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Line ClemmensenView profile

Associate Professor

• Machine Learning
• Statistical Learning
• Data Science
• +6 more

Line Clemmensen serves as Associate Professor at DTU Compute (Department of Applied Mathematics and Computer Science), Technical University of Denmark, where she has held faculty positions since 2010. Her interdisciplinary work bridges statistical learning, machine learning, and real-world applications in mental healthcare, biotechnology, and agricultural informatics. Her academic credentials include: Ph.D. in Image Analysis and Computer Graphics from DTU (2010), thesis: "High-dimensional sparse data analysis" M.Sc. in Applied Mathematics from DTU (2006) Exchange studies at Universitat Politecnica de Catalunya, Barcelona (2004) Mathematical graduate from Falkonergården upper secondary school (2000) Clemmensen's research centers on machine learning and statistical learning with emphasis on low-resource modeling, representation learning, and AI evaluation methodologies. She applies these techniques to mental health (developing biosensor-based OCD monitoring systems), biotechnology (spectral data analysis for pharmaceutical quality control), and environmental science (crop health assessment via remote sensing). Her work consistently addresses challenges in data scarcity and model interpretability. Analysis of her recent publications reveals dominant trends in computational psychiatry (e.g., detecting OCD episodes through physiological signals and oxytocin biomarkers) and agricultural AI (linking soil microbiome composition to crop health via machine learning). Methodologically, she pioneers interpretable deep learning frameworks for low-resource settings and robust time-series analysis techniques for physiological data. She has supervised 10 PhD students to completion (including Jacob Søgaard Larsen on NIR management and Gudmundur Einarsson on psychiatric motion quantification) and mentored 13 Master's/Bachelor's students. Current funding includes the LundbeckFonden LF-Experiment grant for the FAST project (Fast Assessment of psychiatric Symptoms to Transform Mental Health Care). Within DTU Compute's Section for Statistics and Data Analysis, Clemmensen leads collaborations with Novo Nordisk (biostatistics), the Danish Meat Industry, and clinical psychiatry teams, focusing on real-world AI deployment in mental healthcare and industrial applications.

Tommy Sonne AlstrømView profile

Associate Professor

• Machine Learning
• Signal Processing
• Nanotechnology
• +3 more

Tommy Sonne Alstrøm is an Associate Professor at the Department of Applied Mathematics and Computer Science, DTU Compute, Danmarks Tekniske Universitet (DTU). His research focuses on machine learning, signal processing, and nanotechnology-based sensor systems. Key research areas include: Explainable AI for time series and spectroscopy Diffusion models for speech enhancement Federated learning optimization Probabilistic modeling of biosignals His recent publications demonstrate expertise in neural network geometry, federated learning privacy, and spectroscopic data analysis. Current work involves self-supervised learning for variable-channel time series and road condition modeling using LiRA-CD dataset. Contact: tsal@dtu.dk

• Bayesian Statistics
• Gaussian Processes
• Variational Inference
• +5 more

Michael Riis Andersen is an Associate Professor at the Department of Applied Mathematics and Computer Science, DTU Compute , Technical University of Denmark. His research focuses on Bayesian statistics, Gaussian processes, variational inference, and probabilistic programming, with applications in computer vision, natural language processing, and spatio-temporal modeling. His recent work includes: (1) Bayesian optimization with uncertainty-aware frameworks, (2) recommender systems balancing accuracy with editorial constraints, (3) geometry-aware transformers for geospatial analysis, and (4) scalable inference methods for neural networks and Gaussian processes. Many articles explore variational inference and probabilistic programming to enhance computational reliability and efficiency. Research keywords : Bayesian Statistics, Variational Inference, Machine Learning, Computer Vision, Probabilistic Programming, Spatio-Temporal Modeling.

• Probabilistic Machine Learning
• Deep Probabilistic Programming
• Statistical Structural Bioinformatics
• +5 more

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.

• Programming Languages
• Theory of Computation
• Formal Verification
• +3 more

Atta Samiullah serves as an Instructor in the Department of Computer Science at the University of Copenhagen, affiliated with the Programming Languages and Theory of Computation (PLTC) section. His role encompasses teaching responsibilities across undergraduate and graduate computer science programs including BSc in Computer Science, Computer Science and Economics, Machine Learning and Data Science, and Communication & IT. His research profile aligns with PLTC's core focus areas: Programming language technology and formal verification methodologies Theory of computation with applications in distributed ledger systems Security and privacy preservation techniques in software systems Financial technology applications and blockchain infrastructure Compiler technology and high-performance computing architectures Quantum programming language design The PLTC section operates through specialized research groups including Decentralized Systems, Security & Privacy, HOT Lab, FUTHARK (Functional Technology for Modern Architectures), and Quantum Programming. These groups maintain active industry collaborations in fintech and blockchain sectors while developing open-source tools like Moscow ML and MLKit compilers. Research output spans probabilistic programming frameworks, Stein variational inference techniques, and applications in bioinformatics and vaccine design. No scientific awards or student supervision activities are documented in current institutional records. His work contributes to the department's mission in advancing programming language theory with practical implementations in secure financial systems and distributed computing environments.

Firas Al-NiaimiView profile

Adjunct Professor

• Dermatology
• Laser Dermatology
• Acne
• +3 more

Firas Al-Niaimi serves as an Adjunct Professor and Senior Research Fellow at Aalborg University's Faculty of Health Sciences, Department of Dermatology and Venereology, with clinical work based at Aalborg University Hospital. His dual academic-clinical role focuses on advancing evidence-based dermatological interventions through rigorous research and international collaboration. Dr. Al-Niaimi's research spans laser dermatology (including pulsed dye lasers for vascular lesions and fractional CO2 for rejuvenation), acne pathogenesis with pioneering work on microbiome modulation, and ethnic dermatology addressing conditions like melasma and dermatosis papulosa nigra in diverse populations. His fingerprint analysis confirms dermatology as his core discipline (100%), with significant contributions to rhinophyma (81%), acneiform eruptions (59%), and chloasma (36%). Analysis of his 2025 publications reveals three dominant trends: (1) refinement of laser parameters for ethnic-specific skin conditions, (2) paradigm-shifting approaches to acne through microbiome science, and (3) consensus-driven standardization of cosmetic procedures. His work consistently bridges clinical practice and scientific innovation, particularly in treating vascular anomalies and pigmentation disorders across global populations. Scientific Awards No awards or fellowships were documented in the source material Advising and Research Activity Student supervision details unavailable Active grant involvement inferred from 69 publications and 2 research datasets Key dataset: Lessons Learned from the First Decade of Laser Assisted Drug Delivery (2021) Collaborative Networks International multi-center collaborations feature prominently, including a 2025 consensus paper with 33 co-authors across 5 continents. His PlumX metrics show significant reader engagement (28 Mendeley readers for 2021 dataset) and citations in laser therapy applications.

Erik Thorlund ParnerView profile

Professor

• Biostatistics
• Time-to-event data
• Comparison of measurement methods
• +1 more

Erik Thorlund Parner is a Professor at Aarhus University, affiliated with the Department of Public Health and Biostatistics. His research focuses on developing and applying statistical methods in health sciences, particularly in time-to-event data analysis, causal inference, and measurement method comparisons. He collaborates with researchers at Aarhus University Hospital and teaches advanced statistics in the Health PhD program using the flipped classroom method. His work emphasizes methodological contributions to prioritized event comparison and causal parameter estimation for subpopulations. Teaching: He delivers basic and advanced statistics courses in the PhD program at Health, employing instructional videos and interactive teaching. His flipped classroom approach replaces traditional lectures with student-centered activities. Collaborations: Active collaborations with multiple researchers at Aarhus University Hospital, particularly in clinical trials and medical statistics. Notable projects include the Strong Hip Trial and studies on coronary CT angiography outcomes. Projects: Led the project 'Psychological distress and risk of myocardial infarction' (2015–2018), investigating incidence, prognosis, and treatment effects using natural experiments like bereavement and doctor variation.

Rasmus Tangsgaard VarneskovView profile

Professor

• Econometrics
• High-Dimensional Statistics
• Asset Pricing
• +3 more

Rasmus Tangsgaard Varneskov is a Professor of Statistics and Financial Econometrics at the Department of Finance, Copenhagen Business School (CBS). He is also employed by Alphadyne Asset Management. His research focuses on econometrics, high-dimensional statistics, asset pricing, and financial economics, with a strong emphasis on time series analysis and financial econometrics methodologies. Before joining CBS, he was a postdoctoral researcher in Finance at Northwestern University's Kellogg School of Management. He holds affiliations with the Center for Big Data in Finance (BIGFI) and the Center for Statistics at CBS. His educational background includes advanced studies in statistics and econometrics, though specific degrees are not detailed in the provided text. Rasmus' research interests span advanced statistical methodologies for financial data, including volatility estimation, bootstrap techniques, predictive regressions, and structural change analysis. His work has been published in top journals such as Journal of Econometrics , Journal of Financial Economics , and Quantitative Economics . A key achievement is his 2023 Econometric Theory Multa Scripsit award for prolific and impactful contributions. His publications address topics like Laplace transforms of volatility, consistent inference in predictive regressions, and dynamic hedging strategies. While no formal advisees are listed, his industry collaborations (e.g., with Nordea and Alphadyne) suggest applied research engagement. He contributes to CBS's research initiatives through participation in interdisciplinary centers focused on big data and financial statistics.

• Evolutionary Biology
• Variation and Evolution in Gibbons (Hylobatidae)
• Sexual Dimorphism
• +10 more

Hans Christian Petersen is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, where he conducts interdisciplinary research combining evolutionary biology and applied statistics. His work focuses on the variation and evolution in gibbons, sexual dimorphism, and Mesolithic populations in Northern Europe, supported by advanced statistical techniques including multivariate analysis and geometric morphometrics. Research Interests: Evolutionary Biology, particularly gibbon (Hylobatidae) evolution and sexual dimorphism Mesolithic populations in Northern Europe, including skeletal and population analysis Applied statistics in biological data, including nonparametric methods and limits of agreement Geometric morphometrics and multivariate statistical modeling His recent publications show a strong trend in both paleoanthropological studies (e.g., Maglemosian skeleton, Mesolithic burials) and methodological statistical research (e.g., simulation studies on limits of agreement), reflecting a dual expertise in empirical archaeology and statistical innovation. This interdisciplinary approach enables robust analysis of complex biological datasets. Scientific Engagement: Regular speaker on topics such as Neanderthal biology and human evolution Active peer reviewer and conference participant Contributor to public media on human evolution and societal topics He has taught courses such as BB512 Population and Evolution and has engaged in international academic activities, including guest lectures and research stays. While specific grants and advising roles are not detailed, his extensive publication and activity record suggest active research leadership. He is involved in collaborative networks across Europe, particularly in archaeological and anthropological research.

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.

• Genetic Epidemiology
• Translational Medicine
• Mendelian Randomization
• +4 more

Marianne Benn is a Clinical Professor in the Department of Clinical Medicine, specializing in Clinical Biochemistry at the University of Copenhagen's Faculty of Health and Medical Sciences. She works with the Research and Innovation Office and is based at Blegdamsvej 3B, 2200 København N. Dr. Benn holds qualifications as a Medical Doctor, PhD, and DMSc. Her academic credentials reflect a strong foundation in both clinical medicine and advanced scientific research. Marianne Benn's research focuses on genetic epidemiology in translational medicine, specifically using Mendelian randomization in large population cohorts including the Copenhagen General Population Study, the Copenhagen City Heart Study, and the UK Biobank. Her work aims to differentiate biomarkers with causal effects on disease from simple markers of disease, identify patterns of common genetic variants defining individuals at high risk of disease, predict effects of drugs from genetic variation in genes targeted by the drugs, and use genetics to predict public health effects of treatment or intervention. Her research spans cardiovascular disease, metabolic disorders, and the genetic basis of various health conditions with significant clinical implications. Her publication record demonstrates expertise across cardiology, genetic epidemiology, and clinical medicine, with a particular focus on dyslipidemia, familial hypercholesterolemia, and the application of Mendelian randomization techniques to establish causal relationships in observational data. Dr. Benn actively contributes to major European clinical guidelines, including the ESC/EAS Guidelines for the management of dyslipidaemias, and her work informs evidence-based clinical practice across multiple medical specialties. Research has been picked up by 17 news outlets Blogged about by 2 sources Shared by 572+ X (Twitter) users Featured on multiple Facebook pages Read by numerous researchers on Mendeley Dr. Benn maintains active collaborations with B.G. Nordestgaard and other international researchers, contributing to the European Atherosclerosis Society consensus statements and other major scientific initiatives that shape clinical practice guidelines across Europe.

• Computational Genomics
• Machine Learning
• Bioinformatics
• +3 more

Arnór Ingi Sigurdsson serves as a Research Fellow within the Rasmussen Group at the Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, under the Faculty of Health and Medical Sciences. His work bridges computational innovation with biomedical applications, focusing on genomic data analysis and machine learning methodologies to address complex healthcare challenges. His research spans computational genomics, deep learning, and bioinformatics, with specific emphasis on genetic risk prediction for surgical outcomes, metagenomic binning techniques, and integrative models for human genomic data. Sigurdsson develops advanced neural network architectures to improve precision medicine applications, particularly in surgical risk assessment and microbiome characterization, demonstrating strong interdisciplinary collaboration across computational and clinical domains. Recent publications reveal a clear trajectory toward leveraging deep learning for genomic data interpretation, with impactful contributions in PLoS ONE and Communications Biology. His work consistently applies cutting-edge AI techniques—including adversarial autoencoders and integrative modeling—to solve concrete biomedical problems, establishing him as an emerging contributor in computational genomics. As an integral member of the Rasmussen Group, Sigurdsson collaborates extensively with cross-institutional teams at the Center for Protein Research, utilizing large-scale genomic datasets to advance personalized medicine solutions while contributing to the group's reputation in computational biology.

• Cancer Genomics
• Genome Instability
• DNA Repair
• +7 more

Fran Supek is a Professor at the University of Copenhagen's Faculty of Health and Medical Sciences, where he leads the Genome Data Science laboratory at the Biotech Research & Innovation Centre (BRIC). He also maintains a research group at the IRB Barcelona. Supek is the Principal Investigator of ERC Starting and ERC Consolidator projects, an EMBO Young Investigator, and holds an ICREA tenured professor position (currently on leave). Dr. Supek obtained his PhD in Molecular Biology in 2010 from the University of Zagreb, followed by postdoctoral training at the Centre for Genomic Regulation (CRG) as a Marie Curie fellow. He established his independent laboratory at IRB Barcelona in 2017 as a "Ramón y Cajal" tenure-track fellow, and expanded to BRIC in 2024. His research focuses on statistical genomics and machine learning approaches to study genome stability mechanisms, particularly through analysis of mutation patterns in cancer. The Genome Data Science lab performs large-scale bioinformatic studies of multi-omic data from human tumors, populations, and metagenomes. Key research areas include DNA repair mechanisms, mRNA synthesis and turnover pathways, genetic interactions in cancer genomes, and machine learning approaches for inferring gene function from genomic data. Analysis of Supek's recent publications reveals a strong emphasis on cancer genomics, with particular focus on mutational signatures, DNA repair deficiencies, and the application of artificial intelligence to genomic data analysis. His work spans from fundamental mechanisms of genome instability to translational applications in cancer diagnosis and treatment. ERC Starting Grant recipient ERC Consolidator Grant recipient EMBO Young Investigator ICREA tenured professor (on leave) As Principal Investigator of multiple ERC projects, Supek leads a research program with significant funding for innovative genomic research. His laboratory at BRIC and IRB Barcelona conducts interdisciplinary research combining computational approaches with experimental validation to address fundamental questions in genome biology and cancer evolution. The Genome Data Science lab maintains active collaborations across multiple institutions, with research spanning statistical genomics, machine learning applications in bioinformatics, and experimental validation of computational predictions in cancer biology.

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.