جست‌وجوی استاد و پژوهشگر

Manja Gersholm Grønbergمشاهده پروفایل

پژوهشگر

• Statistical Genetics
• Machine Learning
• Biostatistics
• +۵ مورد دیگر

Manja Gersholm Grønberg is a Postdoctoral researcher at the Technical University of Denmark (DTU) in the Department of Applied Mathematics and Computer Science, specifically within the Statistics and Data Analysis group. Her interdisciplinary research bridges statistics, computer science, and biomedical applications, contributing to several UN Sustainable Development Goals related to health and well-being. Her research interests span statistical genetics, machine learning, biostatistics, neuroscience, and data analysis with particular expertise in ordinal data modeling and single-cell RNA sequencing analysis. Dr. Grønberg's work demonstrates strong methodological innovation in crossed random effects models and neighborhood analysis techniques applied to complex biological datasets. Her recent publications (2024-2025) reveal a strong trend toward interdisciplinary applications, with significant contributions in both bioinformatics (scRNAseq data analysis) and neuroscience (brain stimulation techniques for Alzheimer's disease treatment). These works demonstrate her ability to develop sophisticated statistical methods and apply them to cutting-edge biomedical problems. Expertise in Crossed Random Effects modeling Specialization in Imbalanced Data techniques Research in Intranasal Oxytocin applications Advanced Neighborhood Analysis methods Cell Specification computational approaches Dr. Grønberg has served as an internal examiner for Statistical Genetics courses at DTU, demonstrating her expertise and teaching capabilities in this specialized field. Her completed PhD project on 'Computational methods for ordinal mixed models applicable to online user ratings' established her foundation in advanced statistical modeling. Her research team collaborations span multiple disciplines, connecting statistical methodology with biological and medical applications, particularly in the areas of neurodegenerative disease research and single-cell genomics.

Nikolaj Normann Holmمشاهده پروفایل

پژوهشگر ارشد

• Data Science
• Multimorbidity Research
• Chronic Heart Disease
• +۴ مورد دیگر

Nikolaj Normann Holm is a Postdoc researcher at the Department of Applied Mathematics and Computer Science, Statistics and Data Analysis at the Technical University of Denmark (DTU). His research focuses on applying data science techniques to healthcare problems, particularly in the areas of multimorbidity, chronic heart disease, and mortality prediction. His research interests center on developing and applying advanced statistical and machine learning methods to analyze complex healthcare data. Key areas include multimorbidity clustering, disease progression modeling, and mortality prediction using register-based data. His work bridges the gap between data science and medical research, creating tools that help understand complex disease patterns in population-level healthcare data. His recent publications demonstrate a strong focus on using variational autoencoders for disease clustering, analyzing co-occurring diseases in chronic heart conditions, and validating machine learning models for mortality prediction. His research consistently applies rigorous data science methodologies to address pressing healthcare challenges, particularly in the Danish healthcare system context. Dr. Holm actively participates in academic activities including conference presentations, examinations, and teaching. He has organized workshops on chronic disease selection algorithms and served as supervisor for research projects focusing on disease clustering and mortality analysis.

Rune Dodensig Kjærsgaardمشاهده پروفایل

پژوهشگر

• Data Representation
• Machine Learning
• Neural Networks
• +۵ مورد دیگر

Rune Dodensig Kjærsgaard serves as a Consultant in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), with office location at Richard Petersens Plads, Building 324, 2800 Kgs. Lyngby. He completed his PhD at DTU in January 2024 under main supervisor Line Clemmensen, following a research trajectory focused on interdisciplinary machine learning applications. His professional profile integrates computer science with astronomy and maritime engineering, positioning him as an emerging researcher in explainable and domain-specific AI systems. His research program centers on Data Representation and Machine Learning, with specialized expertise in Neural Networks, Anomaly Detection, and Clustering. Key contributions include the TAU framework for telluric correction in astronomical spectroscopy, self-explainable autoencoders for maritime anomaly detection (SEAuAIS), and fair soft clustering algorithms. He addresses critical challenges in making AI systems interpretable while maintaining performance, particularly for observational data with high noise levels in astronomy and maritime contexts. His work consistently bridges theoretical machine learning advancements with practical domain applications. Analysis of his 7 publications (2023-2025) reveals a strong interdisciplinary trajectory: 30% in astronomy applications (e.g., solar spectra analysis), 20% in maritime security, and 50% in core machine learning methodology. Key thematic trends include explainability in deep learning systems, robust anomaly detection for sparse data, and fairness-aware clustering. His recent publications in Ocean Engineering (2025) and Astronomy & Astrophysics (2023) demonstrate successful translation of methods across domains. No scientific awards are documented, but his PhD project 'Extracting Essential Information and Making Inference from Data' (2020-2024) established his research foundation. Current work appears supported through his DTU consultant role and collaborative projects, with evidence of international co-authorship across multiple institutions. As a recent PhD graduate, he does not yet supervise students but maintains active research collaborations. Prospective collaborators should note his focus on practical AI implementations with domain-specific constraints and strong publication momentum in top venues (AAAI, AISTATS).

Jesper Grud Skat Madsenمشاهده پروفایل

دانشیار

• Transcriptional regulation
• Metabolic diseases
• Computational biology
• +۷ مورد دیگر

Jesper Grud Skat Madsen is an Associate Professor at the Department of Biochemistry and Molecular Biology, University of Southern Denmark (SDU), and Principal Investigator of the MadLab research group. His work bridges computational biology with experimental genomics, focusing on transcriptional regulation in tissue plasticity and metabolic diseases. Current affiliation: SDU (Department of Biochemistry and Molecular Biology) Previous affiliation: SDU (Department of Mathematics and Computer Science, 2021-2023) Research centers: ATLAS, Human Gene Regulatory Map (HGRM), ADIPOSIGN

Esmaeil Nadimiمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Science
• +۵ مورد دیگر

Esmaeil Nadimi is a Professor and Head of Unit at The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, where he leads the Applied AI and Data Science research unit. He holds multiple concurrent positions including Visiting Professor at Harvard University, Chair of Data Analytics at SDU eScience Center, and research roles with Innovationsfonden and Swiss National Science Foundation (SNSF) in Medicine & Biology and Health & Well-being domains. His educational background includes a Ph.D. in Electrical and Computer Engineering from Aalborg University (2008) and an M.Sc. in Electrical Engineering (Control Theory) from Sharif University of Technology (2004). Research focuses on foundational AI methodologies including Physics-Infused Learning, Meta & Transfer Learning, with applied research in healthcare (medical imaging, cancer detection, diabetic neuropathy), energy systems, and optical technologies. Core expertise spans machine learning, computer vision, statistical modeling, and medical AI systems development. Publications demonstrate strong emphasis on AI applications in medical diagnostics, particularly capsule endoscopy, cervical/colorectal cancer detection, and diabetic complication screening. Recent work increasingly addresses explainable AI frameworks and practical implementation pathways for clinical adoption. Leadership includes supervision of industrial PhD/postdoc programs and development of academic courses in AI/Data Science. Current grants involve Innovationsfonden and SNSF-funded projects. Leads multiple research teams focused on medical AI systems at SDU Applied AI and Data Science unit.

• Epidemiology
• Nutrition
• Public Health
• +۳ مورد دیگر

Christian Ritz is a Professor at the National Institute of Public Health (University of Southern Denmark). His research spans epidemiology, nutrition, biostatistics, and clinical research, with a focus on malnutrition, community-acquired pneumonia, and statistical modeling. Key collaborations include institutions in Denmark , Uganda , and Burkina Fatos Research areas: Randomized Controlled Trials , Body Composition , Vitamin D , and Latent Transition Analysis Research trends from 374 publications include: Nutritional interventions for stunted children Statistical methods in clinical and public health research Molecular diagnostics for infectious diseases Behavioral modeling in tobacco use prevention

• Environmental Science
• Marine Biology
• Statistics
• +۱ مورد دیگر

Timo Adam is a Guest Researcher at the Department of Mathematical Sciences, University of Copenhagen. His research intersects statistical analysis and environmental science, focusing on ecological monitoring and predictive modeling. Current Affiliation: Department of Mathematical Sciences, University of Copenhagen His recent publications highlight two key areas: (1) climate change effects on aquatic ecosystems using electronic tagging, and (2) methodological advancements in statistical modeling through Markov-switching decision trees. These works reflect expertise in interdisciplinary research connecting computational methods with environmental science. Collaborations span multiple international institutions, with research outputs published in high-impact journals like Canadian Journal of Fisheries and Aquatic Sciences and AStA Advances in Statistical Analysis . His work has garnered attention from 1 news outlet and 44 social media posts.

• Genetic Epidemiology
• Public Health Genomics
• Health Data Science
• +۵ مورد دیگر

Tugce Karaderi is an Associate Professor in the Department of Public Health at the University of Copenhagen, specifically affiliated with the Section for Health Data Science and AI. She maintains an active research profile with significant contributions to genetic epidemiology and translational data science. University of Copenhagen, Faculty of Health and Medical Sciences Department of Public Health, Section for Health Data Science and AI ORCID: 0000-0001-9763-8139 Dr. Karaderi's educational background includes a DPhil in Human Genetics from the University of Oxford (focusing on the genetics of Ankylosing Spondylitis), an MSc in Environmental Health from the Cyprus Institute for the Environment and Public Health in association with Harvard School of Public Health, and a BSc in Biology from the University of Missouri at Kansas City. Her research spans multiple areas of genomic medicine, with particular emphasis on polygenic risk prediction, multi-omics integration for complex diseases, and AI applications in personalized medicine. She has made significant contributions to understanding the genetic architecture of obesity, polycystic ovary syndrome, and thyroid disorders through large-scale international collaborations. Her work often involves multi-ancestry analyses to ensure broader applicability of findings across diverse populations. Analysis of her recent publications reveals a consistent focus on translating genomic discoveries into clinical applications, with particular attention to sex-specific differences in disease presentation and response. Her research methodology integrates statistical genetics with machine learning approaches to extract meaningful insights from complex biological datasets. Dr. Karaderi has contributed to numerous high-impact publications in journals including Nature, Nature Medicine, Nature Communications, and Diabetes, often as part of major international consortia. Her work has been widely disseminated through social media and picked up by multiple news outlets, demonstrating its relevance to both scientific and public audiences. Her research program demonstrates strong collaborative networks across multiple institutions and countries, reflecting the increasingly international nature of genomic research. She appears to be actively involved in both basic science investigations and translational applications of genomic discoveries to improve patient care.

• Quantitative Finance
• Actuarial Science
• Machine Learning
• +۳ مورد دیگر

Yevhen Havrylenko serves as a Postdoctoral Researcher in the Department of Mathematical Sciences at the Faculty of Science, University of Copenhagen, Denmark. His appointment is based at Universitetsparken 5, 2100 København Ø, where he conducts advanced research in quantitative finance and actuarial modeling, with contact details including phone +4535327906 and email yh@math.ku.dk. His research program integrates mathematical finance, actuarial science, and machine learning to solve complex risk management problems. Key focus areas include stochastic volatility modeling for portfolio optimization under value-at-risk constraints, neural network applications for detecting variable interactions in generalized linear models, and game-theoretic approaches to risk-sharing mechanisms in insurance products. This interdisciplinary work bridges theoretical mathematics with practical financial and insurance industry challenges, emphasizing computational rigor and real-world applicability. Analysis of his recent publications reveals a cohesive research trajectory centered on mathematical innovation in risk assessment. His work consistently employs dynamic programming, stochastic processes, and machine learning to address market incompleteness, variable interaction detection, and strategic insurer-reinsurer dynamics. The publications demonstrate strong methodological synergy across finance and insurance domains, with increasing emphasis on computational techniques for complex risk modeling. No scientific awards, student advisement records, or research grants are documented in the available profile. Similarly, no affiliations with specific research laboratories or collaborative teams beyond publication co-authors (Escobar-Anel, Zagst, Heger, Hinken) are specified.

• Computer Science
• Artificial Intelligence
• Data Science
• +۳ مورد دیگر

Ziming Luo serves as an Assistant lecturer in the Department of Computer Science at the University of Copenhagen, located at Universitetsparken 1, 2100 Copenhagen Ø, Denmark. His position falls under the university's faculty structure with direct affiliation to the computer science department. His research spans foundational and applied domains within computing: Computer Science (core theoretical and practical frameworks) Artificial Intelligence (machine learning and cognitive systems) Data Science (statistical modeling and big data analytics) Software Engineering (development methodologies and systems architecture) Algorithms (design and computational complexity) Theoretical Computer Science (formal languages and automata theory) No scientific awards, student advising relationships, or laboratory affiliations are documented in the available institutional records. His professional activities focus on departmental teaching responsibilities within the university's academic framework.

• Exoplanet Research
• Astronomy
• Astrophysics
• +۳ مورد دیگر

Lars A. Buchhave serves as an Affiliate Professor at the Niels Bohr Institute, University of Copenhagen, specializing in Astrophysics and Planetary Science. His research focuses on exoplanet discovery and characterization using data from missions like Kepler and K2, with significant contributions to understanding planetary system architectures and detection methodologies. Dr. Buchhave's research spans exoplanet demographics, spectroscopic analysis, and machine learning applications in astronomy. His work has revealed critical patterns in planetary systems, including the 'peas in a pod' phenomenon where planets in multi-planet systems show similar sizes and regular spacing. He has made substantial contributions to understanding how stellar metallicity influences planet formation across different planetary types. His publication record shows a clear evolution from early exoplanet detection work to sophisticated statistical analyses of planetary systems and innovative computational approaches. Recent work demonstrates increasing integration of machine learning techniques for telluric correction and spectral analysis, reflecting broader trends in astronomical data processing. With 166 research outputs documented, including high-impact papers in the Astronomical Journal and Astronomy & Astrophysics, Dr. Buchhave has established himself as a significant contributor to exoplanet research. His work on the ANDES spectrograph project represents current cutting-edge instrumentation development for future exoplanet characterization. Dr. Buchhave maintains active research collaborations across international teams, as evidenced by his participation in large consortium papers with hundreds of co-authors. His work has received significant attention, with several papers accumulating over 100 citations and substantial media coverage, including features in major news outlets and references in Wikipedia pages.

Christy Anna Hipsleyمشاهده پروفایل

دانشیار

• Evolutionary biology
• Vertebrate paleontology
• Herpetology
• +۷ مورد دیگر

Christy Anna Hipsley is an Associate Professor in the Department of Biology at the University of Copenhagen, Denmark. She holds a joint appointment in the Ecology and Evolution section, with research spanning evolutionary biology, vertebrate paleontology, and herpetology. Her work integrates multiple disciplines through advanced imaging techniques and computational approaches to study morphological evolution across 500 million years of vertebrate history. Her primary research interests include evolutionary biology, vertebrate paleontology, herpetology, phenotypic convergence, and 3D shape analysis. She specializes in using X-ray computed tomography to generate three-dimensional models of fossil and living organisms for evolutionary comparison, often applying geometric morphometrics to diverse groups including primates, canids, marine mammals, marsupials, turtles, and reptiles. Her work specifically examines how Cenozoic climate change has shaped morphological and taxonomic variation, and identifies contexts where biodiversity is generated and destroyed. Dr. Hipsley's recent publications demonstrate a strong interdisciplinary approach, combining traditional morphological studies with computational methods. Her work spans from detailed anatomical studies of reptiles to collaborations with computer scientists on diffusion modeling techniques. This reflects her commitment to integrating bioinformatic, molecular, ecological, paleoclimatic, and morphological approaches in evolutionary research. Selected Awards: Danmarks Frie Forskningsfond (DFF) Project 1 (2,847,513 DKK) Villum Synergy Grant on Stochastic Morphometry (2,140,000 DKK) ARC Discovery Early Career Researcher Award (1,650,697 DKK) Dean's Award for Excellence in Research (Team), University of Melbourne Dr. Hipsley has supervised numerous graduate students across multiple institutions including the University of Copenhagen, Monash University, and the University of Melbourne. She serves as a Senior Editorial Board Member for BMC Ecology & Evolutionary Biology and reviews for numerous prestigious journals including Nature Communications, Science Advances, and Journal of Vertebrate Paleontology. Her international collaborations span Germany, Brazil, Australia, New Zealand, and India, reflecting the global nature of her research program. She is also an active participant in science outreach, having contributed to children's science TV shows, podcasts, and public exhibits.

• Epidemiology
• Respiratory Health
• Allergic Disease
• +۷ مورد دیگر

Dr. Angela Pinot de Moira is a Research Fellow in Epidemiology at Imperial College London's School of Public Health and Faculty of Medicine, and a Guest Researcher at the Department of Public Health, Section of Epidemiology at the University of Copenhagen. She holds an NHLI Fellowship at Imperial and previously held a Lundbeck Foundation fellowship at the University of Copenhagen. With a background in immunology, her research bridges epidemiology, public health, and biomedical sciences through extensive collaboration across European birth cohort studies. Dr. Pinot de Moira's research focuses on understanding how early-life social and environmental conditions impact respiratory health and allergic disease. Her work examines the role of the microbiome and immune response in health outcomes, with particular interest in cross-birth cohort analysis and making data FAIR (findable, accessible, interoperable and reusable). She has developed expertise in federated data analysis through her involvement with DataSHIELD, where she serves on the steering committee and co-leads the Communications and Outreach theme. Her research spans epidemiological methods, environmental health, social determinants of health, and respiratory medicine, with a strong emphasis on collaborative, multi-center studies across European birth cohorts. Her recent publications reveal a strong trend toward large-scale collaborative research using federated analysis methods to examine respiratory health outcomes across multiple birth cohorts. Her work increasingly focuses on social inequalities in health, environmental exposures (particularly green space), and the development of methodologies for secure multi-site data analysis. She has made significant contributions to understanding asthma epidemiology, allergic diseases, and the impacts of early-life exposures on long-term health outcomes across European populations. NHLI Fellowship at Imperial College London Lundbeck Foundation fellowship at University of Copenhagen HDRUK funding as part of the Social and Environmental Determinants of Health Driver Programme Membership on DataSHIELD steering committee Dr. Pinot de Moira has secured significant research funding through fellowships and collaborative grants. She is actively involved in the EU Child Cohort Network and the UNICORN (Unified Cohorts Research Network) project at Imperial College. Her work emphasizes cross-institutional and cross-national collaboration, particularly through federated data analysis approaches that maintain data privacy while enabling large-scale research. She has developed expertise in both traditional epidemiological methods and advanced computational approaches for data analysis across multiple sites. Dr. Pinot de Moira is a key contributor to the DataSHIELD infrastructure, which enables remote and non-disclosive analysis of sensitive research data. She co-leads the DataSHIELD Communications and Outreach theme and has been instrumental in developing and applying federated analysis methods across multiple European birth cohort studies. Her work is central to the EU Child Cohort Network, which has established a FAIR (findable, accessible, interoperable and reusable) network of European and Australian birth cohort data.

• Forest Pathology
• Pathogen Genomics
• Microbiology
• +۶ مورد دیگر

James Doonan serves as Assistant Professor in Forest and Landscape Ecology within the Department of Geosciences and Natural Resource Management at the University of Copenhagen's Faculty of Science. His research focuses on molecular mechanisms underlying tree-pathogen interactions, utilizing advanced genomic and transcriptomic approaches. His primary research interests include forest pathology , pathogen genomics , and tree-microbiome relationships . Current investigations center on Ash Dieback disease in European ash ( Fraxinus excelsior ) caused by Hymenscyphus fraxineus , aiming to identify genomic bases of tolerance. Previous work examined bacterial pathogens in oak trees contributing to Acute Oak Decline, revealing interactions between pathogens and the native insect Agrilus biguttatus . Analysis of his recent publications shows consistent focus on forest disease mechanisms using cutting-edge genomic techniques. His work spans fungal pathogens, bacterial diseases, and nematode interactions, with strong emphasis on European tree species conservation. Collaborative research extends across multiple European institutions, reflecting international engagement in forest pathology. Dr. Doonan's research methodology integrates molecular biology, DNA/RNA sequencing, and computational approaches to address critical challenges in forest health. His work provides foundational knowledge for developing disease-resistant tree varieties and sustainable forest management strategies.

• Animal Welfare
• Veterinary Epidemiology
• Precision Livestock Farming
• +۳ مورد دیگر

Dan Børge Jensen serves as Associate Professor at the University of Copenhagen's Department of Veterinary and Animal Sciences, holding dual appointments in the Production, Nutrition and Health section and Animal Welfare and Disease Control section. His research integrates veterinary science with advanced data analytics to address critical animal welfare challenges across diverse production systems. His primary research interests focus on precision livestock farming methodologies , particularly video-based behavioral monitoring for early detection of welfare issues like piling in hens and tail-biting in pigs. He develops statistical models for predicting health outcomes such as footpad lesions in broilers and post-operative complications in equine dentistry, while also pioneering decision-support systems through the DECIDE project that transform surveillance data into actionable farm management tools. Analysis of his recent publications reveals a strong interdisciplinary trend combining veterinary epidemiology with machine learning and dynamic modeling, spanning poultry, swine, equine, and aquaculture sectors. His work demonstrates consistent application of data-driven approaches to improve animal health monitoring and production efficiency. Dr. Jensen maintains active international collaborations, with research outputs appearing in journals including Smart Agricultural Technology , Frontiers in Marine Science , and Equine Veterinary Journal . His work shows significant practical impact through measurable downloads and academic engagement, though no formal awards or student supervision details are documented in available sources.