جستجوی استاد راهنمای دانشگاهی

Afsaneh M. Nejadمشاهده پروفایل

استادیار

• Data Science
• Bioinformatics
• Biostatistics
• +۱۲ مورد دیگر

Afsaneh M. Nejad is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, where she conducts research at the intersection of data science, bioinformatics, and biomedical research. Her work leverages large-scale omics data and twin studies to investigate aging, hypertension, cancer, and cardiovascular disease. Assistant Professor, Department of Mathematics and Computer Science Specialization: Data Science, Bioinformatics, Biostatistics Institution: University of Southern Denmark (SDU) Her research focuses on epigenomics, transcriptomics, and proteomics, particularly in longitudinal twin cohorts. She applies advanced statistical and machine learning techniques to identify biomarkers of disease and aging. Key areas include DNA methylation, gene expression, and multi-omics integration in conditions such as hypertension, Alzheimer’s, diabetes, and myocardial infarction. The recent publications highlight a strong trend in using twin-based designs to control for genetic background while studying environmental and lifestyle impacts on health. Her work spans clinical applications, predictive modeling, and mixed-methods public health research, particularly in sports medicine and chronic disease. She serves as a peer reviewer for journals including BMC Medicine , Clinical Epigenetics , Human Genomics , and Scientific Reports . Her teaching includes courses in bioinformatics and biostatistics, indicating active mentorship and curriculum development. DM847: Introduction to Bioinformatics Applied Biostatistics Evidence-based Drug Use and Biostatistics She has contributed to significant collaborative studies such as the DREAM Trial and has presented at conferences on topics like cancer and frailty in aging. Her methodological expertise in longitudinal data analysis, omics integration, and machine learning positions her at the forefront of computational biomedicine.

Anders Nielsenمشاهده پروفایل

استاد

• Biostatistics
• Stock Assessment
• Fish Population Dynamics
• +۵ مورد دیگر

Anders Nielsen is a Professor at the National Institute of Aquatic Resources (DTU Aqua), Technical University of Denmark, working within the Section for Marine Living Resources. His research focuses on the intersection of biostatistics, fisheries science, and marine ecology, with particular emphasis on developing statistically sound approaches for fish stock assessment and management. His research interests span biostatistics with a focus on modeling dynamical systems and state-space models, fish stock assessment methodologies, fish movement patterns, and statistical software development. Nielsen specializes in improving stock assessment models through better statistical frameworks, optimized sampling designs, and incorporation of spatial structure. His work on automatic differentiation in statistical tools has contributed significantly to computational approaches in fisheries science. Analysis of Nielsen's recent publications reveals strong trends in environmental DNA (eDNA) applications for fisheries monitoring, advanced statistical modeling for stock assessments, and integration of climate variables into fish population dynamics. His work bridges theoretical statistical development with practical fisheries management applications, particularly in North Sea and Baltic Sea ecosystems. The recurring themes include uncertainty quantification, retrospective analysis of assessment models, and development of robust frameworks for sustainable fisheries management. Nielsen actively participates in numerous research projects including MAKE-IT (sustainable mackerel management), FISHMAP (fish distribution and fisheries management), REVIVAL (herring stock assessment), CanMac (genetic approaches to mackerel assessment), and LenSAM (length-based assessment models). He serves as project coordinator for CanMac and LenSAM, demonstrating leadership in developing innovative assessment methodologies. His collaborative network spans multiple institutions and countries, reflecting the international nature of fisheries science. As a member of the International Council for the Exploration of the Sea (ICES), Nielsen contributes to international fisheries science and advice. His work directly supports UN Sustainable Development Goals related to sustainable marine resource management. While specific details about laboratory facilities aren't provided in the available information, his research clearly involves advanced computational modeling and analysis of fisheries data, likely utilizing high-performance computing resources at DTU.

Oscar Alberto Rojas Castilloمشاهده پروفایل

پژوهشگر

• Biodiversity
• Land-use change
• Ecology
• +۵ مورد دیگر

Oscar Alberto Rojas Castillo is a Ph.D. Candidate and Guest Researcher in the Department of Biology at the University of Copenhagen's Faculty of Science, specializing in Freshwater Biology. With extensive international research experience across six countries, he brings a global perspective to his work on biodiversity, land-use change, and freshwater ecosystems. His educational background includes an MSc in Applied Ecology from the International Master in Applied Ecology program (University of Poitiers, University of Kiel, and University of Coimbra) and a Bachelor's in Biology focusing on the impact of oil palm plantations and land-use change on stream water quality. His academic journey reflects a consistent dedication to understanding human impacts on aquatic ecosystems. Rojas Castillo's research expertise centers on freshwater ecology, conservation biology, and land-use change impacts. His work investigates how agricultural expansion, particularly oil palm cultivation, affects freshwater biodiversity and ecosystem functioning, with special attention to the protective role of riparian buffers. He excels in statistical programming and data analysis using R, and has developed sophisticated methodologies for assessing biodiversity patterns in tropical streams. His scientific achievements include a meta-analysis in Conservation Biology that has significantly advanced understanding of riparian buffer effectiveness, along with multiple high-impact publications examining microbial communities, leaf-litter breakdown, and long-term ecological changes in diverse freshwater systems. His research has been recognized with the TALENT Doctoral Fellowship Scholarship, supporting his doctoral work on oil palm expansion impacts. As an active collaborator, Rojas Castillo has worked with researchers across multiple continents, contributing to a growing body of knowledge about sustainable management of freshwater resources in agricultural landscapes. His work bridges theoretical ecology with practical conservation applications, offering evidence-based solutions for mitigating biodiversity loss in tropical regions.

Erin Evelyn Gabrielمشاهده پروفایل

استاد

• Causal Inference
• Biostatistical Methods Development
• Randomized Trials
• +۷ مورد دیگر

Erin Evelyn Gabriel is a Professor in the Section of Biostatistics within the Department of Public Health at the University of Copenhagen's Faculty of Health and Medical Sciences. She holds a PhD in Biostatistics from the University of Washington awarded on August 10, 2012. She teaches Introductory Statistics and Data Analysis at both Masters and Doctoral levels. Her research focuses on biostatistical methods development with applications to infectious diseases, vaccination, cancer, and aging. Key research areas include: Causal inference methodologies Surrogate evaluation, particularly in the presence of interference Nonparametric causal bounds Designs and estimation methods for emulated and randomized clinical trials Evaluation of prediction-based decision rules Her publication record shows a strong trajectory in methodological biostatistics, with recent work (2022-2025) focusing on causal bounds, mediation analysis, age-period-cohort models, instrumental variables, and biases in randomized controlled trials. Her work appears in top-tier journals including the Journal of the American Statistical Association, Biostatistics, and Epidemiology. Dr. Gabriel has established collaborations across multiple institutions, as evidenced by her co-authorship patterns with researchers including Sachs, Sjölander, Follmann, and Ocampo. Her research impact is indicated by citations and downloads of her work, with some publications gaining attention on social media platforms and news outlets.

Sophie Merrild Juulمشاهده پروفایل

استادیار

• Clinical Research Methodology
• Evidence-based Medicine
• Randomized Clinical Trials
• +۷ مورد دیگر

Sophie Merrild Juul is a Tenure Track Assistant Professor in the Department of Psychology at the University of Copenhagen's Faculty of Social Sciences. Her research focuses on clinical research methodology within adult psychiatry and clinical psychology, with particular expertise in randomized clinical trials, systematic reviews, and meta-analyses. Education: PhD fellow, University of Copenhagen (2018-present) M.Sc. in Psychology, University of Copenhagen (2011-2016) Dr. Juul's research interests center on evidence-based medicine and clinical research methodology, with specific focus on psychotherapy research, particularly mentalization-based therapy (MBT) for personality disorders. She investigates methodological approaches to improve psychiatric research, including outcome measurement and trial design. Her work bridges clinical practice with rigorous scientific methodology to advance evidence-based approaches in mental healthcare. Her recent publications demonstrate a strong focus on systematic reviews and meta-analyses across various psychiatric conditions including personality disorders, PTSD, depression, and ADHD. She is particularly interested in methodological issues related to psychiatric research, advocating for more appropriate outcome measures that align with those used in other medical specialties. Research Grants: Research grant from Trygfonden (2018, 2.585.799 DKK) Research grant from the Research Fund, Capital Region of Denmark (2017, 1.596.000 DKK) Dr. Juul collaborates extensively with researchers across multiple institutions on systematic reviews and clinical guidelines. Her current projects include developing transdiagnostic core outcome sets for mental health disorders and investigating novel treatment approaches for complex psychiatric presentations.

Søren Wengel Mogensenمشاهده پروفایل

دانشیار

• Causal Inference
• Stochastic Processes
• Survival Analysis
• +۳ مورد دیگر

Søren Wengel Mogensen is an Associate Professor at the Department of Finance, Copenhagen Business School, Denmark. His research focuses on developing advanced statistical and machine learning methodologies for complex systems analysis. Research Interests: Dr. Mogensen's work spans causal inference, stochastic processes, survival analysis, and time-series modeling. Key themes include: Causal discovery algorithms for industrial and biological systems Graphical representations of dependencies in high-dimensional data Time-varying mediation in survival contexts Bayesian networks for cascade modeling Publication Trends: His recent articles (2021-2025) demonstrate a strong emphasis on theoretical-statistical innovation with applications in healthcare, industrial monitoring, and computational finance. Dominant methodologies include kernel-based independence tests, continuous-time Bayesian networks, and constrained stochastic process modeling.

Ralf Wilkeمشاهده پروفایل

استاد

• Duration Analysis
• Competing Risks Models
• Econometrics
• +۵ مورد دیگر

Ralf Wilke serves as a Professor in the Department of Economics at Copenhagen Business School's School of Economics and Management. His academic profile demonstrates extensive expertise in econometric methods applied to duration analysis and labor market dynamics. Wilke's research focuses on advanced statistical techniques for analyzing time-to-event data, with particular emphasis on: Competing risks models under various identification frameworks Copula-based approaches for dependent censoring and spell durations Quantile regression applications in unemployment and maternity leave studies Spatial diffusion patterns of historical technologies Addressing omitted variable bias in regression analysis His methodological innovations bridge theoretical econometrics with empirical labor economics applications. Analysis of his recent publications reveals consistent contributions to duration modeling literature, particularly in developing robust frameworks for handling dependent censoring mechanisms and competing risks structures. His work demonstrates increasing integration of copula methods with traditional survival analysis techniques. Wilke actively contributes to economic history through spatial analysis of 19th-century technological diffusion patterns, showcasing interdisciplinary methodological transfer.

• Biostatistics
• Medical Statistics
• Clinical Trials
• +۷ مورد دیگر

Birgit Debrabant is an Associate Professor at the Department of Mathematics and Computer Science, University of Southern Denmark, with additional roles in Data Science and the STEM Center for Educational Research – FNUG. Her interdisciplinary work bridges statistical methodology with applications in neuroscience, clinical medicine, and public health. Department: Department of Mathematics and Computer Science Research Centers: STEM Center for Educational Research – FNUG Professional Affiliation: Active in Danish Society for Theoretical Statistics Her research focuses on biostatistics in clinical contexts, particularly neurosurgical outcomes, chronic subdural hematoma, intraventricular hemorrhage, and public health interventions. She employs advanced statistical methods including randomized trials, observational studies, meta-analyses, and genetic association studies. Her work often involves large-scale registry data and methodological innovation in handling complex health data. The 15 most recent publications reveal a strong trend in medical statistics, especially in neurosurgery and public health. Topics include optimal drainage times, antithrombotic therapy, BMI policy impacts, and statistical methods for DNA methylation and hidden population estimation. These reflect a cohesive research program applying rigorous statistical science to pressing clinical and public health questions. She has no listed scientific awards in the provided text. Birgit Debrabant actively contributes to teaching and curriculum development, particularly in biostatistics and applied statistics for health sciences. She has led or co-taught courses such as 'Advanced Biostatistical Methods in Health Sciences' and 'Theory of Science and Statistics'. While no formal student advising or grant information is mentioned, her involvement in multi-center trials and educational initiatives suggests collaborative leadership. She participates in national and international conferences, including DAGStat and the International Workshop on Applied Probability. She is involved in collaborative research networks, particularly in theoretical statistics and health data science, and contributes to professional events as both organizer and presenter.

Robin Christensenمشاهده پروفایل

استاد

• Biostatistics
• Clinical Epidemiology
• Rheumatology
• +۵ مورد دیگر

Robin Christensen is a Professor of Biostatistics and Clinical Epidemiology at the Clinical Institute of University of Southern Denmark, with dual appointments at KI (likely Karolinska Institutet) and OUH (Odense University Hospital), where he leads research in the Research Unit for Rheumatology in Odense. His research interests span multiple areas within rheumatology and evidence-based medicine, with particular focus on: Biostatistics and clinical epidemiology methodology Rheumatoid arthritis and inflammatory arthritis Knee osteoarthritis pathophysiology and treatment Systematic reviews and meta-analyses Randomized controlled trial design and interpretation Placebo effects in clinical research Professor Christensen's recent publication portfolio demonstrates his leadership in evidence synthesis, particularly in rheumatology domains. His work includes methodological advancements in systematic reviews, network meta-analyses, and consensus methods like the OMERACT delphi surveys. His research increasingly focuses on practical clinical questions regarding osteoarthritis management, weight interventions, and crystal arthropathies. His scientific contributions include: Extensive work on knee osteoarthritis interventions Development of core domain sets for rheumatic conditions Methodological research on evidence synthesis Studies examining patient selection and generalizability in clinical trials Professor Christensen is an active educator, having conducted numerous courses on evidence synthesis, systematic reviews, and research methodology. His teaching portfolio includes specialized courses on GRADE methodology, risk of bias assessment, and handling controversial topics in evidence-based medicine. He has supervised multiple PhD students as indicated by his academic record.

• Biostatistics
• Survival Analysis
• Longitudinal Data Analysis
• +۴ مورد دیگر

Anders Munch serves as an Assistant Professor in the Department of Public Health, Section of Biostatistics at the University of Copenhagen. His contact information includes phone number +4535320805 and email a.munch@sund.ku.dk, with his office located at Øster Farimagsgade 5 opg. B, 1353 København K. He maintains an active research profile with an ORCID identifier (0000-0003-4625-1465) and a dedicated website at https://biostat.ku.dk/. Dr. Munch's research focuses on developing statistically valid inference methods for data-adaptive estimation in longitudinal survival data settings. His primary methodological interests include targeted learning for survival data, cross-validation with censored data, and convergence rates for empirical risk minimizers of censored data loss functions. He also engages in applied research projects utilizing genetics data and large registry datasets, particularly Danish national health registries. His publication record demonstrates a strong focus on biostatistical methodology applied to significant health questions. His recent work spans illness-death models, prenatal alcohol exposure effects, cardiovascular outcomes in diabetes, and craniofacial abnormalities. The research consistently leverages large registry datasets and develops advanced statistical techniques for analyzing complex longitudinal health data. Dr. Munch collaborates extensively with researchers across multiple institutions, as evidenced by his co-authorship on studies published in high-impact journals including The Lancet Public Health, European Heart Journal, and Scandinavian Journal of Statistics. His work shows particular strength in methodological development for survival analysis with applications to public health questions.

• Hematology
• Oncology
• Epidemiology
• +۵ مورد دیگر

Morten Rytter Tulstrup serves as a Lecturer in the Department of Public Health within the Faculty of Health and Medical Sciences at the University of Copenhagen. His academic affiliation centers on the Section of Biostatistics, where he conducts research at the intersection of hematology, epidemiology, and pharmacovigilance. His institutional address is Øster Farimagsgade 5 opg. B, 1353 København K. His research spans hematological malignancies, clonal hematopoiesis, and drug safety assessment. Key focus areas include: Mendelian randomization studies for causal inference in testosterone therapy DNA methylation alterations in myelodysplastic syndromes Epidemiological patterns of anabolic steroid complications Clonal evolution in multiple myeloma and acute leukemias Pharmacovigilance methodologies for adverse drug reactions His work integrates genomic, epigenetic, and population-level data to address clinical questions in hemato-oncology. Analysis of his 16 recent publications (2023-2025) reveals consistent focus on hematological disorders with emerging emphasis on Mendelian randomization techniques. His collaborative network spans Danish hospitals and international consortia, particularly in studies involving large population cohorts and genomic databases. Key publication venues include Leukemia , Haematologica , and specialized pharmacology journals. No scientific awards are currently documented in available sources. His research impact is evidenced by social media dissemination across X (formerly Twitter), Bluesky, and Mendeley readership, with several studies picked up by news outlets. Tulstrup participates in multi-institutional collaborations, notably with Rigshospitalet and Copenhagen University Hospital departments. His current work involves Danish population-based studies on clonal hematopoiesis (e.g., COVID-19 hospitalization research) and testosterone-related adverse events. He appears integrated within the university's hematology research ecosystem but no dedicated lab leadership is indicated.

• Public Health
• Social Medicine
• Multimorbidity
• +۳ مورد دیگر

Nina Katrine Kamstrup-Larsen serves as a Guest Researcher in the Section of Social Medicine, Department of Public Health, Faculty of Health and Medical Sciences at the University of Copenhagen. Her work integrates public health research with practical healthcare delivery across primary, municipal, and hospital sectors. Her research focuses on: Multimorbidity epidemiology and severity metrics Patient-centered care model development for complex cases Cross-sectoral shared-care systems for elderly populations Chronic disease management optimization Quality-of-life interventions in multimorbid patients Analysis of her 2022-2024 publications reveals consistent emphasis on real-world implementation of care models, particularly through nationwide register studies, systematic reviews, and pilot trials. Key trends include methodological innovation in multimorbidity measurement, feasibility testing of integrated care pathways, and addressing fragmentation in healthcare systems serving aging populations with multiple chronic conditions.

• Epidemiology
• Data Analysis
• Disease Modelling
• +۳ مورد دیگر

Maj Beldring Henningsen is a Researcher and PhD fellow at the Department of Veterinary and Animal Sciences, University of Copenhagen. She holds an M.Eng in Bioinformatics and Systems Biology and a B.Eng in Biomedical Engineering. Her work focuses on veterinary epidemiology and registry data analysis, with affiliations in both Animal Welfare and Disease Control and Veterinary Clinical Microbiology research groups. Her research applies quantitative methods including epidemiology, statistical modelling, and data science to improve dairy cattle health. Primary interests include: Antibiotic usage patterns and stewardship in livestock Somatic cell count analysis for early mastitis detection Registry-based disease surveillance systems Biostatistical methods for veterinary applications Publications demonstrate consistent focus on dairy cattle health optimization through national registry analysis. Recent works cluster around: (1) antimicrobial usage reporting validation, (2) somatic cell pattern recognition for infection detection, and (3) evidence-based dry-off treatment protocols. This corpus reflects expertise in transforming agricultural datasets into preventive health strategies. Awards and recognition: 1st prize for most innovative Datathon project (2021) for developing data-driven veterinary solutions No information is available regarding student advising, research grants, or laboratory leadership roles at this time.

Samir Bhattمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Public Health
• +۵ مورد دیگر

Samir Bhatt is a Professor of Machine Learning and Public Health at the University of Copenhagen's Faculty of Health and Medical Sciences, Department of Public Health, Section for Health Data Science and AI. He also holds a position as Professor of Statistics and Public Health at Imperial College London since 2016. His work focuses on developing mathematical, statistical, and computer science tools to address critical questions in human health. His educational background includes a DPhil in Statistical Genetics from the University of Oxford (2010), an MPhil in Computational Biology from the University of Cambridge (2006), and a BEng in Chemical and Bioprocess Engineering from the University of Bath (2005). Professor Bhatt's research spans the intersection of statistics, machine learning, and public health with particular emphasis on infectious disease modeling. His primary research areas include Bayesian inference, genomic epidemiology, and kernel methods applied to health data. His work bridges theoretical statistical approaches with practical public health applications, particularly in disease surveillance and outbreak response. The integration of AI with traditional epidemiological methods represents a key innovation in his research program. Analysis of his recent publications reveals a strong focus on applying advanced computational methods to pressing public health challenges. His work demonstrates consistent innovation in developing AI-driven approaches for infectious disease modeling, genomic surveillance, and survival analysis. Notable themes include the application of graph neural networks to epidemiological data, development of interpretable AI tools for public health decision-making, and sophisticated modeling of disease transmission dynamics across multiple pathogens including malaria, cholera, and respiratory viruses. Professor Bhatt has published extensively with over 107 research outputs to date. His work has received significant attention, with multiple publications covered by news outlets, referenced on social media platforms, and read by researchers on academic platforms like Mendeley. His research on AI for infectious disease modeling published in Nature demonstrates the high impact of his work in both academic and policy spheres. His research group at the University of Copenhagen appears to focus on developing computational tools for health data science, with particular emphasis on creating analytical frameworks that can be rapidly deployed during disease outbreaks. The development of GRAPEVNE (Graphical Analytical Pipeline Development Environment for Infectious Diseases) represents one such effort to create accessible tools for public health practitioners.

• Biostatistics
• Statistical Modeling
• Childhood Cancer Research
• +۵ مورد دیگر

Kathrine Grell serves as an External Lecturer in the Department of Public Health, Section of Biostatistics at the University of Copenhagen's Faculty of Health and Medical Sciences. She chairs the Statistical Modelling work package within the National Research Center for Childhood Cancer CONTROL (Childhood Oncology Network Targeting Research, Organization & Life expectancy). PhD in Biostatistics, University of Copenhagen (2014) Master of Science in Statistics, University of Copenhagen (2009) Her research focuses on statistical modeling of mechanisms (joint longitudinal/time-to-event models) and dose adjustments (dynamic treatment strategies) in the maintenance therapy phase of acute lymphoblastic leukemia (ALL). Her work spans biostatistical methodology development and application to pediatric oncology, with particular emphasis on childhood cancer treatment optimization. She maintains an active research profile with publications spanning public health epidemiology, cancer risk assessment, and statistical methodology for clinical trials. Dr. Grell's research output demonstrates a consistent focus on applying advanced statistical methods to address critical questions in pediatric oncology and public health. Her recent work includes studies on healthy lifestyle impacts on life expectancy, cancer risk associated with diabetes medications, and biomarker development for childhood leukemia treatment complications. As an active researcher, she collaborates extensively across institutions and countries, contributing to the advancement of statistical methodology in medical research. Chair of Statistical Modelling work package, National Research Center for Childhood Cancer CONTROL Post.doc at Children Oncology Laboratory, Rigshospitalet and Section of Biostatistics, University of Copenhagen