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Nicklas WergeView profile

Researcher

• Machine Learning
• Online Algorithms
• Optimization Algorithms
• +10 more

Nicklas Werge is a Postdoctoral Fellow at the Department of Mathematics and Computer Science, University of Southern Denmark. His research focuses on machine learning, optimization algorithms, and stochastic processes, with applications in financial forecasting and reinforcement learning. Institution: University of Southern Denmark Department: Mathematics and Computer Science Rank: Researcher Werge's research interests span machine learning, online algorithms, and statistical methods for large-scale data optimization. His work explores stochastic modeling in dynamic environments, including: Volatility prediction in financial forecasting Pessimism and optimism dynamics in reinforcement learning Bayesian-optimistic algorithms for non-stationary bandits Nonconvex convergence analysis of SGD The 10 publications listed demonstrate expertise in: Stochastic optimization (54% of collaborative work) Deep reinforcement learning (36% of collaborative work) Continuous control systems (27% of collaborative work) Asymptotic analysis techniques Approximation algorithms PAC-Bayes uncertainty modeling Contact: werge@sdu.dk | ORCID

• Sexology
• Gender Studies
• Behavioral Sciences
• +3 more

Christian Graugaard is a Professor of Sexology at Aalborg University, affiliated with the Faculty of Medicine and the Department of Clinical Medicine. He is a key member of the Center for Sexology Research and leads Project SEXUS, aiming to study Danish sexual behavior comprehensively. His work emphasizes the intersection of biological, psychological, and cultural factors in human sexuality, challenging simplistic gender-based stereotypes. Research interests include gender differences in sexual behavior, societal norms influencing sexual health, and the cultural dimensions of human sexuality. He actively participates in public discourse, as seen in his DR-podcast interview on 'Ramt af kærlighed,' where he discussed the complex interplay between biology and culture in shaping sexual identities. Though no specific awards or grants are detailed here, his publications span advanced technical domains like spatiotemporal data analysis, federated learning, and trajectory modeling, suggesting interdisciplinary research collaborations. His work on systems like OneDB and SWASH highlights contributions to distributed computing and data science, which may underpin his methodologies in large-scale sexual behavior studies. He currently holds no listed students or formal advisees in the provided texts, and his involvement in labs/teams is limited to the Center for Sexology Research and Project SEXUS.

• Artificial Intelligence
• Machine Learning
• Bayesian Mathematics
• +3 more

Petr Taborsky is a postdoctoral researcher affiliated with the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His work focuses on artificial intelligence, machine learning, and computational methods within the Cognitive Systems group. Research interests include Bayesian inference , deep learning generalization , and graph-based learning , with applications in high-performance computing and federated learning . Key contributions involve developing the Bayesian Cut method for clustering and analyzing gradient noise in AI models. Recent publications highlight trends in European HPC/AI collaboration (2025) Bayesian graph cut techniques (2022) Statistical modeling in machine learning (2021)

Philipp Georg HaselwarterView profile

Assistant Professor

• Programming Languages
• Formal Verification
• Probabilistic Programming
• +3 more

Philipp Georg Haselwarter is an Assistant Professor at the Department of Computer Science , Aarhus University . His expertise lies in programming languages , formal verification , and cryptographic proofs . His research interests include Higher-order probabilistic programming Separation logic for program analysis Modular cryptographic proofs in Coq Resource-aware and cost-bounded formal methods Key trends in his recent publications span Formal verification techniques for probabilistic and cryptographic systems Integration of separation logic with cost and error analysis Development of frameworks like SSProve for cryptographic proofs Applications of type theory and higher-order logic in programming languages

Steffan Wittrup McPhee ChristensenView profile

Associate Professor

• Medicine
• Musculoskeletal Pain
• Pain
• +15 more

Steffan Wittrup McPhee Christensen is an Associate Professor at the Department of Medicine and Health Technology , Aalborg University , Denmark. His work bridges musculoskeletal physiotherapy and clinical biomedicine , focusing on neck and back pain , pain sensitivity , and exercise-based interventions . Academic Roles : Board member of the International Association for the Study of Pain (IASP) and Danish Physiotherapists Research Foundation since 2023. Teaching Philosophy : Emphasizes problem-based learning (PBL) and clinical simulation to develop clinical reasoning and rehabilitation planning skills. Research Interests include: Musculoskeletal disorders (neck/shoulder/back pain) Pain sensitivity and movement analysis Exercise interventions and clinical guidelines Global Burden of Disease applications to chronic pain management His scientific contributions span 114 publications (2011–2025) and 19 media appearances , including critiques of postural correction clothing as pseudoscience. Education : PhD in Clinical Science and Biomedicine (2018) Assistant Professor of Education (2016–2017) External Associate Professor at University College of Northern Denmark (2018–present)

Manja Gersholm GrønbergView profile

Researcher

• Statistical Genetics
• Machine Learning
• Biostatistics
• +5 more

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.

Anders GranholmView profile

Assistant Professor

• Clinical Trials
• Adaptive Trial Design
• Bayesian Statistics
• +4 more

Anders Granholm serves as an Assistant Professor in the Section of Biostatistics within the Department of Public Health at the University of Copenhagen. He maintains a dual academic-clinical appointment with Copenhagen University Hospital - Rigshospitalet, where he works in the Department of Intensive Care and Platform Trial Unit. His research profile bridges advanced statistical methodology with critical care clinical research, focusing on innovative trial designs that address urgent healthcare questions in intensive care settings. Dr. Granholm's primary research interests encompass clinical trials methodology, with particular expertise in adaptive (platform) trials, Bayesian inference approaches, statistical simulation for trial design, and evidence-based medicine applications including systematic reviews and clinical practice guidelines. His work represents a sophisticated integration of biostatistical innovation with practical clinical applications in critical care medicine. His recent publication portfolio demonstrates a strong focus on methodological advancements in clinical trial design, particularly for critical care applications. The publications reveal consistent engagement with platform trial methodology, Bayesian approaches to trial design and analysis, and practical implementation of complex trial designs in real-world intensive care settings. His work spans theoretical methodological development, practical implementation guidance, and application to specific clinical questions in critical care. As methods lead and coordinating investigator for the Intensive Care Platform Trial (INCEPT), a Bayesian adaptive platform trial in critically ill patients, and methods lead for the EMPRESS trial (an advanced Bayesian adaptive clinical trial), Dr. Granholm plays a central role in advancing trial methodology for critical care interventions. His collaborative network includes prominent researchers in critical care medicine and biostatistics across multiple institutions.

Aksel Karl Georg JensenView profile

Assistant Professor

• Causal inference
• Mediation analysis
• Observational vaccination studies
• +3 more

Aksel Karl Georg Jensen is an Assistant Professor in the Section of Biostatistics within the Department of Public Health at the Faculty of Health and Medical Sciences, University of Copenhagen. His research profile is centered around developing and applying advanced statistical methodologies to address complex questions in epidemiology and public health. His primary research interests include: Causal inference methodologies Mediation analysis techniques Observational vaccination studies Epidemiological study designs Regression modeling approaches Survival analysis methods Dr. Jensen's scholarly work demonstrates a strong focus on methodological development for clinical trials, vaccination studies in global health contexts (particularly in Guinea-Bissau), and investigations into social determinants of health outcomes through European birth cohort studies. His research spans both theoretical methodological development and applied epidemiological investigations, with particular emphasis on causal frameworks and mediation techniques. His scientific contributions include numerous peer-reviewed publications across biostatistics, epidemiology, and clinical research domains. His collaborative network extends internationally, with significant work in West African vaccination research and multinational European cohort studies. His research has received attention from news outlets, academic social media, and scholarly platforms, indicating substantial impact within both academic and broader communities.

• Computational Biology
• Structural Biology
• Molecular Dynamics
• +4 more

Emil Thomasen serves as a Guest Researcher in the Department of Biology within the Biomolecular Sciences unit at the University of Copenhagen's Faculty of Science. His research integrates computational methods with experimental structural biology approaches to investigate complex biomolecular systems. Position: Guest Researcher Institution: University of Copenhagen Department: Department of Biology Research Unit: Biomolecular Sciences Contact: fe.thomasen@bio.ku.dk | +4535323586 Thomasen's research focuses on developing and applying computational methodologies to understand protein structure, dynamics, and interactions. His work bridges quantum mechanics, molecular dynamics simulations, and experimental techniques including small-angle X-ray scattering (SAXS) and cryo-electron microscopy. He has made significant contributions to coarse-grained modeling, particularly with the Martini force field, and has pioneered approaches combining machine learning with quantum mechanical calculations for large molecular assemblies. His publication record demonstrates a clear trajectory in computational structural biology, with recent work emphasizing the integration of multiple experimental and computational techniques to characterize complex protein systems. The research shows increasing sophistication in handling large-scale biomolecular assemblies and applying advanced statistical methods like Bayesian inference for model refinement. Thomasen has established strong collaborative networks, frequently working with Professor K. Lindorff-Larsen and other researchers across computational chemistry and structural biology disciplines. His work has appeared in high-impact journals including Nature Communications, eLife, and Journal of Chemical Theory and Computation. As a recently completed Ph.D. graduate (2024), Thomasen continues to advance his research program as a Guest Researcher, building on his doctoral work focused on integrating coarse-grained molecular dynamics with SAXS data for characterizing multidomain protein assemblies.

Michael Charles SachsView profile

Associate Professor

• Biostatistics
• Causal Inference
• Survival Analysis
• +3 more

Michael Charles Sachs serves as Associate Professor in the Department of Public Health, Section of Biostatistics at the University of Copenhagen's Faculty of Health and Medical Sciences. He holds a Doctorate in Biostatistics from the University of Washington (awarded August 15, 2011) and maintains an active research program with publications spanning 2022-2025. His primary research focuses on biomarker signatures for treatment selection , computational methods for deriving symbolic bounds on causal effects , and regression modeling of cumulative estimands with censored event history data . His general areas of interest include reproducible research, computational statistics, R programming, Bayesian nonparametric models, and survival analysis. Professor Sachs teaches the advanced course "Programming and Statistical Modeling in R" for PhD students and health researchers, covering programming principles, loops, functions, and efficient data manipulation. His recent publications demonstrate expertise in causal inference methodology, survival analysis techniques, and statistical software implementation, with multiple articles accepted for publication in 2025. His work spans theoretical methodology development and practical applications in medical research, particularly in gastroenterology and celiac disease prediction. His scholarly output shows consistent productivity with multiple first-author publications in high-impact biostatistics journals including Journal of Statistical Software, Journal of Computational and Graphical Statistics, and Biostatistics. He actively organizes academic activities, including a May 2025 workshop on statistical modeling in R.

• Epistemology
• Disagreement
• Bayesian Modelling
• +5 more

Josefine Lomholt Pallavicini serves as a Teaching Assistant Professor in the Department of Science Education within the Faculty of Humanities at the University of Copenhagen. Her research bridges formal epistemology, Bayesian modeling, and the philosophy of scientific practice. Her primary research interests span epistemology, social epistemology, Bayesian modeling of disagreement, higher-order evidence, and diagrammatic reasoning in mathematics. She investigates how statistical formal models can explain phenomena like polarization and trust dynamics in epistemic communities, with particular focus on updating Bayesian frameworks to incorporate higher-order beliefs about belief formation reliability. Analysis of her four recent publications reveals a cohesive research trajectory focused on formal modeling of epistemic phenomena. Her work spans historical analysis of mathematical formalism (2022), computational modeling of group polarization (2021), and typological studies of mathematical diagrams (2018-2021), consistently applying formal methods to philosophical questions about evidence, disagreement, and representation. She is affiliated with the Social Epistemology Research Group (SERG) and contributed to the DFF-project 'The social epistemology and social psychology of disagreement.' Her research involves interdisciplinary collaboration with mathematicians and cognitive scientists, particularly on diagrammatic representation projects with M. W. Johansen and M. Misfeldt. Her laboratory and team involvement centers on SERG (Social Epistemology Research Group), where she collaborates on projects examining disagreement dynamics and higher-order evidence. Her work integrates computational modeling with traditional philosophical analysis, operating at the intersection of formal epistemology and cognitive science.

Jørgen Holm PetersenView profile

Associate Professor

• Inter-rater agreement
• Reference curves
• Latent variable models
• +2 more

Jørgen Holm Petersen serves as an Associate Professor in the Department of Public Health within the Faculty of Health and Medical Sciences at the University of Copenhagen. His primary academic affiliation centers on the Section of Biostatistics, where he maintains an active research profile with over 240 research outputs and significant collaborative networks across international institutions. His research expertise spans specialized statistical methodologies including inter-rater agreement analysis, reference curve development, latent variable modeling, and variance components models. Current work focuses on Bradley-Terry models with variance components for estimation and inference, particularly in rater agreement contexts. His methodological innovations directly support clinical and epidemiological investigations across endocrinology, environmental health, and public health domains. Recent publication trends reveal strong engagement with endocrine development research (puberty studies, hormone assays), environmental carcinogenesis (PFAS/organochlorine exposures), and health disparities (long COVID, vaccination uptake). His statistical frameworks enable sophisticated analysis of complex human exposures and longitudinal health outcomes, frequently applied through collaborations with clinical researchers at Copenhagen University Hospital. Petersen actively contributes to academic discourse through lectures on biostatistical applications in diverse fields (astronomy, voice research, cosmology) and service on review committees. His methodological presentations address critical issues in clinical trial randomization, instrumental variable techniques, and rating scale modeling for observer variation. Professional activities include editorial roles, committee chairmanships (e.g., Assessment Committee since 2014), and extensive lecture contributions. His work bridges theoretical statistics with practical healthcare applications, particularly in developmental endocrinology and environmental epidemiology.

• Critical Care Medicine
• Anaesthesiology
• Intensive Care Unit (ICU)
• +4 more

Morten Hylander Møller serves as Clinical Professor in the Department of Clinical Medicine at the University of Copenhagen, with clinical responsibilities in Anaesthesiology at Copenhagen University Hospital (Region H). His academic work centers on advancing evidence-based practices in intensive care through large-scale collaborative research. His research portfolio spans critical care methodology and clinical implementation: Development of core outcome sets for ICU trials using Delphi consensus Pharmacovigilance in critical care (e.g., amiodarone toxicity) Adaptive clinical trial design innovations Nationwide antibiotic consumption surveillance Sepsis guideline evaluation frameworks Scandinavian end-of-life care standardization Recent publications reveal a strategic focus on methodological rigor in ICU research, particularly through platform trials and international consensus-building. His work consistently addresses implementation gaps between clinical evidence and ICU practice, with strong emphasis on Scandinavian healthcare contexts and multinational collaboration patterns visible across all 2025 publications. Professor Møller actively contributes to major initiatives including the CLASSIC septic shock trial and Scandinavian Delphi consensus projects, demonstrating leadership in translating research into standardized ICU protocols across Nordic healthcare systems.