Martin Nordal Petersen is an Associate Professor at the Department of Electrical and Photonics Engineering , Technical University of Denmark (DTU) . His work spans Internet of Things (IoT) , optical networking , and wireless communication systems, with notable contributions to LoRa , NB-IoT , and LPWAN technologies. He actively supervises PhD projects on topics such as machine learning in IoT edge devices , secure 5G communication , and smart community architectures . Active projects (2024–2027): Machine Learning in IoT Edge Devices , Deterministic and Secure 5G Communication Finished projects (2021–2024; 2018–2021; 2015–2018): Reliable M2M/IoT Communication , Smart Communities , IoT 100% , Network Slicing His research explores: IoT Reliability : Multi-RAT communication, backup systems, and signal propagation Optical Networks : Alien wavelength integration, SDN control, and network emulation platforms Wireless Innovation : GPS-free geolocation, maritime NB-IoT use cases, and multimode fiber distribution Current collaborations emphasize cross-disciplinary applications of IoT in healthcare , industrial ergonomics , and smart environments .
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.
Troels Schultz Larsen is an Associate Professor in the Department of Social Sciences and Business at Roskilde University, specializing in Social Dynamics and Change. With a PhD and MSc in Public Administration, he has been with Roskilde University since 2005, progressing from PhD fellow to his current position as Associate Professor since 2014. His work focuses on urban sociology, territorial stigmatization, and the dynamics of marginalized urban areas within the Danish welfare state context. Dr. Schultz Larsen's academic journey includes: 2014-Present: Associate Professor, Department of Social Sciences and Business, Roskilde University 2009-2014: Assistant Professor/Post-Doc, Department of Society and Globalization, Roskilde University 2005-2009: PhD Fellow, Department of Society and Globalization, Roskilde University 1998-2005: MSc (Public Administration equivalent), Department of Social Science, Roskilde University Identifying his theoretical position as "Historically-Specific Praxis-Theory," Dr. Schultz Larsen draws inspiration from Bourdieu, Wacquant, Gramsci, and other critical theorists. His research spans urban sociology, territorial stigmatization, welfare state dynamics, and public innovation. He examines how marginalized urban areas are produced and maintained through intersecting social, economic, and political processes, with particular attention to the Danish context and its "ghetto list" policies. His work challenges simplistic interpretations of urban marginality, emphasizing the complex interplay between housing markets, labor markets, education systems, and family structures in reproducing spatial inequalities. Dr. Schultz Larsen's recent publications reveal a deepening focus on territorial stigmatization as both a theoretical concept and practical policy issue. His 2024-2025 work, including the book "Fragmenting Cities: The State, Territorial Stigmatization and Urban Marginality," develops a comprehensive framework for understanding how state policies actively produce spatial inequalities. He critically analyzes Denmark's controversial "ghetto list" policy, demonstrating how such classifications function as mechanisms of dehumanization and social exclusion. His research increasingly adopts comparative perspectives, connecting Danish urban marginality to broader European and global patterns of spatial inequality. Dr. Schultz Larsen's scholarly contributions include: Active participation in major research projects including EU-HORIZON24's RURALITIC Membership in the Royal Danish Academy (2025-2027) Extensive media engagement on urban policy issues With over 15 years of teaching experience at Roskilde University, Dr. Schultz Larsen has supervised numerous student projects across social sciences, politics, and public administration. His supervision spans diverse topics including urban policy, social integration, housing, labor market policies, and public innovation. He has been involved in several significant research grants, most notably as a participant in the CLIPS project (Collaborative Innovation in the Public Sector), a major multi-institutional collaboration examining innovation in public services. His current research includes the EU-HORIZON24 project RURALITIC and the BOSID project on housing stigmatization in Denmark. Dr. Schultz Larsen is actively involved in research networks focused on urban marginality and territorial stigmatization. He collaborates closely with Dr. Katrine Nørgaard Delica on multiple projects and publications, including their forthcoming book "Fragmenting Cities." His work with the North Seeland Police Department on the "TRYK Politi" initiative represents a significant practice-oriented research partnership examining citizen engagement in crime prevention. As a member of the Royal Danish Academy (2025-2027), he contributes to national scholarly discussions on urban policy and social governance.
Kim Bjerge serves as Associate Professor and Group Leader in Aarhus University's Department of Electrical and Computer Engineering, specializing in computer vision and machine learning applications for ecological monitoring. His research bridges engineering and environmental science to develop innovative solutions for insect biodiversity assessment and sustainable agriculture. His core research interests include computer vision, deep learning, and edge computing systems for real-world ecological monitoring. Dr. Bjerge develops time-lapse camera pipelines and deep learning models specifically for insect population tracking in natural environments, with emphasis on agricultural applications like black soldier fly farming and biodiversity conservation. His work integrates signal processing techniques with biological data to create field-deployable monitoring systems. Recent publications reveal a strong trend toward practical implementations of computer vision in entomology, particularly focusing on edge processing for camera traps, automated trait prediction in insect farming, and biodiversity monitoring systems. Key research areas include nocturnal insect monitoring, floral environment analysis, and developing specialized datasets like AMI for insect identification in wild settings. He leads multiple significant research projects funded through competitive grants: MAMBO: Modern Approaches to Monitoring Biodiversity (2022-2026) FLYgene: Sustainable Insect Production for Livestock Feed (2022-2026) Automatisk monitering af nataktive insekter: Automatic nocturnal insect monitoring (2024-2029) Pilotprojekt for automatisk registrering af invasive plantearter: Invasive species monitoring (2020-2021) As head of the Signal Processing and Machine Learning research group, Dr. Bjerge directs interdisciplinary teams developing computer vision solutions for biological monitoring systems. His laboratory focuses on creating robust field-deployable technologies including scanner-based arthropod imaging systems, time-lapse camera networks for floral environments, and edge AI processors for real-time insect monitoring in agricultural settings.
Ole Winther is a Professor at the Department of Biology, University of Copenhagen, specializing in Computational and RNA Biology. He also holds a joint appointment as Professor at DTU Compute, Technical University of Denmark. His research bridges machine learning, bioinformatics, and natural language processing with applications in biological sequence analysis, transcriptomics, and health informatics. Education: 1998: PhD in Physics, University of Copenhagen 1994: Master of Science in Physics, University of Copenhagen Winther's research focuses on developing advanced machine learning methodologies for biological applications. He has pioneered protein language models for sequence analysis (DeepLoc, SignalP, DeepTMHMM), interpretable deep learning for RNA subcellular localization, and benchmarking frameworks for DNA language models. His work spans latent variable models, variational inference, diffusion models, and novel architectures for deep generative modeling, with increasing emphasis on practical healthcare applications including rare disease diagnosis through findzebra.com and medical question answering with large language models. Scientific Recognition: ELLIS Fellow (2021) Head of ELLIS Copenhagen Unit H-index of 61 (Google Scholar, May 2023) 19,700+ citations (Google Scholar, May 2023) Winther has supervised 25+ PhD students to completion with 7 currently in progress, along with over 100 master's projects. He frequently serves as PhD opponent and committee chairman across European institutions. His research is supported by substantial funding including multiple Novo Nordisk Foundation grants totaling over 60 million DKK for the Center for Basic Machine Learning Research in Life Science and CAZAI projects, plus significant funding from the Danish Independent Research Fund. He leads an active research group developing cutting-edge machine learning approaches for bioinformatics and NLP challenges. Winther co-founded two spin-out companies: findzebra.com (2014, 2018), a search engine for rare diseases, and raffle.ai, an NLP startup for enterprise search. He initiated DTU's popular BSc in AI and Data program and teaches the highly enrolled MSc course in Deep Learning (450+ students) and PhD course in Bayesian Data Analysis.
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
Gyula Mate Kovács is a Research Fellow (Postdoctoral Researcher) at the Department of Geosciences and Natural Resource Management, Faculty of Science, University of Copenhagen. His research is funded by the Novo Nordisk Foundation through the Global Wetland Center. Education Ph.D. in Remote Sensing of Wetlands, University of Copenhagen (2020–2024) M.Sc. in Geography and Geoinformatics, University of Copenhagen (2017–2019) B.Sc. in Environmental Management, Birkbeck University of London (2013–2017) Research Focus Dr. Kovács specializes in AI-driven remote sensing for wetland ecosystem analysis. His work integrates machine learning, deep learning, and satellite data fusion to quantify natural/anthropogenic impacts on wetlands at global scales. Key methodologies include time series analysis, cloud computing, and convolutional neural networks for applications like carbon mapping, water body detection, and land-use impact assessment. Publication Trends His 7 recent publications demonstrate a strong focus on wetland dynamics using satellite remote sensing, with themes spanning deep learning applications (CNN U-Net algorithms), greenhouse gas emissions in croplands, continental-scale wetland inventories, and ecosystem change detection. Research consistently employs advanced AI techniques to address environmental challenges in diverse regions like the Sahel and Europe. Funding & Affiliation Supported by the Novo Nordisk Foundation via the Global Wetland Center, his work advances wetland monitoring capabilities. He collaborates with international teams on projects involving satellite data processing and ecological modeling.
Andrea Crovetto is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the National Centre for Nano Fabrication and Characterization and the Department of Nanofabrication. His research focuses on advanced materials for photovoltaic applications, including solar cell technologies, thin films, and semiconductor materials. His work contributes to UN Sustainable Development Goals related to affordable and clean energy. Key research interests include photovoltaic materials discovery, tandem solar cell design, and semiconductor characterization. Notable achievements include developing monolithic selenium/silicon tandem solar cells and pioneering studies on phosphosulfide semiconductors. He has authored over 90 publications and datasets, including high-impact articles in journals like JPhys Energy and PRX Energy . Dr. Crovetto has received the Young Scientist Award (2016) and actively supervises PhD students in projects such as Experimental Discovery of Phosphosulfide Materials for Solar Cells and Thiophosphate Thin Films for Quantum Technology . His research also involves collaborations on material synthesis, computational modeling, and device fabrication. He has presented at international conferences, including talks on monolithic tandem solar cells and materials discovery methodologies. His lab, based at DTU’s Produktionstorvet , integrates experimental and theoretical approaches to advance sustainable energy technologies.
Kiyoto Kasai is a Professor and Chair of the Department of Neuropsychiatry at the Graduate School of Medicine, The University of Tokyo. As a Principal Investigator, he leads research on biological mechanisms of psychosis onset and early intervention strategies, utilizing neuroimaging (MRI, MR spectroscopy), EEG, and cohort studies like the Tokyo TEEN Cohort. He is a key figure in Japan’s national brain project Brain/MINDS 2.0 and focuses on adolescent mental health, schizophrenia, and 22q11.2 deletion syndrome. Education: M.D. (1995), Ph.D. in Psychiatry (2004) from the University of Tokyo Key Research Areas: Neuroimaging, Clinical Neurophysiology, Adolescent Brain Development, Translational Neuroscience His work bridges biological and psychosocial research, including large-scale cohort studies, clinical epidemiology, and molecular biology. Recent publications highlight his focus on suicide prevention in adolescents, psychosis prediction, and neurochemical mechanisms in psychiatric disorders. Scientific awards include: Distinguished Investigator Award (2003) Young Investigator Award (2008) Kasai’s team collaborates internationally on projects like Brain/MINDS Beyond, aiming to uncover brain maturation mechanisms in the Adolescent and Young Adult (AYA) generation. The department also emphasizes patient-centered care through initiatives like the AYA Generation Center and specialized programs for 22q11.2 deletion syndrome.
Luka Radic is a Researcher in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His work bridges theoretical and applied research in machine learning, with a focus on quantum machine learning , large language models , and fairness in AI systems.
Emmanouil Vasilomanolakis is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). He specializes in Cybersecurity Engineering, focusing on areas such as honeypot technology, cyber deception, IoT/OT security, and network security. His research contributes to the UN Sustainable Development Goals related to innovation and infrastructure. His research interests include: Cyber deception techniques and honeypot design for detecting and mitigating cyber attacks Security of Internet of Things (IoT) and Operational Technology (OT) devices, particularly their exposure to cyber threats Analysis of botnets and cybercrime activities on darkweb markets Social engineering attacks and human aspects of cybersecurity Development of tools for vulnerability analysis and network traffic monitoring His recent publications explore advanced cyber-deception strategies, the security of exposed IoT/OT devices, and the analysis of cybercrime activities. Key areas include device identification, vulnerability detection in industrial systems, and the impact of social engineering in cybersecurity. He currently supervises several PhD students including Kasper Elzer, António Cordeiro Urbano, Aigerim Safargalieva, Davide Maddaloni, and collaborates on projects like "Advanced cyber-deception techniques" and "Defending legacy and modern networks with cyber-deception". His research is supported by grants focused on collaborative security and deception-based defenses. He is part of research teams developing honeypot technologies and cyber deception frameworks, contributing to datasets like the hybrid IoT/OT honeypot collection.
Tove Hels is an Associate Professor in the Department of the Built Environment at the Faculty of Engineering and Science, Aalborg University, Denmark. She is a key member of the Traffic Research Group, focusing on transportation safety, road user behavior, and traffic policy. Her work bridges engineering, public health, and social science to improve road safety outcomes. Research Interests: Her research spans traffic safety, cyclist-motorist interactions, speed enforcement, accident risk modeling, and the impact of vehicle technologies. She investigates both infrastructure design and behavioral factors influencing road safety, with a strong emphasis on data-driven analysis and policy evaluation. The recent publications reveal a consistent focus on improving cyclist safety through infrastructure (e.g., advanced stop boxes, roundabout design), addressing under-reporting of traffic injuries, and evaluating behavioral interventions for speeding. Her work integrates epidemiological methods, statistical modeling, and real-world policy applications. Scientific Contributions: Principal Investigator in the Danmarks Trafikulykker cohort study (2025–2029) Project participant in EASE: Intervention against speed offenders (2019–2025) Author of over 29 research outputs including journal articles, reports, and policy briefs Frequent contributor to national media and advisory bodies like Dansk Vejforening Advising and Grants: While no formal students are listed, her leadership in major funded research projects indicates a supervisory role in training junior researchers. She has secured and contributed to significant research grants related to traffic safety monitoring and intervention evaluation. Labs and Teams: She is part of the Traffic Research Group at Aalborg University, collaborating closely with researchers such as H. Lahrmann, T.K.O. Madsen, and A.V. Olesen. The group conducts field studies, data linkage projects, and policy evaluations with national impact.
Søren Eilers is a Professor at the Department of Mathematical Sciences , University of Copenhagen. His research focuses on Operator Algebras , particularly the classification of C*-algebras related to discrete and low-dimensional structures. He is a member of the FNU network 'Automorphisms and Invariants for Operator Algebras' and advocates for experimental mathematics using computational methods in pure mathematics. Education: MS in Mathematics and Computer Science, University of Copenhagen (1993) PhD in Mathematics, University of Copenhagen (1995) Research Interests: Operator Algebras K-theory Symbolic Dynamics Discrete Mathematics Experimental Mathematics Recent Publications (2016-2024) demonstrate expertise in graph C*-algebras , symbolic dynamics , and computational approaches to pure mathematics, with key collaborations in Denmark, Japan, Canada, and the U.S. Scientific Leadership: President, Danish Mathematical Society (2006-2008) Principal Investigator, Villum Fonden (2012-2016) Main Organizer, Mittag-Leffler Institute Program (2016) Advisory Roles: Supervised 28 master's theses and mentored 9 PhD students/postdocs (2003-2022) across institutions in Denmark, Canada, Japan, and the U.S.
Asger Dag Törnquist is an Associate Professor at the Department of Mathematical Sciences, University of Copenhagen . His research focuses on Descriptive Set Theory , Infinite Combinatorics , and Forcing , with applications to Operator Algebra and Ergodic Theory . He received his Ph.D. in Mathematics from the University of California at Los Angeles in 2005. Research Highlights: His work explores the Borel complexity of orbit equivalence and von Neumann equivalence, with recent contributions to the interplay between set theory and cognitive psychology models. Key topics include projective witnesses , maximal almost disjoint families , and unitarizability in Polish groups. Publications: Törnquist has published extensively in top-tier journals such as the Annals of Pure and Applied Logic , Journal of Mathematical Logic , and Proceedings of the National Academy of Sciences , often addressing foundational questions in set theory and its applications.
Johannes Bjerva is a Full Professor at Aalborg University's Department of Computer Science (Campus Copenhagen), leading the Copenhagen branch and conducting interdisciplinary NLP research integrating linguistic typology. His work focuses on low-resource languages, language model security, and societal AI impact. PhD (University of Groningen, 2017): Thesis on multitask/multilingual lexical modeling M.A. & B.A. in Computational Linguistics (Stockholm University) Research interests span linguistically-informed NLP , language model security , and low-resource language technology . Current projects include the DFF Sapere Aude grant (2025) for language model detection security and the LM2-SEC project (2025–2030). His 2024 ACL paper on embedding inversion security and 2024 EMNLP paper on typological diversity exemplify recent work. Scientific awards include: 2021: Teacher of the Year (AAU Computer Science) 2019: Google Cloud research credits 2022: Carlsberg Semper Ardens (5M DKK) 2024: Novo Nordisk Data Science grant (~10M DKK) Supervision includes 8 PhD students across projects like CreoleVal and HiFi-KPI . He serves on the Industrial Researcher Committee at Innovation Fund Denmark and is a member of Det Unge Akademi (2023–2028).
