جست‌وجوی استاد و پژوهشگر

Chua Tat Sengمشاهده پروفایل

استاد

• unstructured data analytics
• multimedia information retrieval
• recommendation systems
• +۶ مورد دیگر

Chua Tat Seng is a Professor at the School of Computing, National University of Singapore (NUS), holding the KITHCT Chair Professorship since 2009. He serves as co-Director of the NExT++ Center, a joint research center between NUS and Tsinghua University focused on Extreme Search. His academic career spans over three decades at NUS, where he has held various leadership positions including Acting Dean of the School of Computing (1998-2000) and Acting Head of the Department of Information Systems & Computer Science (1996-1998). Professor Chua's research spans unstructured data analytics , multimedia information retrieval , recommendation and conversation systems , and emerging applications in e-commerce and fintech . He established the Lab for Media Search (LMS) at the School of Computing and has been instrumental in advancing multimodal learning and search technologies. His work bridges theoretical foundations with practical applications, particularly in developing trustable AI systems for real-world deployment. His recent publications demonstrate a strong focus on large language models for recommendation systems , multimodal learning , and generative AI applications . The research trends show increasing emphasis on LLM-based recommendation, multimodal understanding, and addressing fundamental challenges in AI reliability, fairness, and efficiency. His work spans theoretical advancements in representation learning to practical applications in e-commerce, finance, and healthcare domains. ACM SIGMM Technical Achievement Award 2015 Multiple Best Paper Awards across ACM Multimedia, IEEE Transactions, and MMM conferences (2007-2020) Professor Chua has supervised 37 PhD students since 2004, establishing himself as a dedicated mentor in the academic community. His research has been supported by substantial grants including NExT++ ($12 million), Base Metals Price Forecasting ($200,000), and Multilingual Multimodal Knowledge Graph ($500,000). He maintains active collaborations with Tsinghua University, University of Southampton, and industry partners like Four Elements Capital and Singapore Press Holdings. As co-Director of the NExT++ Center, he leads a major research initiative focused on Web Intelligence and User Empowerment. His visiting professorships at Tsinghua University (2017-present) and Zhejiang University (2021-present) reflect his international impact in the field of multimedia and AI research.

Manik Varmaمشاهده پروفایل

استاد مدعو

• Machine Learning
• Deep Learning
• Information Retrieval
• +۱۶ مورد دیگر

Manik Varma is a Distinguished Scientist and Vice President at Microsoft Research India, and an Adjunct Professor at the Indian Institute of Technology Delhi. He is a Fellow of the Indian Academies of Science (IASc, INSA, NASI), the Indian National Academy of Engineering (INAE), and the Association for Computing Machinery (ACM). He has received prestigious awards such as the Shanti Swarup Bhatnagar Prize and Microsoft Gold Star Award. Education : BSc in Physics from St. Stephen's College (David Raja Ram Prize) BA in Theoretical Physics from the University of Oxford (Rhodes Scholar) DPhil in Computer Vision and Machine Learning from the University of Oxford (University Scholar) Post-doctoral Fellow at the Mathematical Sciences Research Institute (MSRI), Berkeley Visiting Miller Professor at UC Berkeley His research focuses on Machine Learning (Extreme Classification, Resource-efficient ML, Supervised Learning), Information Retrieval (Computational Advertising, Dense Retrieval, Recommender Systems), and Computer Vision (Image Search, Object Recognition). Recent work includes graph-regularized encoders, label variance reduction, and multimodal classification frameworks. His publications span extreme classification algorithms like NGAME , SiameseXML , and DECAF , with applications in IoT, web search, and recommendation systems. He leads a research group at Microsoft Research India and advises PhD students at IIT Delhi. Scientific Awards : Shanti Swarup Bhatnagar Prize (Government of India) Microsoft Gold Star and Achievement Awards WSDM 2019 Best Paper Prize BuildSys 2019 Best Paper Runner-up Fellow of ACM, IASc, INSA, NASI, INAE He has supervised numerous PhD students, including Sonu Mehta and Suchith Prabhu, and collaborates with institutions like Microsoft Research India, IIT Delhi, and UC Berkeley. His research has led to scalable solutions for billion-label classification and resource-constrained IoT applications.

Adriana Tapusمشاهده پروفایل

استاد

• Robotics
• Human-Robot Interaction
• Machine Learning
• +۷ مورد دیگر

Adriana Tapus is a Full Professor at ENSTA Paris, affiliated with Institut Polytechnique de Paris, leading the Autonomous Systems and Robotics Laboratory (SAR) within the Computer Science and Systems Engineering Unit (U2IS). She holds an HDR (Habilitation) and a PhD from EPFL, Switzerland, with postdoctoral experience at USC. Her research focuses on socially assistive robotics, human-robot interaction (HRI), and personalized therapy for individuals with physical/cognitive impairments. She directs the IP Paris Doctoral School and coordinates national/international projects like the EU-funded ENRICHME and SWEET. Education: PhD in Mobile Robotics, EPFL (2005) Habilitation (HDR), ENSTA Paris (2011) M.S. Computer Science, University Joseph Fourier Engineer, Politehnica University of Bucharest Research Interests: Tapus pioneers socially assistive robotics, integrating machine learning, human modeling, and multimodal communication (verbal/non-verbal/para-verbal). Her work addresses adaptive therapies for vulnerable populations using robotics, physiological data interpretation, and context-aware interaction. Key themes include: Human-robot cooperation and trust Emotion recognition and expression Personalized rehabilitation systems AI ethics and human-centered design Publications: Over 150 articles, with recent work exploring humor in HRI, teleoperation trust models, and cross-cultural intelligent vehicles. Notable 2025 contributions include studies on robot laughter efficacy and multimodal facial expression frameworks. Awards: 2025: 4 IROS papers accepted 2016: 25 Women in Robotics recognition 2010: Romanian Academy Award Multiple conference best paper awards (RO-MAN, ICRA, etc.) Advising & Grants: Supervised over 20 PhD students and led projects like EU Horizon 2020 ENRICHME. Current students focus on teleoperation dynamics, robot humor, and haptic interfaces. Active in editorial roles (IJSR, THRI) and conference organization (HRI General Chair 2019). Labs/Teams: Founder of RoboticsByDesign lab and co-initiator of the Hi! Paris interdisciplinary AI center. The SAR lab develops systems for healthcare, education, and human-robot collaboration.

Nils Holzenbergerمشاهده پروفایل

استادیار

• Natural Language Processing
• Artificial Intelligence
• Legal AI
• +۳ مورد دیگر

Nils Holzenberger is an Assistant Professor at Télécom Paris, France, since February 2023, affiliated with the Data Intelligence Graphs (DIG) research team within the Information Processing and Communication Laboratory (LTcI). His work bridges artificial intelligence, natural language processing, and legal domains through neuro-symbolic approaches to statutory reasoning, particularly in tax law. Education: PhD in Computer Science, Johns Hopkins University (2017-2022) Master's in Engineering, Mines ParisTech (2013-2017) Preparatory Classes, Lycée Louis-le-Grand (2011-2013) Holzenberger's research centers on legal artificial intelligence with emphasis on statutory reasoning limitations in large language models. He pioneered the SARA dataset for tax law reasoning and LegalBench benchmark, developing hybrid symbolic-neural frameworks that expose LLMs' shortcomings in precise legal interpretation. His work integrates Prolog solvers with NLP techniques to create executable tax code mappings and contract analysis tools, establishing foundational methods for verifiable legal AI systems. Analysis of his 15 most recent publications (2019-2024) reveals three dominant research thrusts: (1) Tax law reasoning benchmarks exposing LLM hallucinations, (2) Neuro-symbolic integration for statutory interpretation, and (3) Low-resource template extraction for legal documents. His work consistently demonstrates that pure neural approaches fail at precise legal reasoning, necessitating symbolic grounding for reliable legal AI applications. The DIG research team at Télécom Paris, where Holzenberger leads legal AI initiatives, is actively hiring faculty for neuro-symbolic projects. While specific grant details aren't public, his collaborations with HEC Paris, Copilex startup, and featured podcast appearances indicate substantial industry-academia engagement in legal tech development. Holzenberger directs the legal AI vertical within LTcI's DIG team, focusing on data intelligence for statutory reasoning. His group develops tools for tax minimization strategy discovery, contract analysis, and legal information extraction, maintaining close ties with legal practitioners through projects like the Prolog-based tax code interpreter. The team's infrastructure supports both academic research and startup partnerships in computational law.

Chao Liuمشاهده پروفایل

پژوهشگر

• Surgical Robotics
• Teleoperation
• Haptics
• +۲ مورد دیگر

Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.

Hirokatsu Kataokaمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Artificial Intelligence
• +۵ مورد دیگر

Hirokatsu Kataoka serves as Chief Senior Researcher at the National Institute of Advanced Industrial Science and Technology (AIST) in Japan, with multiple academic affiliations including Academic Visitor at the Visual Geometry Group (VGG) at University of Oxford, Visiting Associate Professor at Keio University, and Adjunct Associate Professor at Tokyo Denki University. He is Principal Investigator of both cvpaper.challenge and LIMIT.Lab, and serves as Research Advisor for SB Intuitions. Dr. Kataoka earned his Ph.D. in Engineering from Keio University (April 2011 - March 2014), where he received the Fujiwara Prize in 2014 as valedictorian equivalent. His research primarily focuses on innovative pre-training methodologies that eliminate dependency on natural image datasets, with his Formula-Driven Supervised Learning (FDSL) framework being particularly influential in the field. Kataoka's research interests center around representation learning with limited data resources, including zero-shot, unsupervised, and synthetic learning approaches. His work explores how visual/multimodal models can be effectively trained with minimal real-world data, addressing critical ethical concerns related to large-scale datasets. He has pioneered methods using fractal geometry, mathematical formulas, and procedural generation to create effective pre-training frameworks that rival traditional ImageNet-based approaches. His publication record shows a clear trajectory toward solving the challenges of learning with limited resources, with recent work expanding FDSL to audio processing, microfossil analysis, and visible-to-infrared translation. His papers consistently address the core challenge of building robust visual recognition systems without relying on massive annotated datasets, with increasing focus on practical applications across diverse domains. Scientific Awards & Recognition ACCV 2020 Best Paper Honorable Mention Award for 'Pre-training without Natural Images' AIST Best Paper Award (2019, 2022) BMVC 2023 Best Industry Paper Finalist Featured in MIT Technology Review His 3D ResNets paper ranks among the top 0.5% most-cited CVPR papers over a five-year period Dr. Kataoka actively advises numerous researchers across multiple institutions, with his research team comprising Ph.D. and Master's students from various universities. He has served as Area Chair for CVPR 2024 and 2025, will serve as IEEE TPAMI Associate Editor beginning in 2025, and organizes the LIMIT Workshop series at major computer vision conferences. His LIMIT.Lab, established in June 2025, serves as a collaboration hub focused on building multimodal AI models under constrained resources including compute, data, and labels.

Elise Lavouéمشاهده پروفایل

استاد

• Technology Enhanced Learning
• Human-Computer Interaction
• Gamification
• +۱۲ مورد دیگر

Elise Lavoué is a full Professor in Computer Science at iaelyon School of Management, Jean Moulin Lyon 3 University, and a key researcher at the LIRIS laboratory (CNRS). She leads the SICAL research team and holds leadership roles including Editor-in-Chief of the STICEF journal, member of Labex ASLAN’s management committee, and member of the University of Lyon’s Research Ethics Evaluation Committee (CER-UdL). She is also affiliated with the ATIEF association. Her research focuses on enhancing motivation and engagement in digital learning environments through adaptive gamification, learning analytics, and human-computer interaction. She explores how tailored game elements, emotional awareness tools, and immersive technologies like virtual reality can support self-regulated learning, critical thinking, and skill development in complex digital contexts. Her recent publications span top journals such as IEEE Transactions on Learning Technologies, International Journal of Human-Computer Studies, Computers & Education, and CHI PLAY. These works reflect a strong trend in adaptive and personalized learning technologies, emotion-aware systems, and immersive training environments, particularly in educational and professional settings. Honorable Mention Award at ACM CHI PLAY 2019 (top 4%) Best Industrial Paper award at CSEDU 2020 Elise Lavoué actively supervises PhD students and post-doctoral researchers and leads multiple funded projects including LudiMoodle+, RENFORCE, Lex.gaMe, BODEGA, and Emoviz. These projects involve collaborations with institutions across France and focus on gamification, VR training, emotional dashboards, and vocabulary acquisition. She has secured funding from ANR, Labex ASLAN, CNRS, and other national bodies. Her work emphasizes interdisciplinary collaboration between computer science, education, and social sciences. She is involved in several research teams and labs, primarily the SICAL team within the LIRIS laboratory, a major interdisciplinary research unit in computer science, images, and information systems. Her projects often involve industry partners such as SpeakPlus and Woonoz, and she contributes to both scientific advancement and practical educational innovation.

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +۴ مورد دیگر

Frederic PRECIOSO is a University Professor at Côte d'Azur University, affiliated with the Sophia Antipolis Computer Science, Signals and Systems Laboratory (I3S), a joint research unit of CNRS, Inria, and the university. He is a member of the MAASAI Project Team (Joint INRIA-CNRS-UCA), based at the Fermat Building, INRIA Sophia Antipolis Méditerranée, where he conducts research in artificial intelligence and deep learning. His research interests lie at the forefront of modern AI, focusing on Deep Learning , Large Language Models (LLM) , Vision-Language Models (VLM) , and Small Language Models (SLM) . His work bridges theoretical and applied aspects of artificial intelligence, particularly in multimodal and scalable learning systems. The analysis of his research themes indicates a strong focus on next-generation AI architectures, particularly in language and vision integration, with implications for natural language processing, computer vision, and intelligent systems. He teaches courses in Computer Science, Artificial Intelligence, and Machine Learning, contributing to both undergraduate and graduate education in digital sciences. Frederic PRECIOSO is actively involved in research through the MAASAI Project Team, a collaborative effort between Inria, CNRS, and Côte d'Azur University, focusing on advanced AI methodologies and their applications. His work is supported by this interdisciplinary research environment, fostering innovation in AI and data science.

Antoine Doucetمشاهده پروفایل

استاد

• Information Retrieval
• Natural Language Processing
• Text Mining
• +۷ مورد دیگر

Antoine Doucet is a Full Professor at the University of La Rochelle, where he teaches in the Computer Science department of the University Institute of Technology (IUT). He conducts his research at the Computer Science, Image and Interaction Laboratory (L3i) within the 'Images and Content' team, which he has led since 2015. He is also a member of the Franco-Vietnamese laboratory ICTLab and serves as Director of the ICT Department at the University of Science and Technology of Hanoi since 2016. His research focuses on information retrieval, natural language processing, text mining, and artificial intelligence, with emphasis on automatic analysis of text in all forms across languages. His work prioritizes generic methods that work across languages without relying on language-specific linguistic resources. This approach is particularly valuable for under-resourced languages and noisy texts from sources like social media or OCR output. As coordinator of the Horizon 2020 NewsEye project, he led efforts to improve access to European historical newspapers through semantic enrichment and advanced search capabilities. His research has practical applications in epidemic surveillance, document fraud detection, and historical content analysis. The NewsEye project involved 11 teams across Europe, including 3 national libraries and multiple research groups. Best paper award from IMIA Yearbook 2016 (among 1,272 candidates) Best paper award at HCI International with Ilona Nawrot Press coverage for ACL 2013 paper in major publications Recipient of French scientific excellence award (Prime d'Excellence Scientifique) Doucet actively supervises PhD and Master's students, with recent advisees including Chloé Artaud (Document fraud detection), Paul Martin (Photograph Time-Stamping), Ilona Nawrot (Temporal and Multilingual Text Analysis), and Gaël Lejeune (Multilingual Epidemic Surveillance). His research has been funded through multiple projects including ANR Digistory, AmeliOCR, PHC Nusantara, and USTH SWARMS. He has also coordinated significant European projects like NewsEye and Embeddia. At L3i, he leads a research group of approximately 40 persons focused on Images and Digital Content. His work bridges theoretical advances in multilingual text processing with practical applications in historical document analysis, epidemic surveillance, and document security.

Jean-Baptiste Alayracمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Multimodal Learning
• +۳ مورد دیگر

Jean-Baptiste Alayrac is a Researcher at DeepMind, focusing on structured learning from video and natural language. His academic background includes a PhD from the Sierra and Willow groups at Ecole Normale Supérieure and Telecom ParisTech, where he explored machine learning and computer vision. He has held teaching roles as a Teaching Assistant at Ecole Normale Supérieure and other universities, contributing to courses in statistical machine learning and mathematics. His research interests span multimodal learning, vision-language models, self-supervised learning, and efficient retrieval systems. Notable projects include the Flamingo model for few-shot learning and the Perceiver IO architecture for structured data processing. He has also contributed to foundational works like HowTo100M, leveraging large-scale video-text embeddings. Alayrac's publications emphasize cross-modal interactions, with key contributions in adversarial robustness, layered video representations, and weakly supervised learning. His work often bridges computer vision and natural language processing, with applications in instructional video analysis and cross-lingual translation.

Taein Kwonمشاهده پروفایل

پژوهشگر ارشد

• Egocentric Vision
• Action Recognition
• Contextual AI
• +۶ مورد دیگر

Taein Kwon is a postdoctoral research fellow at the Visual Geometry Group (VGG) within the Department of Engineering Science at the University of Oxford, working under Prof. Andrew Zisserman. Previously, he completed his PhD at ETH Zurich under Prof. Marc Pollefeys and earned master's and bachelor's degrees from UCLA and Yonsei University, respectively. His educational background includes: Bachelor's in Electrical Engineering from Yonsei University, Seoul, Korea Master's degree from UCLA PhD from ETH Zurich (defended July 2024) His research spans Egocentric Vision, Action Recognition, Hand-object Interaction, Video Understanding, AR/VR, and Multi-modal Learning, with emphasis on first-person perspective analysis for AI assistants and human-computer interaction. His work integrates 3D reconstruction, pose estimation, and multimodal signals to model complex human activities and physical interactions. Analysis of his 2021-2025 publications reveals a consistent focus on egocentric vision datasets (H2O, HoloAssist, EgoPressure) and novel frameworks for hand-object interaction, action recognition, and gesture understanding. His research demonstrates strong interdisciplinary connections between computer vision, robotics, and human-centered AI, with increasing emphasis on pressure sensing, co-speech gestures, and cross-modal alignment. His scientific recognition includes: CVPR Egovis 2022/2023 Distinguished Paper Award for HoloAssist (July 2024) SNSF Postdoc.Mobility fellowship (May 2024) He actively mentors students on egocentric vision projects, supervising master's theses, semester projects, and collaboration initiatives leading to publications at top conferences. His research is supported by the SNSF fellowship and industry collaborations with Meta Reality Labs and Microsoft Research. As part of Oxford's Visual Geometry Group, he contributes to cutting-edge computer vision research while maintaining strong ties with ETH Zurich's computer vision community through ongoing collaborations and dataset development efforts.

Xavier Alameda-Pinedaمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Audio Processing
• +۷ مورد دیگر

Xavier Alameda-Pineda is a Research Director at Inria Grenoble Rhône-Alpes, where he leads the RobotLearn Team. He is affiliated with Université Grenoble Alpes and has been a key member of the Perception team. His work integrates machine learning, computer vision, and audio processing for scene understanding and human-robot interaction. Research Interests: His research lies at the intersection of multimodal machine learning and social behavior analysis. He focuses on developing algorithms for understanding human behavior in natural settings using audio-visual signals, with applications in robotics and AI companions. His work emphasizes real-world challenges such as noisy data, missing modalities, and dynamic environments. Publication Trends: His recent publications reflect a consistent focus on multimodal fusion, particularly combining vision and audio for social scene analysis. Themes include group behavior recognition, sound source separation, and cross-modal learning, often applied in robotics contexts. Scientific Awards: SIGMM Rising Star Award 2018 IEEE TMM Outstanding Associate Editor Award 2022 ACM TOMM Best Paper Award 2020 Best Paper Award, ACM MM 2015 Best Scientific Paper Award, ICPR 2016 Best Student Paper Award, IEEE WASPAA 2015 Outstanding Paper Award, ICMI 2011 Novel Technology Paper Award Finalist, IROS 2017 Advising and Grants: Xavier has mentored students and early-career researchers, evidenced by co-authored student papers. He coordinated the H2020 SPRING project on socially pertinent robots in gerontological healthcare and co-leads an AI chair on audio-visual perception for companion robots, indicating leadership in funded research initiatives. Labs and Teams: He is the leader of the RobotLearn Team at Inria and was previously part of the Perception team. He has also collaborated with the Multimodal and Human Understanding Group at the University of Trento.

• Image Processing
• Pattern Recognition
• Machine Learning
• +۶ مورد دیگر

Jean-Christophe Burie is a Professor at the University of La Rochelle, serving as Vice-President for Digital Campus and Information Systems, Deputy Director of the L3i Laboratory (Informatics, Imaging, and Interaction), and Director of the joint research laboratory SAIL (Sequential Art Image Laboratory). His interdisciplinary work bridges computer science and humanities. His research focuses on image processing, pattern recognition, and artificial intelligence , with applications in historical manuscripts, comics indexing, and digital security. Key projects include enhancing palm-leaf manuscript analysis (AMADI/STIC ASIE), developing e-BDthèque for comics content extraction, and identity verification systems (MOBIDEM/IDECYS). He collaborates internationally with institutions like Leiden University and Vietnam's ICT Lab on medical imaging and cultural heritage preservation. He advises doctoral students in areas spanning ancient text analysis, document security, and manga character recognition. His recent publications demonstrate a strong emphasis on deep learning adaptations for document analysis, biometric security, and cross-modal recognition systems.

Jean-Luc Dugelayمشاهده پروفایل

استاد

• Biometrics
• Image Forensics
• Video Surveillance
• +۷ مورد دیگر

Professor Jean-Luc Dugelay is a faculty member at EURECOM's Digital Security Department, specializing in facial image processing, image forensics, and biometrics. He holds a PhD in Image Processing from the University of Rennes (1992) and is a Fellow of IEEE (2012) and IAPR (2018). His research focuses on security applications like deepfake detection, privacy protection, and multi-spectral imaging. He leads projects on UAV surveillance, cross-spectrum face recognition, and deepfake countermeasures. Education: PhD in Image Processing (1992, University of Rennes), HDR (2013, University of Nice). Notable projects include HEIMDALL (deepfake detection), CONVERGE (transport security), and ImVerif (image forensics). His work on biometric systems and digital watermarking has been recognized with awards like the SEE Blondel Medal (2010). Research interests span facial analysis, video surveillance, and thermal imaging. He has advised students on topics like age estimation via GANs and cross-spectrum face recognition. Key publications include work on deepfake detection (IPAS 2025), thermal-to-visible face conversion, and event-based vision systems.

Slim Essidمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۷ مورد دیگر

Slim Essid is a Full Professor at Télécom Paris, leading the Audio Data Analysis and Signal Processing (ADASP) group. He holds a Doctorat (Ph.D.) and Habilitation from Université Pierre et Marie Curie (UPMC). With 15+ years of research experience, he has advised 15 PhD graduates and currently co-advises 10 others. His work focuses on machine learning, signal processing, and multimodal systems, publishing over 150 peer-reviewed papers. He serves as a reviewer for top journals/conferences (e.g., IEEE Transactions) and research funding agencies. Education: State Engineering Degree, École Nationale d’Ingénieurs de Tunis (2001) M.Sc. (D.E.A.) in Digital Communication Systems, École Nationale Supérieure des Télécommunications, Paris (2002) Ph.D., Université Pierre et Marie Curie (2005) Habilitation (HDR), UPMC (2015) Research Interests: Multimodal learning, self-supervised representations, audio-visual segmentation, music structure analysis, domain generalization, and speech enhancement. Recent publications highlight innovations like TACO (training-free sound-prompted segmentation) and CLOUDS (domain-generalized semantic segmentation framework using foundation models). His work bridges audio processing with vision and language models, emphasizing unsupervised/zero-shot approaches. Key achievements include state-of-the-art methods in sound event detection, speaker diarization, and music segmentation. He collaborates with 14 post-docs and leads projects funded by French/EU agencies.