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• Computer Vision
• Image Analysis
• Machine Learning
• +5 more

Serge Belongie is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, where he holds dual affiliations with the Pioneer AI research section and the Image Analysis, Computational Modelling, and Geometry section. His academic position places him at the forefront of interdisciplinary research connecting computer vision with language models, geospatial analysis, and cultural understanding. Professor Belongie's research program encompasses several critical domains in modern artificial intelligence: Advanced computer vision and image analysis techniques Vision-language model integration and multimodal systems 3D point cloud processing and semantic segmentation Geospatial representation learning for environmental applications Fine-grained object recognition and detection Cultural context understanding in AI systems His recent publication record reveals a sophisticated trajectory toward developing precise control mechanisms for vision-language models, with applications spanning forensic analysis, cultural heritage preservation, and social media understanding. The research demonstrates increasing sophistication in handling cultural context and enabling fine-grained manipulation of visual content through natural language interfaces. Professor Belongie maintains an active research group producing significant scholarly output, with over 280 research publications documented in his academic profile. His work is supported by research funding that enables cutting-edge exploration in multimodal AI systems with practical societal impact. He plays a key role in the Pioneer AI center at the University of Copenhagen, which focuses on advancing artificial intelligence through interdisciplinary collaboration and innovative research approaches that bridge theoretical computer science with real-world applications.

• Auditory Working Memory
• Neural Mechanisms of Imagination
• Neuroscience of Music
• +5 more

David Ricardo Quiroga Martinez is a Postdoctoral Researcher in the Department of Psychology at the University of Copenhagen's Faculty of Social Sciences. His research focuses on the neural mechanisms underlying auditory working memory and musical imagination using intracranial EEG and MEG techniques. He investigates how the brain represents and manipulates sounds during perception and imagination, with applications for understanding psychiatric disorders and brain-computer interfaces. His research interests span Auditory Working Memory , Neural Mechanisms of Imagination , Neuroscience of Music , and Reward Coding . He employs invasive and non-invasive neuroimaging to study how musical sequences are processed in the brain, examining temporal hierarchies in predictive processing from pure tones to complex songs. His work bridges cognitive neuroscience, computational modeling, and clinical applications. Key publications reveal trends in neural representation of musical thoughts (2024), asymmetric reward prediction coding (2023), and temporal hierarchies in melody processing (2023). His research consistently explores how musical structure, predictability, and expertise shape neural responses, with implications for understanding disorders involving auditory imagery abnormalities. Reintegration Fellowship from the Carlsberg Foundation: The representation of musical thoughts in neurons of the human brain International Postdoc from DFF: The neural basis of musical imagination His collaborative network spans international institutions, with significant work on congenital amusia, atonal music processing, and neural gain modulation. Current projects involve decoding imagined musical sounds and investigating reward prediction errors in cortical networks, supported by major Danish research foundations.

Alexandros GelastopoulosView profile

Research Fellow

• Mathematical Modeling
• Probability Theory
• Stochastic Processes
• +4 more

Alexandros Gelastopoulos is a Research Fellow at the Institute for Advanced Studies in Toulouse with a PhD in Mathematics from Boston University (2019). His research employs mathematical modeling to study biological and social systems, focusing on probability theory, stochastic processes, and reinforcement dynamics in social contexts like cumulative advantage and ranking-based behaviors. Primary research interests include analyzing how popularity rankings influence social systems, studying brain oscillations for memory functions, and exploring decision-making paradoxes. His work often bridges mathematical rigor with behavioral sciences to uncover mechanisms driving social inequalities and neural computations. His publication trends show strong emphasis on social dynamics (e.g., lock-in effects, reinforcement processes) and computational neuroscience (e.g., neural rhythms, memory substrates), using stochastic modeling and empirical validation across disciplines. European Commission's Seal of Excellence (2024) Collaborates extensively with international researchers on projects involving mathematical sociology and behavioral experiments. Teaches game theory and mathematical courses while developing expository materials on real analysis and information theory.