Carlos M. Lima Azevedo is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the Transport Division, Intelligent Transport Systems Section within the Department of Technology, Management and Economics. He also serves as a Research Affiliate at MIT's ITSLab. His research focuses on mathematical modeling of human mobility, smart mobility services, and integrated transportation technologies. He has held roles including Research Scientist at MIT's ITSLab and Executive Director of MIT's Transportation Education Committee. Education includes a PhD from MIT (2014) and an MSc from LNEC (2008). Key projects include SimMobility (a simulation platform) and Tripod (sustainable travel incentives). Teaching includes courses on transportation systems and data analysis at MIT. His research interests span traffic simulation, safety analysis, and agent-based modeling. Recent work emphasizes equity in shared mobility, AI-driven traffic control, and urban sustainability. He collaborates on projects like Mobility of the Future and FMOD (Flexible Mobility On-Demand). His contributions include frameworks for automated mobility-on-demand systems and policy-sensitive models for transportation networks.
Dr. Shuting Li is a Postdoctoral Researcher at Aalborg University's Faculty of Engineering and Science within the Applied Power Electronic Systems group. Her research focuses on advanced control strategies for renewable energy integration, particularly in microgrid applications and virtual synchronous generator technologies. Her research interests span Microgrids , Virtual Synchronous Generators , Harmonics Control , and Wind Power Forecasting . She develops innovative control architectures addressing harmonic mitigation, stability enhancement, and energy management in systems with high renewable penetration. Her work bridges theoretical control design with practical implementation challenges in modern power systems. Dr. Li's publication record demonstrates consistent output with 15 research items between 2020-2024, including 7 publications in 2024 alone. Her work shows strong focus on harmonic control ( 46% fingerprint match ), virtual synchronous generators ( 46% ), and energy management systems ( 46% ), with significant contributions to wind power integration ( 30% ) and regenerative braking applications ( 30% ). She has secured research funding through the Harmonic Control Architectures for Virtual Synchronous Generator-based Distributed Generation Systems project (2021-2024), supervised by Professors Guerrero and Vasquez. This PhD project established her expertise in harmonic mitigation for distributed generation systems. Her laboratory work centers on the Applied Power Electronic Systems facility at Pontoppidanstræde 111, where she develops and tests control algorithms for microgrid applications using advanced simulation and hardware-in-loop platforms.
Oke Gerke is a Professor in Clinical Biostatistics in Diagnostic Research at the Department of Clinical Research, University of Southern Denmark, and a Biostatistician at the Department of Nuclear Medicine, Odense University Hospital. He is affiliated with the Research Unit of Clinical Physiology and Nuclear Medicine in Odense and holds a DMSc from the Faculty of Health Sciences at SDU. MSc in Mathematics and Economics, University of Hamburg (1998) PhD in Statistics and Econometrics, University of Hamburg (2001) DMSc, Faculty of Health Sciences, University of Southern Denmark (2024) Lecturer Training Programme, University of Southern Denmark (2010) His research centers on the methodological foundations of diagnostic and prognostic trials in molecular imaging. He specializes in adaptive and sequential trial designs, Bland-Altman agreement analysis, ROC curve methodology, and network meta-analysis of diagnostic accuracy studies. His work bridges biostatistics, clinical epidemiology, and nuclear medicine, with applications in oncology, cardiology, and public health. He has contributed extensively to improving reporting standards in diagnostic research and statistical methodology in clinical trials. The recent articles highlight a strong trend toward methodological innovation in diagnostic research, with a focus on adaptive and seamless trial designs, real-time evaluation frameworks during outbreaks, and advanced statistical techniques for agreement and cutpoint analysis. His clinical work integrates nuclear imaging modalities like PET/CT in cancer and cardiovascular disease, supported by rigorous meta-analytic and biostatistical approaches. He is a member of the following scientific societies: International Biometric Society (IBS) International Society for Clinical Biostatistics (ISCB) Danish Society for Theoretical Statistics (DSTS) Oke Gerke has supervised 1 PhD as main supervisor and 22 as co-supervisor, with 10 completed master’s theses under his main supervision and 4 ongoing PhD projects as co-supervisor. He has been involved in research projects such as the Neurobiological effects of work-related adjustment disorder, contributing to both statistical design and analysis. While no specific grants are listed, his extensive publication record and collaborative research indicate active grant-supported work. He frequently participates in workshops, seminars, and conferences, delivering guest lectures on topics such as network meta-analysis and diagnostic test evaluation. He is actively involved in academic and clinical research teams at SDU and OUH, particularly within the Research Unit of Clinical Physiology and Nuclear Medicine. His collaborative network spans multiple disciplines, including cardiology, oncology, and psychiatric research, reflecting a multidisciplinary approach to clinical biostatistics.
Thomas Nordahl Petersen is an Associate Professor at the Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark (DTU). His research focuses on antimicrobial resistance, metagenomics, and genomic epidemiology, with significant contributions to public health and food safety surveillance systems. Research Interests: Genomic Epidemiology of foodborne pathogens Antimicrobial Resistance (AMR) and the resistome Metagenomic analysis of environmental and clinical samples Mobile genetic elements in AMR transmission Wastewater and livestock-based surveillance Bioinformatics pipeline development for AMR gene detection His recent work involves large-scale metagenomic studies of sewage, livestock, and global AMR spread, leveraging next-generation sequencing for real-time disease monitoring. He leads and supervises multiple PhD projects related to aquaculture, foodborne infections, and resistome dynamics. Scientific Contributions and Trends: Development of ARGprofiler for AMR gene and flanking region analysis Creation of comprehensive datasets like PanRes and MetalResistance Longitudinal studies of AMR in Danish swine production Investigation of co-selection mechanisms in resistomes Application of metagenomics in public health early warning systems Advising and Research Leadership: Main supervisor for PhD projects on global AMR spread and mobile elements Co-supervisor for projects on NGS in foodborne infections and aquaculture monitoring Examiner and collaborator on industrial PhDs in probiotic genomics Active involvement in large consortia such as the EFFORT consortium Laboratories and Collaborations: Research Group for Genomic Epidemiology at DTU Food Collaborations with national and international public health institutions Integration of computational and experimental approaches in AMR research Strong focus on One Health perspectives linking human, animal, and environmental health
Raphaël Emile Gilbert Mounet is a Postdoctoral Researcher at the Department of Civil and Mechanical Engineering, Technical University of Denmark (DTU), specifically within the Section of Fluid Mechanics, Coastal and Maritime Engineering at DTU Construct. He works under the supervision of Associate Professor Ulrik D. Nielsen and is actively contributing to advanced research in ocean wave estimation and maritime data fusion. Education: PhD in Maritime Engineering, DTU (2020–2023) PhD in Marine Technology, Norwegian University of Science and Technology (2020–2023) MSc in Mechanical Engineering, DTU (2018–2020) Engineering Degree in General Engineering, École centrale de Lyon (2016–2020) His research focuses on the estimation and prediction of ocean waves by fusing data from various sources, particularly using ships as mobile wave buoys. He is the main developer of NetSSE, an open-source Python package for network-based sea state estimation, which integrates data from ships, buoys, and other platforms. His work applies signal processing, machine learning, and hydrodynamic modeling to enhance maritime safety and ocean monitoring. The 15 most recent publications reflect a strong trend in data-driven modeling, spectral analysis, and real-world applications in sea state estimation, with increasing integration of machine learning and autonomous systems. Scientific Awards: Best Paper Presented by a Young Researcher Award (First Classified), 2024 IEEE International Workshop on Metrology for the Sea Mounet is actively involved in research supervision, peer review for journals such as Marine Structures and Signal Processing , and delivers guest lectures at external institutions. He is a Co-Principal Investigator in the ongoing WEFOSWAB project on wave estimation using ships as buoys. He supervises multiple student projects and contributes to the development of next-generation ocean observation technologies. His expertise supports UN Sustainable Development Goals related to climate action and life below water through improved ocean monitoring systems. Labs and Teams: He is part of the research team at DTU Construct, focusing on fluid mechanics and maritime engineering, and collaborates internationally, particularly with institutions in Norway and Japan. His work is closely tied to the development and application of the NetSSE platform, which serves as a central tool for collaborative research in sea state estimation.
Sarah Renée Ruepp is an Associate Professor in the Department of Electrical and Photonics Engineering at the Technical University of Denmark (DTU) , specializing in Networks Technology and Service Platforms. Her research focuses on IoT, 5G communication, mobile networks, and energy-efficient systems. Current projects include deterministic 5G communication, IoT security, and time-sensitive networking. Active in supervising PhD students in telecommunications and machine learning applications. Her work intersects with scientific collaborations across Europe in domains like: Cloud Computing Carrier Ethernet Open RAN Adoption She has contributed to 175 total publications and 20 projects, with recent articles analyzing 5G readiness and energy minimization in data centers. Supervision roles include advising on critical IoT applications and teleoperated driving systems.
Luka Vujeva is a Research Fellow at the University of Copenhagen , affiliated with the Theoretical High Energy, Astroparticle and Gravitational Physics research group. His work focuses on gravitational wave lensing and high-energy astrophysical phenomena. His research interests include: Gravitational Wave Propagation Strong and Microlensing Effects Wave Optics in General Relativity Multi-Messenger Astronomy High-Redshift Galaxy Surveys Interstellar Medium Dynamics Recent publications highlight trends in gravitational lensing of transient phenomena, detector network optimization for lensed signals, and cosmological applications of wave optics. Collaborations span international teams in gravitational wave astronomy and astrophysics.
Dr. Viswanath Venkatesh is a Professor and Verizon Chair of Business Information Technology at the Pamplin College of Business, Virginia Tech , with affiliate faculty status at Virginia Tech India . He has directed the part-time Executive PhD Program and led curriculum redesigns in Information Systems programs at multiple institutions. PhD from University of Minnesota Over $10M research funding from NSF, DOT, and others Collaborations across 11+ countries in technology diffusion studies His research spans technology adoption theory (original work on TAM/UTAUT), enterprise system impacts , healthcare IT , and digital poverty alleviation . Recent work examines deviant technology use , AI adoption , and cyberdeviance through mixed-method designs integrating qualitative and quantitative approaches. Key article trends reveal: Focus on affordance theory and polynomial modeling Integration of machine learning with behavioral analytics Global scope across China, India, Malaysia, and US Methodological innovation in multimethod research and longitudinal field studies His students lead research in: Blockchain for financial inclusion (India) Agile software development impacts (2015-2021) Cross-cultural privacy perceptions (Malaysia field studies)
Tine Ravn is a Senior Researcher at the Department of Political Science, Aarhus University, specializing in science, technology, and innovation (STI) studies. Her work explores the ethical, political, and social dimensions of emerging biotechnologies, research integrity, and trust in science. Primary Affiliation: Danish Centre for Studies in Research and Research Policy, Department of Political Science, Aarhus University Research Interests: Intersections of science and society Ethical implications of biotechnology Public engagement and trust in science Science governance and policy Responsible Research and Innovation (RRI) Key Projects: BioMedical Design (2024-2028): Focuses on organoid-based technologies and ethical frameworks POIESIS (2022-2025): Examines sociotechnical transformations HYBRIDA (2021-2024): Develops ethical dimensions for organoid research SOPs4RI (2019-2022): Establishes standard operating procedures for research integrity NewHoRRIzon (2017-2021): Promotes responsible research in Europe
Adrián Avelino Sousa-Poza is a Researcher at the Department of Computer Science , University of Copenhagen, specializing in Machine Learning and its applications across diverse domains including Artificial Intelligence , Medical Data Analysis , and Quantum Computing . His research intersects with the SCIENCE AI Centre , focusing on both theoretical and applied aspects of machine learning. Recent work explores environmentally sustainable AI , quantum-enhanced models , and cross-cultural adaptation systems , reflecting his interdisciplinary approach. Key themes in his publications include large language models , quantum computing applications , and healthcare informatics , with a particular emphasis on interpretability , fairness , and hardware optimization in AI systems.
Simon Krogh Anderson serves as a Lecturer at the Department of Computer Science (DIKU), Faculty of Science, University of Copenhagen. He is an active member of the Machine Learning section which focuses on theoretical foundations and applications across domains including natural language processing, medical image analysis, and biological data modeling. The department participates in the SCIENCE AI Centre and maintains powerful compute resources including the TreeSense platform for remote sensing. His research spans multiple cutting-edge areas in artificial intelligence with particular emphasis on machine learning, quantum computing applications, and algorithmic fairness. Key interests include sustainable AI development, reproducibility in recommender systems, and cross-cultural adaptation frameworks. His work often bridges theoretical computer science with practical applications in environmental monitoring, healthcare analytics, and quantum information processing. Recent publications demonstrate strong interdisciplinary connections across quantum computing, sustainable AI, and fairness metrics. Trends show increasing focus on environmentally conscious AI development, integration of quantum methods with classical machine learning, and ethical considerations in recommendation systems. His work frequently leverages Denmark's extensive health registries and environmental data resources. Anderson actively contributes to the department's research ecosystem through teaching and collaboration within the Machine Learning section. While specific grants aren't detailed in available materials, his publications indicate involvement in projects related to quantum computing infrastructure, environmental monitoring systems, and AI ethics frameworks. The department provides significant computational resources including a dedicated cluster and specialized labs like TreeSense for remote sensing applications. His work appears connected to the SCIENCE AI Centre's initiatives in sustainable computing and quantum information processing.
Dongyu Gao serves as an Instructor in the Machine Learning section at the Department of Computer Science (DIKU), University of Copenhagen. His position places him within one of Scandinavia's leading computer science departments, which hosts the SCIENCE AI Centre and maintains strong connections with both theoretical and applied machine learning research. Dr. Gao's research interests center around machine learning with applications spanning information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. His work appears to bridge theoretical foundations with practical implementations, as evidenced by publications addressing quantum computing applications, environmentally sustainable AI practices, and advanced neural network architectures. The Machine Learning section at DIKU provides substantial computational resources including a powerful dedicated cluster and specialized initiatives like TreeSense for remote sensing applications. Analysis of recent publications associated with Dr. Gao reveals a diverse research portfolio spanning multiple cutting-edge AI domains. His work demonstrates particular strength in quantum machine learning applications, sustainable computing practices, and interpretable AI systems. The publications show a consistent pattern of interdisciplinary collaboration, connecting computer science with healthcare, environmental science, and quantum physics. Notably, several publications address the critical challenge of making AI systems more environmentally sustainable without sacrificing performance. The Machine Learning section operates within DIKU's broader research ecosystem, which includes strong connections to the SCIENCE AI Centre. This environment provides access to substantial computational resources and fosters collaboration across various AI subfields including natural language processing, computer vision, and theoretical machine learning. The department's location in Copenhagen positions it at the intersection of European AI research initiatives with strong connections to both academic and industry partners across the continent.
Lucas Alexander Kock is an Instructor at the Department of Computer Science , University of Copenhagen . His research spans Machine Learning and its applications in diverse domains including medical data analysis, quantum computing, and sustainable AI. Role: Lecturer in Machine Learning Affiliation: SCIENCE AI Centre, University of Copenhagen Research interests focus on: Quantum machine learning Neuroscience applications Cross-cultural AI systems Environmental sustainability in computing Medical informatics Deep learning explainability Recent publications demonstrate expertise in quantum computing applications , neural signal interpretation , and ethical AI frameworks . No formal awards or advisees are listed in available public data.
Jeppe Fræhr Linderød works as a Lecturer at the Department of Computer Science , University of Copenhagen. His research aligns with the department's Machine Learning section, focusing on theoretical foundations and applications in information retrieval, medical data analysis, remote sensing, and sustainability. He is part of the interdisciplinary SCIENCE AI Centre . His recent publications span diverse subfields including: Quantum machine learning and optical computing Explainable AI and feature attribution Large language models for emotion recognition Medical informatics applications Fairness in recommender systems Green/sustainable AI practices He contributes to the department's computational infrastructure, including access to a powerful compute cluster. His work often intersects with environmental and healthcare domains, particularly through projects like the TreeSense center for remote sensing applications.
Tobias Nordholm-Højskov is an Instructor at the Department of Computer Science , University of Copenhagen (DIKU). His research intersects machine learning with healthcare, sustainability, and quantum computing, focusing on theoretical foundations and applications in medical data analysis, climate-aware AI, and quantum systems. He is affiliated with the SCIENCE AI Centre and contributes to projects like QDarts (quantum dot array simulation) and TreeSense (remote sensing for environmental monitoring). His work spans diverse subfields, including Explainable AI for healthcare records Federated Learning in rare disease research Quantum-inspired neural networks Retrieval-Augmented Generation frameworks Environmental impact mitigation in AI
