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Manfred LaubichlerView profile

Professor

• Theoretical Biology
• Evolutionary Developmental Biology
• History of Biology
• +7 more

Manfred Laubichler is President’s Professor of Theoretical Biology and History of Biology at Arizona State University (ASU), where he serves as Director of the School of Complex Adaptive Systems and the Global Biosocial Complexity Initiative. He is also an External Professor at the Santa Fe Institute (SFI) and co-director of the ASU-SFI Center for Biosocial Complex Systems. His academic affiliations include the Center for Social Dynamics and Complexity, the Center for Biology and Society, the Center for Evolution and Medicine, and the Water Institute at ASU. His educational background spans zoology, philosophy, and mathematics from the University of Vienna; biology from Yale University; and history/history of science from Princeton University. Laubichler's research centers on evolutionary novelties across scales—from genomes to knowledge systems—alongside the structure of evolutionary theory and the evolution of scientific knowledge. His work bridges theoretical biology, philosophy of science, and complex systems, with a strong emphasis on biosocial complexity and sustainability. He has contributed extensively to understanding the conceptual foundations of evolutionary developmental biology (Evo-Devo) and the historical and philosophical dimensions of biological theory. His recent publications reflect an interdisciplinary trend, integrating epidemiology, public health, and social behavior during crises such as the COVID-19 pandemic, while maintaining a core focus on theoretical biology, history of science, and complex systems. Themes include trust, scientific responsibility, epistemology of science, and modeling biosocial dynamics. Elected Fellow of the American Association for the Advancement of Science Fellow at the Wissenschaftskolleg zu Berlin Vice Chair of the Global Climate Forum Laubichler has led multiple NSF-funded research initiatives in digital humanities, history and philosophy of science, and complex adaptive systems. He has advised numerous doctoral projects and co-led training programs such as the Embryo Project. He is actively involved in interdisciplinary labs and centers, including the Information and Competition Lab, Complexity Economics Lab, and the Social Insect Research Group. His leadership extends to editorial roles in journals like Biological Theory , Journal of Experimental Zoology Part B , and the Archimedes series.

Lars DittmannView profile

Professor

• Software-Defined Networking (SDN)
• Network Function Virtualization (NFV)
• 5G Networks
• +6 more

Lars Dittmann is a Professor at the Department of Electrical and Photonics Engineering at the Technical University of Denmark (DTU), leading the Networks Technology and Service Platforms section. His work bridges advanced networking technologies with real-world applications in healthcare and transportation. Academic Role: Professor, Head of Section University: Technical University of Denmark (DTU) Department: Networks Technology and Service Platforms Research Interests: Professor Dittmann specializes in Software-Defined Networking (SDN) , 5G and IoT technologies , and energy-efficient network design , with a focus on applications in telemedicine and transportation systems . His work integrates machine learning for privacy-preserving traffic analysis and explores optical networks for high-bandwidth scenarios. Scientific Contributions: His recent publications emphasize green cellular networks using SDN/NFV/C-RAN, IoT benchmarking for coverage and mobility, and secure edge architectures for railways. Collaborative projects like the Future Patient telerehabilitation program highlight his interdisciplinary impact. Supervision: He supervises PhD candidates such as Radheshyam Singh, focusing on SDN-based IoT security and 5G network optimization. Labs & Projects: Leads initiatives like EXplorative network PLAnnINg and Broadband Trial Integration , addressing challenges in network reliability , emergency communication , and optical data center scaling .

Rasmus WaagepetersenView profile

Professor

• Spatial Statistics
• Quantitative Genetics
• Markov Chain Monte Carlo Methods
• +3 more

Rasmus Waagepetersen is a Professor in the Department of Mathematical Sciences at Aalborg University, affiliated with The Faculty of Engineering and Science. His research focuses on spatial statistics, quantitative genetics, and statistical methodology for spatial point processes. He leads and participates in interdisciplinary projects such as urbanLab (spatial data analysis for urban planning) and studies on microbiome interactions in agricultural systems. Key research areas include spatial point processes, Markov chain Monte Carlo methods, and statistical inference for complex ecological and biomedical data. His work frequently involves collaborations with environmental and biological scientists, as seen in projects analyzing root microbiota assemblies in legumes and climate data for building simulations. Waagepetersen has contributed to methodological advancements in spatial statistics, including goodness-of-fit tests, likelihood-based inference for log Gaussian Cox processes, and quasi-likelihood approaches for case-control point pattern data. His research has been supported by grants from institutions like the Villum Foundation. Key Projects: urbanLab, Klimadata til fugtsimuleringer, Nod factor signaling in plant microbiota. Grants: Multiple projects funded by the Villum Foundation and Danish research councils. His recent publications address topics such as space-time point processes, microbiome data analysis, and statistical modeling in education. Waagepetersen maintains an active research group and collaborates internationally on both theoretical and applied statistical problems.

Morten NielsenView profile

Professor

• Harmonic Analysis
• Approximation Theory
• Sparse Signal Processing
• +7 more

Morten Nielsen is a Professor in the Department of Mathematical Sciences at the Faculty of Engineering and Science, Aalborg University, Denmark. His research is centered on harmonic analysis, approximation theory, and sparse signal processing, with strong emphasis on modulation spaces, wavelet systems, and matrix-weighted function spaces. His research interests include: Harmonic and Functional Analysis Approximation Theory and Sparse Representations Wavelet and Time-Frequency Analysis Nonlinear Approximation in Banach Spaces Matrix-Weighted Function Spaces Applications in Signal and Image Processing The recent publications show a consistent focus on theoretical developments in function spaces, particularly α-modulation and Triebel-Lizorkin spaces, with applications to sparse representations and nonlinear approximation. His work often involves deep connections between operator theory, frame theory, and signal decomposition techniques. Although no specific scientific awards are listed, his extensive publication record in top-tier journals and book chapters in prestigious series such as Applied and Numerical Harmonic Analysis indicates significant recognition in the mathematical community. Morten Nielsen has been involved in multiple research projects, including 'Generalized Wavelet Systems', 'Sparse Representation of Data', and 'Muckenhoupt Matrix Weights', often in collaboration with researchers like Hrvoje Šikić. He has supervised at least one PhD student and continues to be actively involved in research, as evidenced by publications in 2025. His work is supported by ongoing research grants and collaborations across Europe. He leads and participates in research groups focused on harmonic analysis and approximation theory, contributing to both theoretical advancements and practical applications in data science and engineering.

• Multiple Sclerosis
• Neurology
• Neuropathic Pain
• +7 more

Henrik Boye Jensen is a Clinical Associate Professor at the Department of Regional Health Research, IRS - Sygehus Lillebælt, Forskningsenhed for Neurologi (Kolding), affiliated with the University of Southern Denmark. His work bridges clinical neurology and research, with a strong focus on multiple sclerosis, stroke, and neurorehabilitation. Institution: University of Southern Denmark Department: Department of Regional Health Research Research Unit: Research Unit for Neurology, Kolding Academic Rank: Clinical Associate Professor His research interests center on multiple sclerosis , particularly disease progression, pharmacological treatments, and the role of exercise in neuroprotection. He also investigates stroke rehabilitation and diagnostic challenges in neurological conditions such as Lyme neuroborreliosis. His work frequently employs advanced methodologies including randomized controlled trials , cohort studies , and neuroimaging (MRI) . The 15 most recent articles reflect a strong trend in clinical neuroscience , with emphasis on MS phenotypes , pharmacological interventions , exercise as therapy , and diagnostic accuracy . His publications span high-impact journals in neurology and rehabilitation, demonstrating consistent scholarly output. Scientific Awards: No specific awards listed in the provided text. Henrik Boye Jensen has been actively involved in advising and research projects. He has contributed to at least three supervised works, including a Ph.D. thesis, and is a working partner in the RoboRehab project on stroke rehabilitation. He has participated in multiple collaborative research initiatives and has delivered presentations in both academic and public settings. He is also involved in public discourse through media contributions on neurological health issues. Labs and Teams: He is associated with the Research Unit for Neurology in Kolding and collaborates extensively within the Region of Southern Denmark and with national and international research groups, particularly in MS and stroke research.

Stefan Kragh NielsenView profile

Professor

• Plasma Physics
• Fusion Energy
• Collective Thomson Scattering
• +7 more

Stefan Kragh Nielsen is a Professor and Section Leader in the Department of Physics at the Technical University of Denmark (DTU), specializing in Plasma Physics and Fusion Energy. He is actively involved in experimental and theoretical research related to fusion plasma diagnostics, particularly collective Thomson scattering and microwave-based measurements in tokamak devices such as ASDEX Upgrade and Wendelstein 7-X. His research interests include: Plasma Physics and Fusion Energy Collective Thomson Scattering Fast Ion Dynamics Electron Cyclotron Resonance Heating Parametric Instabilities Microwave Diagnostics The recent publications highlight a strong focus on advanced diagnostics, nonlinear wave interactions, and fast ion behavior in fusion plasmas. His work spans theoretical modeling, experimental validation, and instrumentation development, particularly in high-frequency microwave systems for continuous plasma monitoring. Trends show increasing emphasis on reduced modeling techniques and real-time diagnostic capabilities for next-generation fusion reactors. Scientific contributions include: Development of ultrafast digitizers for microwave diagnostics Commissioning of 174 GHz CTS systems at W7-X Modeling of metaplectic geometrical optics for plasma waves Investigation of parametric decay in gyrotron beams He actively supervises multiple PhD students on topics such as non-linear processes in electron Bernstein wave heating, ion dynamics via CTS, and parametric decay instabilities in spherical tokamaks. His projects are well-funded and aligned with international fusion research goals. He has collaborated extensively with major fusion facilities including ASDEX Upgrade, Wendelstein 7-X, and JET. No formal awards are listed in the provided text. He leads a research team focused on advancing plasma diagnostic capabilities for future fusion reactors.

Patrick MunkView profile

Associate Professor

• Antimicrobial Resistance
• Genomic Epidemiology
• Metagenomics
• +12 more

Patrick Munk is an Associate Professor at the National Food Institute, Technical University of Denmark (DTU), where he leads research in the Research Group for Genomic Epidemiology and contributes to the DTU Microbes Initiative. His work integrates genomics, metagenomics, and bioinformatics to study antimicrobial resistance (AMR) across human, animal, and environmental systems. He is actively involved in global surveillance efforts and is currently accepting PhD students. PhD, MSc Member, The Danish Young Academy (2024–2029) Patrick Munk’s research focuses on antimicrobial resistance, genomic epidemiology, metagenomics, and bioinformatics . His work leverages large-scale sequencing of sewage, livestock, and environmental samples to understand the global spread of resistance genes. He has led and contributed to major international studies, including the global sewage surveillance project across 101 countries, and has developed key genomic resources such as the PanRes and MetalResistance databases. His recent publications span high-impact journals like Nature Communications and Nature Microbiology , with themes centered on sewage-based surveillance, fecal resistomes in livestock, source attribution of microbial communities, and novel sequencing methodologies . His research has been widely covered in media and policy documents, reflecting its societal impact. Genomic analysis of sewage from 101 countries (Nature Communications, 2022) Time-series metagenomics for source-attributed surveillance (Nature Communications, 2024) High-throughput single-cell metagenomic sequencing (Frontiers in Microbiology, 2025) Patrick Munk has received recognition through significant media coverage, policy citations, and widespread academic engagement. His work has been highlighted in over 40 news outlets, referenced in policy sources and patents, and discussed across social media and academic platforms like Mendeley and Wikipedia. He supervises multiple PhD students and is a Principal Investigator or Supervisor on several active and completed projects, including Bioinformatic methods for multi-metagenome integration and Host range and epidemiology of antibiotic resistance . He is also a co-creator of key datasets that support open science in AMR research. His lab is part of a broad collaborative network involving DTU, international consortia, and public health agencies.

• Image Registration
• Image Segmentation
• Image Classification
• +6 more

Sune Darkner is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in the Image Analysis, Computational Modelling, and Geometry research section. His work focuses on medical image processing with particular emphasis on neuro-imaging data including MRI and PET scans. His primary research interests include Image Registration, Segmentation and Classification of Medical Image Data , with a specific focus on estimation of image similarity as his main research interest. Darkner strongly believes that the implementation of image processing algorithms should be thoroughly tested and reflect the theoretical properties as accurately as possible. His work primarily centers on neuro-imaging data such as MRI and PET. His recent publications (2024-2025) reveal a strong focus on medical image analysis, with particular emphasis on tumor volume delineation, deformable image registration with physics constraints, and applications of deep learning in medical imaging. His work spans both theoretical foundations of image processing and practical clinical applications. Darkner previously held a Post Doc position at the Technical University of Denmark from February 2009 to January 2010, demonstrating his longstanding engagement with image analysis research in the Danish academic community.

Peter SestoftView profile

Professor

• Programming Languages
• Software Development
• Parallel Programming
• +4 more

Peter Sestoft is a Professor at the IT University of Copenhagen (ITU), leading the Computer Science Department since 2017. His primary roles include academic leadership, research in programming languages and software engineering, and teaching. He holds a PhD in Computer Science from the University of Copenhagen (1991) and has held academic positions at institutions like the Royal Veterinary and Agricultural University and the Technical University of Denmark before joining ITU in 1999. His research focuses on programming languages, functional and managed object-oriented languages, parallel programming, compilers, and spreadsheet implementation technologies. He has developed influential tools like the C5 Generic Collection Library for C# and Moscow ML, a Standard ML implementation. His work on Funcalc and Corecalc advanced spreadsheet technology with user-defined functions and efficient recalculation algorithms. Key contributions include over 30 publications, including books on programming language concepts and Java/C# syntax. He has led major research projects such as 'Popular Parallel Programming' (P3) and 'Probabli' for actuarial calculations. His academic service includes roles on national grant committees and international conference organizing committees. Notable advising includes PhD students like Andrzej Wasowski (ITU Professor) and David Christiansen (Director of Haskell Foundation). His work has been recognized through grants exceeding 25 million DKK and collaborations with institutions like Microsoft Research and Harvard University.

• Applied probability theory
• Markov chain Monte Carlo methods
• Spatial statistics
• +4 more

Jesper Møller is a Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science, specializing in Statistics and Mathematical Economics. His research focuses on advanced statistical methodologies with applications across various scientific domains. His educational background includes extensive training in mathematical sciences, though specific degree details aren't provided in the current materials. His research interests span: Applied probability theory Markov chain Monte Carlo methods (MCMC) Spatial statistics Stochastic geometry Stochastic simulation Point process modeling Professor Møller's recent publication record shows consistent productivity with 239 research outputs including journal articles, reports, and book chapters. His work demonstrates strong focus on spatial point processes, Bayesian inference methods, and applications of stochastic geometry. The research trends indicate increasing sophistication in modeling complex spatial patterns and developing computational methods for statistical inference. His scientific contributions have been supported by numerous research projects, with 28 projects documented including the current "Peculiar Distribution Functions and Interesting Stochastic Processes" (2022-2026). His work has generated significant scholarly impact with citations across multiple disciplines. Professor Møller has supervised 8 PhD students and maintains active collaborations across international research networks. His current projects suggest continued research activity in developing novel statistical methodologies for complex spatial data analysis with applications in materials science, neuroscience, and environmental statistics.

• Protein Structure and Function
• Mass Spectrometry
• Biochemical Methods Development
• +3 more

Professor Peter Højrup is affiliated with the Department of Biochemistry and Molecular Biology at the University of Southern Denmark, focusing on protein structure and function through advanced biochemical techniques such as mass spectrometry and chemical cross-linking. His research spans biomedical mass spectrometry, systems biology, and proteomics, with a particular emphasis on endoplasmic reticulum proteins like calreticulin and calnexin. Education: PhD in Molecular Biology (1986, Odense University), MSc in Molecular Biology (major) and Chemistry (minor) (1982, University of Aarhus) His research interests include: Developing methods for determining protein 3D structures and interactions via chemical cross-linking and mass spectrometry. Fast glycosylation analysis of immunoglobulins and cancer markers. De novo proteomics of fish mucus proteins. The articles highlight trends in mass spectrometry applications, structural biology, and glycosylation studies, with recent work on SARS-CoV-2 epitope mapping, therapeutic antibody characterization, and host cell protein quantitation. Supervision includes training postdocs, PhD, MSc, and BSc students, though specific student names are not provided.

• Mathematical Statistics
• Probability Theory
• Applied Statistics
• +3 more

Ernst Hansen is an Associate Professor at the Department of Mathematical Sciences, University of Copenhagen . He is based at University Park 5, Copenhagen Ø, and his work spans mathematical statistics, probability theory, and applied statistical modeling in public health and finance. Email: erhansen@math.ku.dk Research areas: Public health interventions, Markov chain applications, measure-theoretic probability, and statistical education His publications include textbooks and peer-reviewed articles on topics such as neck/shoulder pain prevention , continuous-time rating transition probabilities , and geometric drift analysis . While his work intersects with causal inference and stochastic processes, no formal awards or student advising records are documented in the provided texts.

• Statistics
• Machine Learning
• Functional Data Analysis
• +3 more

Anton Rask Lundborg is a Postdoctoral Research Fellow at the Department of Mathematical Sciences, University of Copenhagen. He works in the areas of Statistics, Machine Learning, and Functional Data Analysis. Key research areas: Statistics, Machine Learning, Functional Data Analysis, Causal Inference, Bioinformatics, Nonparametric Statistics Research groups: SPT (Statistical Learning Theory), CoCaLa (Collaborative Causal Learning) His recent work focuses on causal feature selection, variable significance testing, and functional data analysis applications. Publications span journals like Journal of the American Statistical Association , Briefings in Bioinformatics , and Annals of Statistics . Methodological innovations include the Projected Covariance Measure and conditional independence testing in Hilbert spaces. Explore his full research profile and publications via ORCID or the University of Copenhagen's Mathematical Sciences website .

• Bioinformatics
• Machine Learning
• Computational Biology
• +7 more

Jan Baumbach is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he leads cutting-edge research at the intersection of computer science, bioinformatics, and biomedical data science. His work integrates machine learning, network biology, and systems medicine to address complex challenges in health and disease. His research interests include Bioinformatics , Machine Learning , Gene Regulatory Networks , Drug Repositioning , Biomedical AI , and Computational Biology . He applies advanced computational methods to analyze large-scale biological data, with applications in cancer, metabolic diseases, dermatology, and infectious diseases. The 15 most recent publications highlight a strong trend toward biomedical artificial intelligence , network-based analysis , and translational bioinformatics . His work spans from foundational machine learning in healthcare to clinical applications in osteoarthritis, bone regeneration, and skin biology. A consistent theme is the integration of multi-omics data and the development of privacy-preserving federated learning frameworks for distributed healthcare systems. Jan Baumbach has been principal investigator on several major research projects, including: EU Horizon2020: Privacy-preserving federated machine learning in distributed healthcare Danish National Research Foundation: ATLAS Center for Functional Genomics of Tissue Plasticity Villum Foundation: Big Data Bioinformatics (Young Investigator Programme) Carlsberg Foundation: Computational profiling of bacterial volatile metabolomes (ProVol) He has supervised 13 PhD students and has an extensive publication record of 262 works. His research has been featured in high-profile media outlets, emphasizing the societal impact of drug repositioning and AI in medicine. He leads a research group focused on computational systems biology , with strong collaborations across Europe in the fields of genomics, metabolomics, and clinical data science.

• Functional data analysis
• Statistical inference for dependent data
• Statistical inference for stochastic processes
• +3 more

Helle Sørensen is a Professor at the Department of Mathematical Sciences, University of Copenhagen. Her work bridges theoretical and applied statistics with interdisciplinary applications in biological and environmental sciences. Education : BSc (1993), MSc (1997), PhD (2000) in Statistics from University of Copenhagen. Employment : Professor (2018–present), Head of Data Science Lab (2018–2021), Professor MSO and head of Laboratory for Applied Statistics (2013–2018), Associate/Assistant Professor across multiple departments (2000–2013). Research Interests focus on: Functional data analysis Statistical inference for dependent data and stochastic processes Applications in biology, agriculture, and food science Her recent publications highlight statistical methodologies applied to: Enzymatic degradation of plant material Multivariate analysis in metabolic studies Random forest efficiency in metric spaces Quantile regression for longitudinal data Child food texture preferences and insect acceptance Teaching includes courses in basic probability, statistical theory, and applied statistics for bio/life sciences students. She supervises BSc, MSc, and PhD students in Statistics with co-supervision roles in interdisciplinary fields.