جستجوی استاد راهنمای دانشگاهی

Nicolas Mathevon is a Professor at the University of Saint-Etienne, with a focus on Animal Behavior and Bioacoustics . He currently serves as Director of Studies at Ecole Pratique des Hautes Etudes and leads the ENES Team . His research explores acoustic communication across diverse species, including crocodiles, seals, penguins, and humans, with particular emphasis on vocal recognition, signal evolution, and neurobiological underpinnings. Major collaborations include Dr. T. Aubin (CNRS), Dr. I. Charrier (marine mammals), Dr. N. Grimault (crocodilian bioacoustics), and Prof. D. Reby (human communication). He also co-edits a new book, The Voices of Nature , published by Princeton University Press. His recent work spans ecoacoustic monitoring for conservation, multi-modal communication in crocodiles, and cross-species analysis of distress signals in bonobos, chimps, and humans. Neurobiological studies include brain activation patterns in response to vocalizations and sound localization mechanisms in reptiles. Scientific Awards include senior membership in the Institut universitaire de France .

• Acoustic Communication in Animals
• Bioacoustics
• Animal Behavior
• +۷ مورد دیگر

Dr. Yves Boubenec is an Associate Professor at École Normale Supérieure (ENS)-PSL University, Paris, France. He serves as Head of the LSP Neuro Platform and Director of Studies at the Department of Cognitive Studies. Academic Rank: Associate Professor Institution: ENS-PSL Departments: Cognitive Studies (ENS), LSP Neuro Platform Email: yves.boubenec@ens.psl.eu Research Focus: Boubenec investigates neural mechanisms of auditory perception and cognition using integrated methodologies spanning single-neuron electrophysiology to large-scale neuroimaging. His work reveals how context, learning, and multisensory interactions shape sound encoding in mammalian neocortex. Primary Research Themes Context-dependent auditory encoding Perceptual attention mechanisms Task-driven neural plasticity Self-supervised learning models Population-level cortical dynamics Human/ferret auditory comparisons Publication Trends: Recent work (2024-2025) examines speech production networks, premotor auditory categorization, and algebraic structures in sound learning. Earlier studies (2018-2022) focus on population gating, hierarchical auditory coding, and self-voice mechanisms. 2025 Self-voice frequency analysis Hierarchical ferret auditory cortex mapping Temporal window constraints 2024 Premotor category hemodynamics Self-supervised sound structures Human speech cortical encoding Methodological Expertise: Combines awake ferret functional UltraSound, Neuropixels recordings, and computational modeling to analyze neural representations across spatial scales. Specializes in translating animal model findings to human auditory processes.

• Auditory Neuroscience
• Perceptual Attention
• Sound Encoding
• +۳ مورد دیگر

Jayneel Parekh is a Postdoctoral Researcher in the MLIA (Machine Learning and Artificial Intelligence) team at ISIR (Institut des Sciences et Industries du Réel), Faculty of Science, Sorbonne University, working with Prof. Matthieu Cord. His research focuses on understanding and enhancing large multimodal models, with applications across audio, visual, and multimodal domains. Parekh completed his PhD at LTCI, Telecom Paris under Prof. Florence d'Alche and Prof. Pavlo Mozharovskyi, researching neural network interpretability applied to image and audio data. He earned his undergraduate degree in Electrical Engineering from IIT Bombay, where he worked with Prof. Preeti Rao and Prof. Yi-Hsuan Yang on Speech-to-Singing conversion. His research spans neural network interpretability, audio processing, computer vision, and multimodal models, with emphasis on explainable AI. His work demonstrates a consistent trajectory from foundational audio/image interpretability methods to cutting-edge large multimodal model analysis, showing increasing complexity and impact across NeurIPS, ICML, and ICCV publications. L2I paper awarded 2nd prize for STIC Best Scientific Contribution 2023 Top Reviewer at NeurIPS 2023 Parekh actively contributes to the academic community through workshop organization (ICCV on Explainable Computer Vision, ELLIS Unconference on Robustness/Fairness/Explainability) and presentations at institutions including IIT Jodhpur, Deezer Research, and IBM Research. His collaborative network spans MPI Informatics, TU Darmstadt, TU Munich, and Télécom Paris.

• Neural Network Interpretability
• Audio Processing
• Computer Vision
• +۳ مورد دیگر

Fionn C. Bennett is a Lecturer at the University of Reims Champagne-Ardenne, affiliated with the CIRLEP research team (section 11). His academic work bridges philosophy, language sciences, and interdisciplinary humanities. He focuses on intermedial semiotics, comparative poetics, and speculative music, while contributing to environmental humanities and multilingual didactics. His research explores intersections between ancient Greek poetics, musicology, and philosophical thought, often analyzing how artistic forms encode cultural and cosmological frameworks. Recent work interrogates pre-modern models for human-environment relations, applying semiotic theories to contemporary ecological discourse. He also critiques modern language proficiency assessment norms and investigates ethnoecological glossopoiesis in early Irish contexts. Bennett has published extensively since 2000, with over 20 peer-reviewed articles in journals like Humanities , International Review of the Aesthetics and Sociology of Music , and Revue de Métaphysique et Morale . Upcoming 2019 publications address cosmopolitical ethics and interdisciplinary methodologies. His work frequently combines philosophical analysis with empirical research in musicology, linguistics, and classical studies. Despite no listed scientific awards, his prolific output and cross-disciplinary approach indicate significant academic engagement. He is part of a large research team (CIRLEP) involving 39 main researchers, 35 doctoral students, and multiple affiliated institutions. Contact: fionn.bennett@univ-reims.fr

• Intermedial Semiotics
• Comparative Poetics
• Speculative Music
• +۳ مورد دیگر

Gaël Richard is a Professor at Télécom Paris specializing in machine learning and audio signal processing. He leads the Hi! Paris center, focusing on AI and data science applications. His research emphasizes hybrid interpretable AI for sound analysis, including projects like Hi-Audio funded by a €2.5M ERC Advanced Grant (2022). Key areas include machine listening, music source separation, and speech processing. Applications span autonomous vehicle acoustics and music technology. Notable contributions include neural audio compression (QINCODEC), diffusion models for music synthesis (Diff-TONE), and source separation techniques (Inverse Drum Machine). Research & Awards Recipient of the 2022 ERC Advanced Grant for the Hi-Audio project exploring hybrid AI models that integrate domain knowledge with neural networks. This approach reduces data requirements and enhances model interpretability. Active in audio-visual scene analysis and weakly-supervised learning systems. Affiliations & Labs Executive Director of Hi! Paris, a multidisciplinary lab advancing AI and data science for societal impact. Collaborates on projects like the HI-AUDIO online platform for distributed music data collection and the MAD-EEG EEG dataset for auditory attention decoding.

• Machine Learning
• Audio Signal Processing
• Artificial Intelligence
• +۳ مورد دیگر

Mathias FINK is a Professor at ESPCI Paris on the Georges Charpak chair. His research focuses on fundamental wave physics in complex media with major applications in medical imaging, telecommunications, and geophysics. He pioneered time-reversal mirrors for wave focusing and co-founded 6 technology companies. Key Institutions: ESPCI Paris, Collège de France Research Themes: Wave physics, time-reversal techniques, matrix imaging, metasurface design His work spans multi-echo wave systems , ultrasonic therapeutic devices , and adaptive electromagnetic communication systems . Recent publications emphasize 3D matrix imaging in biological tissues and space-time interface dynamics . Scientific recognition includes: First academic elected at Collège de France (2008) Over 400 peer-reviewed publications 70+ patents and 6 start-ups Collaborations extend to Institut des Hautes Études Scientifiques , Langevin Institute , and Hong Kong University of Science and Technology . His team's volcanic imaging work with seismic noise has revolutionized subterranean mapping.

• Wave Physics
• Medical Imaging
• Telecommunications
• +۶ مورد دیگر

Thierry Artières is a University Professor at Aix-Marseille University, primarily affiliated with École Centrale Marseille (ECM), where he holds multiple leadership positions including Head of the Computer Science teaching unit, Head of the IAAA course of the Computer Science Master's degree, and Head of the IAM course of the 3rd year Computer Science option. He is a key member of the QARMA (Machine Learning) research team within the LIS (Laboratoire d'Informatique et Systèmes) and collaborates with several research institutes including the READ laboratory, Institut de Neurosciences de la Timone (INT), and the ILCB (Institute of Language, Communication and the Brain). His research interests span Machine Learning, Deep Learning, and Artificial Intelligence with applications to neuroscience, medical imaging, and computational biology. His work focuses on understanding brain representations of voice and sound, optimizing MRI acquisition through deep learning, and developing novel machine learning techniques for multi-label classification and generative modeling. He has supervised numerous PhD students including Loris Berthelot, Hamed Benazha, Malek Senoussi, Swetali Nimje, and Charly Lamothe. His recent publications reveal a strong focus on applying deep learning to neuroscience problems, particularly in understanding how the brain processes sound and voice. His work combines theoretical machine learning advances with practical applications in medical imaging and cognitive neuroscience, often through collaborations between computer science and neuroscience laboratories. His research demonstrates a consistent trend toward interdisciplinary work that bridges AI with biological and medical domains. Member of QARMA Machine Learning team at LIS Collaborator with Institut de Neurosciences de la Timone Involved with ILCB Institute (Institute of Language, Communication and the Brain) Supervisor of multiple PhD students and Master's interns Regularly posts about internship opportunities in Machine Learning and AI Professor Artières actively mentors students through PhD positions, Master's internships, and engineering student projects. He has secured funding for multiple research projects, including ANR-funded collaborations with neuroscience institutes. His lab regularly offers 5-6 month internships on cutting-edge topics in machine learning, and he has facilitated numerous research opportunities for students interested in AI and data science careers. He also contributes to understanding the French job market for AI and data science professionals.

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۶ مورد دیگر

Gaël Richard is a Professor at Télécom Paris, part of the Institut polytechnique de Paris. He leads the Signal, Statistics and Learning (S²A) research group within the LTCI laboratory and is the Executive Director of Hi! Paris, a hub for AI and data science. His primary research focuses on audio signal processing, including speech synthesis, source separation, and non-negative matrix factorization techniques. He has contributed significantly to applications in automotive and music industries, and his work has been recognized with the 2020 IMT-Académie des Sciences Grand Prix and a 2022 ERC Advanced Grant for the HI-Audio project. Education and Career: Gaël Richard transitioned from classical music to applied mathematics, earning a PhD in speech synthesis. His academic journey blends technical expertise with creative problem-solving, leading to impactful contributions in machine learning and audio technologies. Research Interests: His work spans machine learning for audio signals, including speech synthesis, sound separation, and music information retrieval. He emphasizes reproducibility, publishing tools like YAAFE and databases widely used in the field. Key Projects: The HI-Audio project explores AI-driven audio processing, while his ERC grant supports advancements in hybrid deep learning for audio. He co-supervises PhD students in areas like intelligibility in noisy environments and EEG-based source separation. Awards and Recognition: In addition to major grants and the IMT-Académie des Sciences prize, his work on source separation and audio indexing has earned international acclaim. He actively contributes to academic conferences and serves on editorial boards. Labs and Teams: He leads the S²A team at LTCI, fostering collaborations between academia and industry partners like Thales and Deezer. His research bridges theoretical advancements with practical applications in audio technology.

• Audio Signal Processing
• Speech Synthesis
• Source Separation
• +۵ مورد دیگر

Florence Levé is a full Professor in Computer Science at the University of Picardy Jules Verne within the College of Sciences. She is affiliated with multiple research teams including MIS (Modeling, Information, Systems) Amiens, SDMA Team, and Algomus Team, while associated with CRIStAL (UMR 9189). Her research focuses on the intersection of computer science, music information retrieval, and computational modeling of musical structures. Key Institutions: University of Picardy Jules Verne, CRIStAL Research Themes: Musical texture analysis, rhythm processing, human-AI co-creation in music Research Interests span computational modeling of musical structures, symbolic music processing, and interdisciplinary applications in neuroscience and digital humanities. Her recent work explores rhythmic complexity across cortical and subcortical auditory areas, texture annotation in classical compositions, and algorithmic orchestration techniques. Publications Trends show sustained focus on computational approaches to musical texture and rhythm. Key areas include symbolic music analysis (Mozart, Corelli), AI-assisted composition, and mathematical modeling of auditory processing across species. Collaborative work with researchers in neuroscience, musicology, and sound engineering reveals multidisciplinary scope. Research Networks include collaborations with: Mathieu Giraud (Computational Musicology) Fabrice Wallois (Neuroscience Applications) Christophe d'Alessandro (Organ Acoustics) Laboratory Affiliations : MIS (Modeling, Information, Systems) Amiens SDMA Team Algomus Team Associated with CRIStAL (UMR 9189 CNRS)

• Computer Science
• Music Information Retrieval
• Computational Modeling of Musical Structure
• +۳ مورد دیگر

Keith Doelling is a researcher at the Pasteur Institute, affiliated with the Auditory Cognition and Communication team. His work focuses on the neural mechanisms underlying auditory perception, speech processing, and brain plasticity, particularly in aging and hearing rehabilitation contexts. His research interests include: Cognitive Neuroscience Auditory Perception Neural Oscillations Speech Processing Temporal Prediction Brain Plasticity His recent publications reveal a strong focus on how rhythmic structures, predictability, and acoustic features shape perception and cognition. He frequently collaborates with Diane S. Lazard and Luc H. Arnal, contributing to high-impact journals such as PLoS Biology , iScience , and Journal of Neuroscience . His work integrates psychophysics, neuroimaging, and computational modeling to understand how the brain processes temporal information in speech and sound. There are no listed scientific awards or grants. He has not been mentioned as advising students or leading a lab, though his role as Structure Manager suggests organizational and technical leadership within his research unit.

• Cognitive Neuroscience
• Auditory Perception
• Neural Oscillations
• +۵ مورد دیگر

Yves Boubenec is an Associate Professor (maître de conférences) in neuroscience at the École Normale Supérieure (ENS) in Paris, where he is affiliated with the Department of Cognitive Studies and the Perceptual Systems Laboratory. His research focuses on the neural basis of auditory cognition, particularly how attention, learning, and memory interact with auditory processing at the cortical level. He teaches neurophysiology of auditory perception at ENS through the IMaLiS and Cogmaster programs. His research spans three main conceptual angles: Attention , examining how top-down attention modulates auditory processing; Learning , studying how exposure to natural sounds shapes auditory perception across multiple time scales; and Memory , investigating how exposure to sounds leaves long-term traces in auditory cortex. He employs mesoscopic-scale neural recording techniques including electrophysiological recordings of neuronal populations and large-scale functional UltraSound (fUS) neuroimaging. Boubenec's recent publications (2023-2024) reveal a strong focus on speech processing, task engagement effects on auditory cortex, evidence integration across sensory modalities, and the neural mechanisms underlying categorical perception. His work frequently involves cross-species comparisons (particularly using ferrets) and combines experimental approaches with theoretical modeling. His laboratory offers multiple internship opportunities for students at various levels (L3, M1, M2, DENS) focusing on hemogenetic imaging, cross-species comparison of rhythm perception, and learning dynamics in biological and artificial networks. Boubenec collaborates extensively with researchers including Shihab Shamma, Srdjan Ostojic, and various interdisciplinary teams across neuroscience, acoustics, and computational modeling fields. His work bridges fundamental neuroscience with applications in speech processing and artificial intelligence.

• Auditory Cognition
• Neural Basis of Auditory Processing
• Attention Mechanisms
• +۵ مورد دیگر

Dario Prandi is a Researcher at the Laboratoire des Signaux et Systèmes (L2S) within CentraleSupélec, located in Gif-sur-Yvette, France. His work bridges mathematical analysis, control theory, and neuroscience, with a focus on geometric models of vision and auditory processing. He holds a PhD from École Polytechnique (2013) and has authored numerous high-impact publications in journals like SIAM and IEEE conferences. Research interests span sub-Riemannian geometry, neural field equations, and mathematical neuroscience. Notable contributions include cortical-inspired models explaining visual illusions (e.g., MacKay and Poggendorff effects), sound reconstruction via bio-inspired geometric frameworks, and the application of control theory to image processing and neural dynamics. His work often combines advanced mathematical techniques with applications in biomedical engineering and artificial perception. Recent activities include developing homogeneous observers for cortical activity models and analyzing decay rates in nonlinear systems. He collaborates extensively with institutions like Inria and the University of Paris-Saclay, contributing to transversal research axes in energy, Industry 4.0, and health technologies. Labs/Teams: Member of L2S's research groups Modélisation et Estimation, Problèmes Inverses, and the Automatique et Systèmes department. His research aligns with interdisciplinary projects in geometric science of information and hypoelliptic diffusion processes.

• Sub-Riemannian Geometry
• Neural Fields
• Control Theory
• +۴ مورد دیگر

Brice Bathellier is a Research Director and team leader at the Institut Pasteur in Paris, where he heads the Neural Code in the Auditory System group within the Hearing Institute – Pasteur Institute Center . His research integrates advanced experimental and computational approaches to investigate auditory perception and neural coding in the mammalian brain. He is also a co-organizer of the international Pasteur Course Hearing: from mechanisms to restoration technologies (HeaR) , aimed at Master’s and PhD students, clinicians, and hearing professionals. His research interests lie at the intersection of systems neuroscience , neurophysiology , and sensory systems , with a primary focus on the auditory system. His team uses two-photon calcium imaging , multichannel electrophysiology , optogenetics , and behavioral analysis to study how sound representations are formed, manipulated, and integrated with other sensory modalities in the brain. Key projects include decoding nonlinear operations in auditory perception, generating artificial auditory percepts via optogenetic stimulation, modeling reinforcement learning in sensory discrimination, and exploring multisensory interactions between auditory, visual, tactile, and olfactory systems. The team's recent publications reveal a strong interdisciplinary trend, combining in vivo neuroscience , computational modeling , and behavioral neuroscience . Themes include neural coding of sensory identity, cortical dynamics during learning and memory, cross-modal integration, and the impact of network architecture on perception. The work spans molecular, cellular, and systems levels, with applications in understanding brain function and potential neuroprosthetic technologies. Brice Bathellier supervises several PhD students and postdoctoral researchers, indicating an active mentoring role. His team is part of broader institutional initiatives on brain connectivity and neurodegenerative diseases, suggesting involvement in collaborative, cross-functional research projects. While specific grant details are not mentioned, the scope and technical sophistication of the work imply significant research funding. The team is embedded within the Hearing Institute, a multidisciplinary center focused on progressive sensory deficits, pathophysiology, and therapy.

• Systems Neuroscience
• Neurophysiology
• Sensory Systems
• +۹ مورد دیگر

Caio Corro is a Maître de Conférences (Associate Professor) at INSA Rennes, conducting research in the LinkMedia team of the IRISA laboratory. His work bridges theoretical computer science with practical applications in natural language understanding, with particular expertise in structured prediction problems for NLP systems. Dr. Corro's research program spans multiple interconnected domains: Natural Language Processing, with special focus on named entity recognition (including nested and discontinuous entities) Machine Learning algorithms for linguistic structure modeling Combinatorial optimization approaches to NLP challenges Application of AI techniques to linguistic research (AI for science) Development of efficient algorithms balancing theoretical soundness with practical performance His recent publication trajectory shows increasing emphasis on domain adaptation and multilingual capabilities while maintaining rigorous theoretical foundations. Corro has pioneered approaches that reduce computational complexity in structured prediction tasks without sacrificing accuracy, with significant contributions to named entity recognition methodologies. His work on EuroBERT demonstrates commitment to European language technologies, while his insurance domain research shows practical application focus. Dr. Corro maintains an active supervision portfolio: Current PhD students: Ayoub Hammal (co-supervised with Pierre Zweigenbaum and Miguel Couceiro), Benno Uthayasooriyar (co-supervised with Franck Vermet and Antoine Ly) Former students include Alban Petit (PhD graduate), Nicolas Devatine (now PhD student at IRIT), and several master's students who have contributed to his research program As a core member of the LinkMedia team at IRISA, Dr. Corro collaborates across institutional boundaries on cutting-edge NLP challenges, with particular emphasis on adapting techniques to specialized domains where data constraints present unique challenges.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

François Ribac is a Professor at the Faculty of Letters and Philosophy, University of Burgundy. His research explores the intersection of popular music, performing arts, technology history, and cultural prescription with a strong focus on Anthropocene impacts. He holds a Habilitation (HDR) from Sorbonne University and has authored significant works including the book Performing Arts and Music in the Anthropocene (2024). Research interests span cultural policy analysis, digital transformations in music, and ecological dimensions of artistic practice. Key themes include: Cultural democratization and institutional critique Technology-mediated performance practices Ecomusicology and sustainable arts Historical epistemology of sound His publications demonstrate consistent engagement with Anthropocene challenges through interdisciplinary lenses, particularly examining how digital platforms reconfigure music consumption and how performance arts respond to ecological crises. Recent work critically assesses cultural management models and sustainability frameworks.

• Popular Music
• Performing Arts
• Comparative History of Science and Technology
• +۲ مورد دیگر