced Chemometrics and International PhD School of Chemometrics (12 ECTS annually) Leadership : Founder and leader of the ODIN research consortium (2001–), connecting academia and industry (Arla, Dupont, Chr. Hansen) His work bridges machine learning with analytical chemistry to enhance food quality and safety. He has developed open-source MATLAB toolboxes for multi-way analysis and maintains key web resources like ChemoBro and Chemometrics World . Scientific Awards : 10th Herman Wold Gold Medal (2011) Nils Foss Excellence Prize (2016) IEEE Best Paper Award (2001) Elsevier Chemometrics Award (2000) PhD Cum Laude (University of Amsterdam, 1998) Beyond academia, Bro co-founded MedicoMetrics Aps and serves as a consultant to firms like Foss Analytical and Eigenvector Research . His 2003 US Patent on biological sample classification remains widely cited.

• 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.