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Erwin SchoofView profile

Associate Professor

• Proteomics
• Mass Spectrometry
• Single-Cell Analysis
• +7 more

Erwin Schoof is an Associate Professor at the Department of Biotechnology and Biomedicine , Technical University of Denmark. He leads the Cell Diversity Lab and focuses on advancing proteomics and mass spectrometry technologies. Expertise in single-cell proteomics , stem cell niches , and bioinformatics . Active in myelofibrosis and leukemia research , with applications in UN Sustainable Development Goals . Research Trends from 2025–2024 include: Machine learning-driven peptide sequencing (InstaNovo, InstaNexus). Single-cell resolution tools for mapping hematopoietic stem cells and tumor microenvironments . Biomarker discovery in chronic diseases and respiratory conditions . Supervision : Mentors multiple PhD students on single-cell proteomics , omics data analysis , and biotherapeutic production . Labs & Collaborations : Collaborates with international teams on plasma proteomics , 3D bioengineering , and advanced mass spectrometry workflows .

Navid BayatiView profile

Associate Professor

• Renewable/Hybrid Power Systems
• Protection and Transient Analysis of Power Grids
• Microgrids and Active Energy Networks
• +5 more

Navid Bayati is an Associate Professor at the University of Southern Denmark, affiliated with the Institute of Mechanical and Electrical Engineering and the Centre for Industrial Electronics. He leads the Control and Protection of Smart Grids (CAP-SG) group and focuses on renewable/hybrid power systems, microgrid protection, and grid code compliance. Education: Ph.D. in Power Systems & Microgrid Protection (2020, Aalborg University); M.Sc. in Power Systems (2017, Amirkabir University of Technology) His research spans renewable energy integration , transient analysis , grid interconnection , and digital twin applications . Recent work includes machine learning for carbon emission prediction, fault localization in DC microgrids, and supercapacitor resilience in hybrid systems. Collaborations include projects like IEA Wind Task 50 and RePoSys , addressing grid renovation, life cycle assessment, and digital twin resilience. His teaching portfolio covers power electronics , energy management , and microgrid control .

• Ancient DNA Analysis
• Paleogenetics
• Forensic Genetics
• +4 more

Elena Irene Zavala serves as an Assistant Professor in the Section of Forensic Genetics and Guest Researcher at the Globe Institute, Section for Geogenetics at the University of Copenhagen's Faculty of Health and Medical Sciences. Her work bridges forensic science with paleogenetic research, focusing on ancient human DNA analysis and population genetics. Dr. Zavala's research interests span ancient DNA analysis, paleogenetics, forensic genetics, human evolution, population genetics, archaeogenetics, and anthropological genetics. Her work demonstrates a consistent focus on understanding human evolutionary history through genetic analysis, with particular emphasis on migration patterns, adaptation to diverse environments, and the development of methodological approaches for analyzing degraded DNA samples. She has made significant contributions to understanding Neanderthal admixture timing and early human dispersal into Europe. Her publication record shows a strong trend toward high-impact interdisciplinary research, with numerous publications in Nature and other top-tier journals. Her work frequently involves international collaborations across multiple institutions, reflecting the global nature of paleogenetic research. A notable pattern in her recent publications is the integration of multiple analytical approaches (genomic, isotopic, archaeological) to reconstruct human history. Young Investigator Award (2019) Miller Postdoctoral Fellowship (2022) Peter M. Schneider ISFG Fellowship (2023) Novo Nordisk Hallas-Møller Emerging Investigator Grant (2024) Dr. Zavala has secured significant research funding including the prestigious Novo Nordisk Hallas-Møller Emerging Investigator Grant in 2024, indicating strong institutional support for her research program. Her work has garnered substantial attention with multiple publications being picked up by hundreds of news outlets and referenced across social media platforms and academic networks. As a Guest Researcher at the Globe Institute's Section for Geogenetics, Dr. Zavala collaborates with interdisciplinary teams focused on ancient DNA and human evolutionary history. Her research often involves large international collaborations, as evidenced by the extensive author lists on her publications, suggesting she works within substantial research networks dedicated to paleogenetic investigations.

Line Katrine Harder ClemmensenView profile

Associate Professor

• Machine Learning
• Statistical Modelling
• Deep Learning
• +3 more

Line Katrine Harder Clemmensen is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), affiliated with the DTU Microbes Initiative. She holds a Ph.D. from DTU's IMM (2006–2009) and previously served as Principal Data Scientist at the Maersk Group (2016–2017). Her research focuses on machine learning, statistical modeling, deep learning, and sparse methods, applied to environmental, biological, industrial, and financial domains. Notable projects include hydroacoustic modeling in aquaculture systems, AI-driven sea safety, and bio-based sustainability modeling. Her recent work addresses topics like parent-child interaction patterns in OCD, Alzheimer’s treatment via spectral flicker, and genomic studies on social trust. She supervises multiple PhD students, including those exploring Raman spectroscopy applications and contamination detection in drug products. Language skills include Danish, English, Spanish, French, and Portuguese.

Matthias Oliver WilhelmView profile

Associate Professor

• Quantum Field Theory
• Mathematical Physics
• Special Functions
• +3 more

Matthias Oliver Wilhelm is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, affiliated with the Quantum Mathematics research group. His work focuses on advanced theoretical physics topics including scattering amplitudes in gauge/gravity theories, Feynman integrals, special functions, and applications of machine learning in physics. He has contributed to groundbreaking research at the intersection of quantum field theory and mathematical physics, particularly in understanding gravitational wave phenomena and high-energy particle interactions. Research Interests: His research combines quantum field theory with algebraic geometry and computational methods, exploring topics like elliptic Feynman integrals, post-Minkowskian expansions, and machine learning-driven amplitude calculations. Recent work includes leveraging Calabi-Yau manifolds for gravity-related Feynman integrals and developing transformer-based algorithms for scattering amplitude computations. Awards: He received the Velux Grant - Villum Young Investigator in 2018, recognizing his innovative contributions to theoretical physics. Projects: Leveraging Algebraic Geometry for High-Precision Fundamental Physics (2024-2028, DFF-funded) Thermodynamics of strongly coupled Quantum Field Theory (2019-2027, private foundation-funded) Key Themes in Recent Work: His articles emphasize novel computational techniques (e.g., machine learning for integration-by-parts reduction), formal developments in scattering amplitude theory, and geometric approaches to quantum gravity problems. Notable contributions include classifying Feynman integral geometries for black-hole scattering and advancing elliptic function methodologies in perturbative QFT.

Izabela Ewa NielsenView profile

Professor

• Artificial Intelligence
• Operations Research
• Unmanned Aerial Vehicles
• +5 more

Izabela Ewa Nielsen is a Professor at Aalborg University's Department of Materials and Production under The Faculty of Engineering and Science. Her research focuses on artificial intelligence applications in operations research, unmanned aerial vehicles (UAVs), genetic algorithms, and mobile robotics. She holds a degree from Warsaw University of Technology (25 Oct 2025). Research Interests: Her work integrates AI with logistics optimization, health data analysis, and sustainable supply chains. Notable projects include the EU-funded 'Operational Reliability Management System (ORMS)' and 'UAWORLD', exploring UAVs in industrial settings. Projects & Collaborations: Leading ORMS (2016-2019) to enhance operational reliability through AI-driven solutions. Contributing to TAPAS (2010-2014), advancing robotics in factory automation. Co-developing ValuePole (2008-2011) for SME performance optimization. Advising & Grants: Supervised projects such as the EU classification methodology study (2022-2023) and contributed to over 6 major research initiatives. Her work frequently involves interdisciplinary teams and industry partnerships. Labs & Teams: Active in AI for Operations Research labs, collaborating with robotics and logistics experts. Her group focuses on real-world applications of autonomous systems in manufacturing and healthcare.

Mette NyegaardView profile

Professor

• Genetics
• Genomics
• GWAS
• +4 more

Mette Nyegaard is a Professor in the Department of Health Science and Technology at Aalborg University, within The Faculty of Medicine. Her research focuses on genetics and genomics, with over 25 years of expertise linking genetic variation to phenotypes in both rare and common complex diseases. She has a strong interest in gender medicine and women’s reproductive health, particularly leading Denmark’s contribution to the largest genome-wide association analysis of endometriosis. Nyegaard has held external positions, including Associate Professor roles at Aarhus University and Senior Scientist at Aarhus University Hospital. Education: PhD in Human Genetics, University of Southern Denmark (2003) MSc in Chemistry-Biotechnology, Aarhus University (1997) Research Interests: Nyegaard’s work spans genetics, genomics, GWAS, endometriosis, coronary artery disease, and personalized medicine. Her research emphasizes translating genetic insights into clinical applications, such as polygenic risk scores and biomarker development. She collaborates internationally, with projects addressing calmodulinopathies, cardiovascular genetics, and reproductive health. Projects & Grants: Lead PI on projects like 'Understanding the Genetic Architecture of Common Complex Diseases' and 'FEMaLe: Finding Endometriosis Using Machine Learning.' Co-Investigator in studies like 'The Deadly, the Deleterious, and the Dangerous – Decoding the Differential Pathogenicity among CALM1, CALM2, and CALM3.' Labs/Teams: Nyegaard’s work is part of interdisciplinary teams at Aalborg University and collaborating institutions, focusing on genomic medicine, endometriosis, and cardiovascular genomics. She contributes to initiatives like Neuroboost and the International Calmodulinopathy Registry.

• Supply Chain Management
• Sustainable Operations
• Reverse Logistics
• +5 more

Devika Kannan is an Associate Professor at the Department of Technology and Innovation , University of Southern Denmark, and a member of the SDU Centre for Sustainable Supply Chain Engineering . Her work focuses on integrating sustainability into supply chain practices, particularly for SMEs. Research Interests : Supply Chain Management, Reverse Logistics, Green Supply Chain, Multi-Criteria Decision-Making, Closed Loop Systems, Circular Economy, and SMEs. Recent Projects : Circular economy to minimize waste challenges in Indonesia (IndoCircularWaste) (2021-2024), SDG Tools - Toolkit for the implementation of SDGs through the Circular Economy Practices (2020-2023). Scientific Awards : Emerald Citations of Excellence for 2017 Grants : Recipient of DKK 5 million from the Carlsberg Foundation (2021) to study female entrepreneurs in the green economy. Publications highlight trends in sustainable reverse logistics, smart waste management frameworks, blockchain integration in manufacturing, and machine learning applications in healthcare and supply chain optimization. Editorial Roles : Editor for the International Journal of Advanced Operations Management and International Journal of Business Performance and Supply Chain Modelling (since 2016).

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +3 more

Ingemar Johansson Cox serves as a Professor within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His research bridges theoretical machine learning foundations with practical applications across medical data analysis, information retrieval, remote sensing, and sustainability initiatives. His research portfolio emphasizes machine learning applications in high-impact domains, particularly medical data analysis (e.g., early detection of gynecological malignancy using online search activity) and sustainability (e.g., reducing AI's carbon footprint). The Machine Learning section actively contributes to the university's SCIENCE AI Centre, focusing on both algorithmic innovation and real-world problem-solving in biological modeling and environmental monitoring. Recent publication trends reveal expanding work in quantum computing applications for biomolecular modeling, sustainable AI frameworks, and cross-cultural NLP systems. His 2024-2025 output demonstrates strong interdisciplinary collaboration, especially in medical informatics and climate-related AI research. Professor Cox operates within the Department of Computer Science's robust research ecosystem, which includes dedicated compute clusters and specialized initiatives like TreeSense for global tree resource monitoring through remote sensing and deep learning. The department's infrastructure supports large-scale machine learning projects requiring significant computational resources.

• Bioinformatics
• Protein Stability
• Molecular Biology
• +3 more

Amelie Stein is an Associate Professor at the Department of Biology, University of Copenhagen, specializing in Bioinformatics and RNA Biology. Her research focuses on protein stability, molecular mechanisms of disease variants, and computational methods for protein design. She is affiliated with the UCPH Quantum Hub, reflecting interdisciplinary interests in biological systems. Her work integrates bioinformatics tools, mutational scanning, and structural biology to understand protein degradation pathways and their relevance to human diseases such as Lynch syndrome and metabolic disorders. Key research areas include analyzing protein variants using deep learning models (e.g., SSEmb), developing web-based tools like MutationExplorer for 3D visualization, and characterizing disease-linked mutations in proteins such as Parkin and MLH1. Her publications highlight breakthroughs in rapid protein stability predictions, degon mapping, and the interplay between protein toxicity and degradation. No scientific awards are explicitly mentioned in the provided texts. Stein’s research also explores the application of computational approaches to biotechnology and therapeutic development, emphasizing translational applications of her findings. Her lab, linked to the SCARB research group (https://www1.bio.ku.dk/english/research/scarb/), focuses on structural and computational biology, with ongoing projects involving protein quality control networks and enzyme variant analysis. Collaborations span molecular biology, bioinformatics, and interdisciplinary quantum-related research through her UCPH Quantum Hub membership.

Paul PopView profile

Professor

• Embedded Systems
• Systems Engineering
• Time-Sensitive Networking
• +7 more

Paul Pop is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). He holds the position of Professor within the Embedded Systems Engineering group at DTU Compute. His academic journey includes a Ph.D. in Computer Systems from Linköping University, Sweden (2003), followed by roles as an Assistant Professor there before joining DTU in 2006. Dr. Pop's research focuses on systems engineering methodologies for embedded and cyber-physical systems, with emphasis on modeling, analysis, optimization, and real-time guarantees. His work addresses challenges in time-sensitive networking (TSN), automotive systems, industrial automation, and fog computing. Key application areas include safety-critical systems, mixed-criticality architectures, and sustainable industrial IoT solutions aligned with UN SDGs. He leads major research projects such as G3C (Green Computing & Communication Continuum), TRANSACT (Safety-Critical Cyber-Physical Systems), and AgroRobottiFleet (Agricultural Robotics). His contributions span 194 publications in journals/conferences and 31 active/completed projects, emphasizing interdisciplinary collaboration. Dr. Pop advises doctoral students in embedded systems and serves as Principal Investigator on EU-funded initiatives. His technical expertise includes TSN configuration tools (TSNConf), fog computing platforms (FORA), and microfluidic biochip design. He actively contributes to industry standards through IEEE 802.1 TSN working groups and collaborates internationally with institutions like Nordic Innovation and EU Horizon programs.

• Evolutionary Genetics
• Population Genetics
• Ancient DNA Analysis
• +4 more

Rasmus Nielsen is a Professor at the University of Copenhagen, specifically affiliated with the Globe Institute and the Section for Geogenetics. His research spans evolutionary genetics, population genetics, and computational genomics with a focus on both human and non-human species. He maintains an active research profile with numerous high-impact publications in leading scientific journals. Professor Nielsen's research interests center around evolutionary and population genetics, with particular expertise in ancient DNA analysis, statistical methods for genomic data, and phylogenetics. His work bridges computational biology with empirical data from diverse species, including humans, plants, and other organisms. He has made significant contributions to understanding human evolutionary history, population structure, and the genetic basis of adaptation. His research also extends to medical genomics, particularly in understanding the genetic architecture of complex traits and diseases. His recent publications demonstrate a strong focus on methodological development in genomic analysis, with papers appearing in top-tier journals like Nature , Science , and Nature Reviews Genetics . His work shows consistent engagement with both theoretical and applied aspects of genetics, spanning human evolutionary history, medical genomics, and plant evolutionary biology. The research output reveals a collaborative approach, with frequent co-authorship across multiple institutions and disciplines. As a Professor at the University of Copenhagen's Globe Institute, Nielsen leads research within the Section for Geogenetics, which focuses on evolutionary and population genetics using cutting-edge genomic approaches. The section maintains strong connections with both computational and empirical research groups, facilitating interdisciplinary work that spans from methodological development to application in diverse biological contexts.

• Biophysics
• Computational Biology
• Systems Biology
• +2 more

Mathias Spliid Heltberg is an Assistant Professor at the Biocomplexity section of the Niels Bohr Institute, University of Copenhagen. His research focuses on nonlinear dynamics in biological systems, particularly oscillatory mechanisms in cellular processes and medical informatics applications. Current affiliation: Niels Bohr Institute, University of Copenhagen Research areas: Biocomplexity, Systems Biology, Chronobiology His recent publications span topics like p53 dynamics in DNA damage response, signal transmission in excitable media, and medical informatics applications. Collaborations include institutions in Denmark, USA, and China. The research integrates computational modeling with experimental validation in diverse biological contexts. Key contributions include theoretical frameworks for biological oscillations and practical applications in gestational diabetes intervention and surgical outcome prediction. His work on allelic buffering and potassium ion dynamics highlights cross-disciplinary approaches combining physics and biology.

Abdulkadir CelikkanatView profile

Assistant Professor

• Computer Science
• Machine Learning
• Data Science
• +3 more

Abdulkadir Celikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design and the Data, Knowledge and Web Engineering research group. His research focuses on graph representation learning, network analysis, bioinformatics, and machine learning applications in dynamic systems. Key projects include the Villum Foundation-funded 'DarkScience: Illuminating microbial dark matter through data science,' which explores metagenomic binning and microbial ecology using advanced data science techniques. He has been recognized with the Best Paper Award (2023) for contributions to temporal graph analysis and modeling. His work spans continuous-time dynamic node representations, scalable genome profiling, and polarization detection in social networks. Celikkanat collaborates widely, contributing to interdisciplinary research at the intersection of computer science, biology, and environmental science. Recent publications highlight innovations in graph embeddings, citation network modeling, and hybrid membership latent distance models. His research addresses challenges in low-dimensional graph representations, efficient kernel methods, and integrating biological networks for protein analysis.

• Applied Probability
• Insurance Mathematics
• Finance
• +3 more

Mogens Bladt is a Professor of Applied Probability and Insurance Mathematics at the Department of Mathematical Sciences, University of Copenhagen. He has held positions since 2018 after 24 years as Principal Researcher at the National University of Mexico (1994-2018), with visiting professorships at Technical University of Denmark and University of Copenhagen since 2001. Research: Focuses on time-inhomogeneous phase-type distributions, matrix-oriented life insurance models, heavy-tailed distributions, and diffusion bridge simulation Teaching: Offers graduate/undergraduate courses in Applied Probability, Stochastic Processes, Risk Theory, and Numerical Analysis Scientific Contributions: Developed R packages for Markov jump processes, phase-type distributions, and diffusion bridges. Holds grants from Mexico and Denmark, including Danish Research Council funding (2007–2008). Supervised 5 PhD, 8 Master’s, and 13 Bachelor’s theses Organized academic workshops and served as Associate Editor for Stochastic Models since 1997