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Jørgen Arendt Jensen is a Professor of Biomedical Signal Processing at the Technical University of Denmark (DTU), with dual affiliations in the Department of Health Technology and the Department of Electrical Engineering (DTU Elektro). He leads the Center for Fast Ultrasound Imaging (CFU), a collaborative initiative involving DTU, BK Medical, Rigshospitalet, and DTU Nanotech. His research focuses on advanced medical ultrasound technologies, including synthetic aperture imaging, vector flow imaging, and ultrasound simulation, aiming to improve clinical image acquisition efficiency and accuracy. He teaches medical imaging courses and co-initiated the joint biomedical engineering program between DTU and the University of Copenhagen. Jensen’s work contributes to UN Sustainable Development Goals related to health and innovation. His research interests span algorithm development for fast ultrasound imaging, blood velocity characterization, and simulation of ultrasound systems. He supervises multiple PhD students and collaborates on projects involving transducer design, real-time imaging systems, and microvascular pathology analysis. Recent publications emphasize advancements in super-resolution ultrasound imaging, transducer optimization, and pressure gradient estimation. His lab, CFU, develops cutting-edge imaging solutions for clinical applications. Jensen’s contributions include patents on ultrasound imaging techniques and collaborative ventures to enhance diagnostic capabilities through interdisciplinary engineering.

• Medical Ultrasound
• Biomedical Signal Processing
• Digital Signal Processing
• +4 more

Claus Hélix-Nielsen is a Professor and Head of Department at the Department of Environmental and Resource Engineering, DTU Sustain, Technical University of Denmark. His research focuses on lipid-protein interactions, biomimetic membranes, membrane transport, and membrane channel proteins, utilizing electrophysiology, fluorescence spectroscopy, and computational modeling. Current Position: Professor and Head of Department Institution: Technical University of Denmark Research Unit: DTU Sustain Department: Environmental and Resource Engineering Research Interests: Dr. Hélix-Nielsen investigates how the hydrophobic coupling between transmembrane proteins and lipid bilayers regulates protein function through bilayer mechanical properties. His work also explores how these properties influence membrane dynamics, including vesiculation. He develops biomimetic membranes for biosensor and separation technologies, employing advanced techniques like Raman spectroscopy and molecular dynamics simulations. Recent Publications Trends: His 15 most recent articles emphasize biomimetic membrane engineering, membrane biophysics, and applications in environmental and biomedical fields. Topics range from artificial ion channels and drug delivery systems to computational modeling of lipid-protein interactions and environmental monitoring sensors. Techniques: Key methodologies include electrophysiology, fluorescence spectroscopy, electron paramagnetic resonance, Raman spectroscopy, and molecular dynamics simulations.

• Lipid-protein interactions
• Biomimetic membranes
• Membrane transport
• +3 more

John Bagterp Jørgensen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His research focuses on computational methods for Model Predictive Control (MPC), numerical optimization, and dynamic optimization, with applications in industrial processes, biomedical systems, and sustainable energy. He holds leadership roles in 2-control ApS, a company developing advanced control solutions for industries such as cement production and oil recovery. Education: PhD and M.Sc. in Technical Sciences from DTU (1997–2005 and 1991–1997). Professional experience includes roles as an Assistant Professor at DTU and CTO/CEO at 2-control ApS. Research interests span MPC algorithms, numerical methods for differential equations, and system identification. His work bridges academia and industry, addressing challenges in energy efficiency, vaccine manufacturing, diabetes treatment, and cement production processes. His recent articles emphasize industrial applications of control systems, including cement rotary kiln dynamics, vaccine production optimization, and dual-hormone artificial pancreas development. He has received the Nordic Energy Research Award (1994) and contributed to UN Sustainable Development Goals related to affordable energy and industrial innovation. Advising and grants: Supervises multiple PhD projects on topics like electrification of industrial processes and sustainable SCP production. Collaborates with global institutions on energy and biomedical research. Labs/teams: Leads teams in DTU’s Scientific Computing and Center for Energy Resources Engineering, with active partnerships in industry and academia.

• Model Predictive Control
• Numerical Optimization
• Process Control
• +5 more

Kunihiko Kaneko is a Professor at the Niels Bohr Institute, University of Copenhagen, with a distinguished career in theoretical biophysics and complex systems. He received his PhD and MSc in Physics from the University of Tokyo, and has held leadership roles at the Universal Biology Institute and Center for Complex Systems Biology. PhD Physics, 1984 - University of Tokyo MSc Physics, 1981 - University of Tokyo His research spans five primary areas: Universal Biology, Evolutionary Constraints, Ecosystem Dynamics, Neural Cognition, and Universal Anthropology. He has published extensively on multi-level consistency principles, dimensional reduction in biological systems, and reciprocity between robustness and plasticity across scales. Recent publications show strong focus on microbial ecosystems (2025), evolutionary game theory (2025), neural modular architectures (2024), and dimensional reduction in cellular systems (2024). His work bridges physics and biology through dynamical systems theory applied to diverse phenomena from protocells to human societies.

• Theoretical Biophysics
• Complex Systems
• Chaos
• +6 more

Christian Igel is a Professor at the Department of Computer Science, University of Copenhagen, and serves as director of the SCIENCE AI Centre . He is also a co-lead of the Pioneer Centre for Artificial Intelligence in Denmark. His academic journey includes a Doctoral degree from Bielefeld University (2002) and a Habilitation degree from Ruhr-University Bochum (2010). Igel is a Juniorprofessor (2002–2010) and has held editorial roles at journals like KI - Künstliche Intelligenz and Artificial Intelligence Journal . Doctoral degree: Faculty of Technology, Bielefeld University, Germany (2002) Habilitation degree: Department of Electrical Engineering and Information Sciences, Ruhr-University Bochum, Germany (2010) His research spans Machine Learning , focusing on Support Vector Machines , Evolution Strategies , Reinforcement Learning , Deep Neural Networks , and PAC-Bayesian Analysis . He applies these methods to Environmental Monitoring , Medical Diagnostics , and Climate Research . Recent publications highlight work on adversarial machine learning , environmentally sustainable AI , and tree resource mapping using deep learning. His scientific awards include being a ELLIS Fellow . Igel’s software tools like Shark , woody , and Multi-Planar UNet are widely used in research and industry. Notable grants and collaborations involve projects with European Lab for Learning and Intelligent Systems (ELLIS) , SCIENCE AI Centre , and international teams in Denmark , Germany , and France . His lab leadership emphasizes open-source frameworks and reproducible research. Editorial Roles: German Journal on Artificial Intelligence , Evolutionary Computation Journal , Artificial Intelligence Journal Software Projects: Shark , woody , Multi-Planar UNet , U-Time Collaborations: SCIENCE AI Centre , Pioneer Centre for Artificial Intelligence , European Lab for Learning and Intelligent Systems

• Machine Learning
• Pattern Recognition
• Classification
• +6 more

Professor David E. Gloriam is a leading expert in G protein-coupled receptors (GPCRs) at the University of Copenhagen , Department of Drug Design and Pharmacology. Recognized as a top 1% Clarivate Highly Cited Researcher, he leads GPCRdb, a major database with >50,000 annual users, and develops computational tools like GPCRgraphs for drug discovery. His innovation roles include Senior Scientific Expert at Kvantify A/S and applications in pharmaceutical industry tools with patent citations. Education: Ph.D. in Medicine (Uppsala University, 2006), M.Sc. in Pharmaceutical Sciences (Uppsala University, 2003) Leadership: Head of GPCRdb (2014–), EU COST Actions member (2014–17), and institutional leadership roles in Pharmaceutical Data Science unit and Research Leadership Forum Research Interests: His work spans computational modeling of GPCR dynamics, virtual screening methods for inaccessible receptors, pharmacogenomics (PGxDB platform), and biased signaling for safer drugs. He integrates structural biology, data science, and bioinformatics to advance pharmaceutical discovery. Awards: Clarivate Highly Cited Researcher (2022) IUPHAR Analytical Pharmacology Award (2023) Lars Arge Prize for Big Data (2021) UCPH Forward Talent Program (2019) ERC Starting Grant (2014) Teaching & Supervision: Teaches Molecular Pharmacology and AI in Drug Discovery , and supervises 3 current PhD students. He has mentored 13 PhDs and 14 Postdocs, with former members attaining tenured academic or industry roles.

• G Protein-Coupled Receptors (GPCRs)
• Computational Drug Discovery
• Biostructural Research
• +6 more

Kantaro Fujiwara serves as Associate Professor at the Graduate School of Medicine, The University of Tokyo, with concurrent appointments at the International Research Center for Neurointelligence (IRCN) and the Department of Mathematical Informatics, Graduate School of Information Science and Technology. He also manages the Data Science Core infrastructure for IRCN. His academic background includes a Ph.D. in Information Science and Technology from the University of Tokyo (2008), followed by postdoctoral research at the University of Tokyo (JSPS) and University of Cambridge, then assistant professorships at Saitama University and Tokyo University of Science before joining the University of Tokyo faculty. Dr. Fujiwara's research bridges computational neuroscience and neural data analysis through mathematical modeling of neural networks, development of neural data analysis methodologies, and exploration of brain-inspired machine learning. His work extends to biological information processing with specific applications in pancreatic beta cell modeling for diabetes research, establishing connections between theoretical frameworks and experimental neuroscience. His publication record (2017-2023) reveals consistent interdisciplinary contributions applying echo state networks, recurrence analysis, and nonlinear dynamics to neural data classification, physiological signal processing, and disease modeling. These works demonstrate strong integration of computer science, neuroscience, and biomedical engineering methodologies to solve complex neurobiological problems. As Data Science Core Manager at IRCN, he oversees computational infrastructure and software resources that enable advanced neurointelligence research across the University of Tokyo ecosystem, providing critical support for data-intensive neuroscience projects.

• Computational Neuroscience
• Neural Data Analysis
• Machine Learning
• +1 more

Sneha Das is an Assistant Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), specializing in Speech and Language Technology, Machine Learning, and Privacy-Preserving AI. Her research bridges technical innovation with applications in mental health and physiological signal analysis. Her work focuses on Speech Emotion Recognition , Distributed Speech Processing , and Explainable AI , with recent publications exploring model interpretability, speaker anonymization, and physiological data analysis for emotion detection. She actively supervises PhD students in projects involving AI for mental health and hydroacoustic modeling of fish behavior. Key Research Areas: Speech Emotion Recognition (SER) Privacy and Fairness in Speech Processing Transfer Learning with Physiological Time Series AI Applications in Health and Aquaculture Notable achievements include earning a DSc (Tech) degree for her thesis on robust distributed speech processing. She also contributes to educational activities, including teaching applied statistics and R programming to PhD students.

• Speech and Language Technology
• Machine Learning
• Data Privacy
• +5 more

Susanne Ditlevsen is a Professor at the Department of Mathematical Sciences , University of Copenhagen. Her research focuses on statistical inference for stochastic processes , mathematical modeling of physiological systems , nonlinear dynamics , neuroscience , and biomathematics . Research : She develops statistical methods for diffusion processes, hidden Markov models, and stochastic differential equations, with applications in biomedical data and marine mammal behavior. Teaching : Covers basic statistics, probability, stochastic processes, regression, and generalized linear models. Publications highlight her work on climate tipping points (2023, Nature Communications ), nonlinear neuronal systems (2017), and statistical ecology (2020). Her collaborations span Denmark, France, and international institutions.

• Statistical Inference
• Stochastic Processes
• Biomathematics
• +5 more

Bo Markussen is a Professor at the University of Copenhagen within the Department of Mathematical Sciences . He is also a member of the Data Science Laboratory , where he contributes to statistical methodology and interdisciplinary collaborations. His academic journey began with a Cand.Scient (MSc) and PhD in Statistics from the University of Copenhagen, awarded in 1998 and 2002 respectively. 2012–present: Professor, Department of Mathematical Sciences, University of Copenhagen 2009–2012: Associate Professor, Department of Basic Sciences and Environment, University of Copenhagen 2006–2009: Assistant Professor, Department of Basic Sciences and Environment, University of Copenhagen Bo Markussen's research focuses on applied statistics , particularly in functional data analysis and multiple testing corrections in genetics . His work spans diverse domains including environmental science, agriculture, and public health. Recent research output highlights applications in Arctic climate data analysis, fire risk modeling, plant stress phenotyping, and nutritional biomarker prediction. His recent publications demonstrate a strong trend toward machine learning integration with statistical modeling , addressing challenges in high-dimensional data analysis and environmental risk assessment. Collaborations span institutions in Denmark and internationally, reflecting his engagement in pan-Arctic climate studies and tropical agricultural research. 2018–present: Associate Editor, Scandinavian Journal of Statistics 2017–2019: Chair, Danish Society for Theoretical Statistics 2015–2017: Board Member, Danish Society for Theoretical Statistics As a central figure in the Data Science Laboratory , Markussen leads statistical consultancy initiatives and contributes to methodological advancements. His expertise bridges theoretical statistics with real-world applications, particularly in handling complex datasets across biological and environmental domains.

• Applied Statistics
• Statistical Consultancies
• Functional Data Analysis
• +5 more

Rob Gleasure is a Professor in the Department of Digitalization at Copenhagen Business School (CBS), Denmark. His research focuses on the intersection of information systems, digital technologies, and human behavior, with particular expertise in blockchain technology, crowdfunding, AI applications, and technology adaptation. Based at Solbjerg Square 3 in Frederiksberg, he contributes to CBS's mission of advancing knowledge in business and society through digital transformation. Professor Gleasure's research spans several key areas within information systems and digital innovation: Digital finance and blockchain technologies, including cryptocurrency and financial applications Crowdfunding platforms and digital fundraising mechanisms Artificial intelligence applications in various domains including healthcare and banking Human-computer interaction and the psychological aspects of technology adoption Digital collaboration and the affective dimensions of online work Quantum computing infrastructure and emerging technologies His recent publications reveal an evolving research trajectory that increasingly addresses the societal implications of digital technologies. Gleasure has moved from foundational work on crowdfunding and blockchain to more complex examinations of AI ethics, gender bias in technological systems, and the psychological impacts of digital media. His research demonstrates growing attention to sustainable development goals, particularly those related to responsible consumption and production, reduced inequalities, and climate action. The interdisciplinary nature of his work bridges business, technology, and social sciences, often employing both qualitative and experimental methodologies. Professor Gleasure has served as a supervisor for numerous students (21 supervisor tasks mentioned) and has been active in academic service including co-chairing the ACM Collective Intelligence Conference in 2021. His research has attracted media attention, with contributions to discussions on cryptocurrency, carbon offsetting in aviation, and AI applications.

• Information Systems
• Digitalization
• Blockchain Technology
• +7 more

Raghavendra Selvan, an Assistant Professor (Tenure Track) at the University of Copenhagen, holds joint appointments in the Machine Learning Section (Department of Computer Science), Kiehn Lab (Department of Neuroscience), and the Data Science Laboratory. His academic journey includes a PhD in Medical Image Analysis (2018), MSc in Communication Engineering (2015), and BSc in Electronics and Communication Engineering (2009). PhD - Medical Image Analysis, University of Copenhagen (2018) MSc - Communication Engineering, Chalmers University (2015) BSc - Electronics and Communication Engineering, BMS Institute of Technology, India (2009) His research focuses on Bayesian Machine Learning with emphasis on Medical Image Analysis, Graph-based Learning, Tensor Networks, Approximate Inference, and Multi-Object Tracking Theory. Recent publications highlight his contributions to environmentally sustainable AI practices, efficient deep learning in medical imaging, and novel applications of tensor networks. Key research areas: Green AI and Environmental Sustainability Medical Image Analysis Graph Neural Networks Crystal Structure Prediction Model Compression Materials Science Applications

• Bayesian Machine Learning
• Medical Image Analysis
• Graph-based Learning
• +3 more

Ole Nørregaard Jensen is a Professor in Biomedical Mass Spectrometry and Systems Biology at the Department of Biochemistry and Molecular Biology, University of Southern Denmark . His research integrates advanced mass spectrometry, proteomics, and bioinformatics to study chromatin biology, post-translational modifications, and cellular signaling networks. He is actively involved in major research initiatives funded by Novo Nordisk Foundation and Lundbeck Foundation. His research interests include Mass Spectrometry, Proteomics, Posttranslational Modification, Histone Biology, Chromatin Biology, Bioinformatics, Systems Biology, Lipidomics, and Protein Chemistry . He employs cutting-edge techniques such as tandem mass spectrometry and ion mobility spectrometry to analyze protein isomers and dynamic modifications. His work has significant implications for understanding gene regulation, DNA replication, and disease mechanisms. His recent publications demonstrate a strong trend in chromatin dynamics, epigenetics, and integrated omics approaches , combining proteomics with transcriptomics and lipidomics to unravel complex biological systems. His research spans from fundamental molecular mechanisms to translational applications in biomedicine and food science. He has been recognized with several prestigious awards: MCP Lectureship Award Juan Pablo Albar Proteomics Pioneer Award 2019 EliteForsk 2009 prize Knight Order of Dannebrog (Ridder af Dannebrogordenen) Jensen is deeply involved in academic service, including peer review for journals like Nature Communications and Molecular and Cellular Proteomics , organizing conferences, and supervising students. He teaches courses such as Biomedical Mass Spectrometry - Principles and Applications and coordinates the Computational Biomedicine international Master’s program. He leads multiple active research projects, including PLATO and INTEGRA, focusing on health data, imaging, and protein networks. He is a key member of a vibrant research environment in biomedical mass spectrometry at SDU, contributing to both national and international scientific collaborations. His lab is at the forefront of developing and applying novel mass spectrometry methodologies for systems biology.

• Mass Spectrometry
• Proteomics
• Posttranslational Modification
• +9 more

Jakob Eg Larsen is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the Department of Applied Mathematics and Computer Science (DTU Compute) within the Cognitive Systems Section. He leads the Mobile Informatics and Personal Data Laboratory (MILAB). His research focuses on Human-Computer Interaction (HCI), Personal Informatics, Quantified Self, and Information Visualization, with applications in wearable technology, mental health, and healthcare. He teaches courses in Digital Media Engineering, including user experience, mobile application prototyping, and personal informatics. Education: PhD (2005) and MSc in Computer Science (1999) from the University of Copenhagen, with additional studies in cognitive psychology. Research emphasizes wearable devices for mental health interventions (e.g., PTSD treatment), physical activity tracking in pregnancy, and personalized hearing aid systems. Over 98 publications and 12 supervised PhD projects highlight his contributions to HCI, mHealth, and data-driven healthcare solutions. Labs/Teams: MILAB focuses on mobile informatics and personal data interaction. Collaborations span interdisciplinary fields like audiology, clinical psychology, and public health.

• Human Computer Interaction (HCI)
• Personal Informatics
• Quantified Self
• +3 more

Lars G. Johansen is an Associate Professor at Aarhus University, affiliated with the Department of Electrical and Computer Engineering. His work bridges interdisciplinary domains, with a focus on signal processing and machine learning. Research interests include Audio engineering and acoustic signal analysis Biomedical signal processing Neuroscience applications in Parkinson's disease studies Recent publications highlight trends in audio engineering (e.g., loudspeaker distortion analysis) and biomedical signal processing (e.g., ECG-derived respiration techniques). Collaborative work spans neuroscience, Parkinson's disease treatment evaluation, and noise reduction systems. Contact: Email: lgj@ece.au.dk Phone: +45 41 89 32 74 Labs/Teams: Signal Processing and Machine Learning Laboratory at Aarhus University.

• Signal Processing
• Machine Learning
• Audio Engineering
• +3 more