جست‌وجوی استاد و پژوهشگر

• Criminal Law
• Corporate Criminal Law
• Cybercrime
• +۴ مورد دیگر

Alexandra Serinet is a Lecturer at the University of Versailles Saint-Quentin-en-Yvelines, Faculty of Law and Political Science, Department of Private Law. She serves as the Director of the Master II Corporate Criminal Law and Compliance and Head of the Master I Business Law. Her academic work is centered in the Business Law and New Technologies Laboratory (DANTE). Education: Doctorate in Law (1999), University Paris XIII - Thesis: 'the presumption of innocence and presumptions in criminal law', with distinction 'Very honorable with congratulations from the jury' Certificate of aptitude for the legal profession (2000) Research Interests: Dr. Serinet's research spans Criminal Law, Corporate Criminal Law, Cybercrime, Business Law, Compliance, and Defamation Law. Her work examines the application of criminal law in corporate and digital contexts, with a focus on procedural aspects and the protection of rights. She has contributed to the simplification of criminal law and the understanding of criminal liability in business settings. Recent Publications Trends: Between 2015 and 2016, her work addressed pressing issues such as defamation in the judiciary, the simplification of criminal law, provocation as an excuse, and the intersection of criminal law with cybercrime and corporate compliance. These publications reflect a consistent focus on the evolving challenges of criminal law in modern society, particularly in business and technology-driven environments. Scientific Awards: No scientific awards mentioned Advising and Academic Leadership: As Director of the Master II Corporate Criminal Law and Compliance and Head of the Master I Business Law, Dr. Serinet plays a key role in graduate education. She is a member of the university's selection committee for private law and has organized academic conferences, such as the 2008 event on managing criminal risk in companies. Laboratories and Research Teams: Business Law and New Technologies Laboratory (DANTE) - active member Associated with CESDIP, PRINTEMPS, and VIP research laboratories

• Compliance Law
• Corporate Governance
• Digital Law
• +۵ مورد دیگر

Marion Larouer is a Lecturer at Toulouse Capitole University, affiliated with the Institute of Space, Territorial, Cultural and Communication Law (IDETCOM). Specializing in Private Law and Criminal Sciences, her work critically examines regulatory frameworks for corporate behavior, particularly through non-binding instruments like codes of conduct in digital and AI contexts. Based at desk AR311, she maintains active scholarly engagement with recent publications through 2024. Her research centers on Compliance Law and Corporate Governance , with deep expertise in: Soft law mechanisms for regulating AI and digital platforms Ethical investment standards and societal performance metrics Interoperability challenges between technical and legal standards Protective frameworks for individuals in health AI applications Evolution of corporate governance codes (e.g., AFEP-MEDEF) Dr. Larouer consistently bridges theoretical legal analysis with practical regulatory solutions, emphasizing how corporations adopt internal standards to address emerging ethical dilemmas. Analysis of her 15 most recent publications (2017-2024) reveals a dominant focus on soft law as a regulatory tool across digital transformation contexts. Over 70% of her work addresses AI governance, platform regulation, and ethical charters, demonstrating increasing specialization in digital law since 2020. Her scholarship uniquely connects compliance mechanisms with societal impact metrics, particularly in corporate social responsibility frameworks. Scientific Awards: No awards or fellowships documented in provided materials Regarding academic mentorship and funding, the available documentation contains no references to doctoral students, research grants, or externally funded projects. Her scholarly output appears primarily institutionally supported through IDETCOM and university press channels. As a core member of IDETCOM, Dr. Larouer contributes to this interdisciplinary research institute focused on contemporary legal challenges in space, territorial, cultural, and communication law. The institute serves as her primary academic hub for collaborative work on regulatory innovation, particularly regarding digital transformation's legal implications.

Jean-Baptiste Eichenlaubمشاهده پروفایل

دانشیار

• Cognitive Neuroscience
• Sleep Research
• Neurophysiology
• +۳ مورد دیگر

Jean-Baptiste Eichenlaub is an Associate Professor at Université Savoie Mont Blanc (USMB), affiliated with the Laboratoire de Psychologie et NeuroCognition (LPNC). Previously, he held postdoctoral positions at Massachusetts General Hospital/Harvard Medical School and Swansea University. His work spans sleep research , cognitive neuroscience , and neural correlates of dreaming , with a focus on memory consolidation , dream recall frequency , and REM sleep mechanisms . Ph.D. in Cognitive Neuroscience (Lyon Neuroscience Research Center, 2011) Master of Physiology & Neuroscience (Lyon University, 2008) Bachelor of Biology (Lyon University, 2006) His research integrates neuroimaging , electrophysiology , and computational tools , exemplified by contributions like the DREAM EEG database and Spinky toolbox for sleep analysis. Recent publications examine sleep habits in preteens , dream-lag effects , and gamma oscillations in NREM sleep . Notable roles include: Junior member, Institut Universitaire de France (2024 - present) Co-responsible - Memory Team (2021 - present) Coordinator - 1st Year Bachelor's in Psychology (2021 - 2024)

Michel Barretمشاهده پروفایل

پژوهشگر

• Statistical signal processing
• Coding theory
• Source separation
• +۳ مورد دیگر

Michel Barret is a researcher affiliated with the Georgia Tech International Joint Unit and the National Center for Scientific Research (CNRS). His work focuses on statistical signal processing, coding theory, and source separation, with applications in multicomponent image compression, hyperspectral data analysis, and seismic signal processing. Research Focus: Statistical signal processing, coding algorithms, and source separation Applications: Hyperspectral image compression, seismic data analysis, and medical signal processing Barret has contributed to advancements in zero-tree encoding, orthogonal matching pursuit algorithms, and independent component analysis (ICA) for high-rate source coding. Recent publications highlight his work on exogenous spectral transforms, polar codes for covert communications, and novel approaches to multicomponent data compression. His collaborative research spans diverse domains including geophysics, remote sensing, and biomedical engineering, often involving algorithm design for complex signal processing challenges. While specific awards or mentoring activities aren't mentioned, his extensive publication record demonstrates significant contributions to signal compression methodologies.

Matthieu Labeauمشاهده پروفایل

مدرس ارشد

• Natural Language Processing
• Machine Learning
• Representation Learning
• +۲ مورد دیگر

Matthieu Labeau is a Senior Lecturer at Télécom Paris, affiliated with the Department of Image, Data, Signal (IDS). He joined the institution in 2019 after completing his PhD at the University of Paris-Saclay and a postdoctoral position at the University of Edinburgh. His research primarily centers on Natural Language Processing (NLP), with specialized interests in representation learning, language modeling, and conversational AI. His work spans: Core NLP : Contextual word representations, semantic alignment, and polysemy analysis. Machine Learning : Hierarchical classification, graph prediction, and few-shot learning techniques. Applications : Emotion recognition in dialogues, persuasiveness decoding, and educational NLP tools. Labeau leads research in the Signal, Statistics and Learning (S2A) team at the Information Processing and Communication Laboratory (LTCI). His recent publications demonstrate a strong focus on improving language model interpretability and efficiency, with innovations in tokenization effects and multimodal fusion. Though no awards or grants are mentioned, his consistent output in top-tier venues (e.g., NeurIPS, ACL, AAAI) highlights significant scholarly contributions. He actively collaborates on tools like EZCAT for conversation annotation and mentors researchers in NLP projects. Current work explores LLM capabilities in persuasion assessment and optimal transport methods for graph-based learning.

Georgios Exarchakisمشاهده پروفایل

پژوهشگر

• Machine Learning
• Theoretical Neuroscience
• Signal Processing
• +۵ مورد دیگر

Georgios Exarchakis is a Research Scientist (Chargé de recherche) at the Institut Hospitalo-Universitaire (IHU) Strasbourg, specializing in Machine Learning and Theoretical Neuroscience. Previously, he held postdoctoral positions at Institut de la Vision and Prof. Stephane Mallat’s DATA team at École Normale Supérieure. He earned a Dr. rer. nat. in Machine Learning from the University of Oldenburg (2016), an M.Sc. in Computational Science from Goethe University Frankfurt (2012), and a Mathematics Diploma from Aristotle University of Thessaloniki (2008). His research focuses on sparse coding, graphical models, signal processing, and deep learning, with applications in spike sorting, quantum chemistry, and natural image statistics. He developed the Kymatio library for wavelet scattering transforms and contributed to ProSper, a probabilistic sparse coding library. His work bridges theoretical neuroscience and machine learning, emphasizing interpretable models and scalable algorithms. Key contributions include advancing variational sampling techniques for generative models, solid harmonic wavelet scattering for molecular property prediction, and discrete sparse coding frameworks. He teaches machine learning courses at École Polytechnique and Oldenburg University, emphasizing practical applications and theoretical foundations. Georgios collaborates with interdisciplinary teams, including the Redwood Center for Theoretical Neuroscience at UC Berkeley and the University of Frankfurt, to advance methodologies in computational neuroscience and AI. His current research at IHU Strasbourg integrates machine learning into image-guided surgery technologies.

• Neuroscience
• Parkinson's Disease
• Decision Making
• +۵ مورد دیگر

Thomas Parent is a Researcher at the University of Bordeaux affiliated with the Institut de Mathématiques de Marseille (I2M) and the GCE research team. His work bridges clinical neurology, computational neuroscience, and behavioral studies, focusing on Parkinson's disease pathophysiology and therapeutic interventions. Research interests span Neuroscience , Parkinson's Disease , Decision Making , and Neuroethics . He investigates basal ganglia-cortical loops in motor adaptation using primate models, develops computational frameworks for neural decision processes, and critically examines societal implications of neuroscience discourse. Recent work includes apomorphine infusion trials for impulse control disorders and validation of non-motor symptom scales in Parkinson's. Publication trends (2021-2025) reveal three convergent trajectories: 1) Clinical Parkinson's research (therapeutics, progression biomarkers, late-stage care), 2) Fundamental mechanisms of reward-based decision making in primates, and 3) Critical analysis of neuroscience communication and ethics. This triad demonstrates integration of bedside observations, laboratory neuroscience, and philosophical scrutiny. Active advising and grant activities are implied by his extensive collaborative publications across French and European institutions, though specific projects remain unenumerated in source materials. His laboratory work with non-human primates and Parkinson's patient cohorts indicates ongoing experimental and clinical research programs.

Edwin Carlinetمشاهده پروفایل

استادیار

• Mathematical Morphology
• Image Processing
• High Performance Computing
• +۱ مورد دیگر

Edwin Carlinet is an Assistant Professor at EPITA Research Laboratory (LRE), specializing in mathematical morphology, hierarchical image representations, and high-performance computing. His research develops efficient algorithms for image processing with applications in document analysis and historical map vectorization. Key projects include the SoDuCo initiative extracting data from 19th-century Parisian trade directories and parallel implementations of morphology algorithms. He leads development of the Pylene image processing library, focusing on generic and efficient implementations. Teaching covers parallel programming, data compression, C++, and GPU programming at EPITA, alongside bioinformatics courses at Sup'Biotech. Research supervision includes PhD candidates working on modern implementations of mathematical morphology algorithms and noise estimation techniques. Recent publications focus on optimizing computational workflows for historical document processing, including noise impact analysis on morphological structures and GPU acceleration of max-tree computation.

• Systems Neuroscience
• Neurophysiology
• Sensory Systems
• +۹ 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.

• Signal Processing
• Audio Signal Processing
• Machine Learning
• +۷ مورد دیگر

Roland Badeau is a Full Professor in the Signal, Statistics and Learning (S2A) team within the Image, Data, Signal (IDS) Department at Télécom Paris, Institut Polytechnique de Paris. His primary affiliation is with the Information Processing and Communication Laboratory (LTCI). Research Interests: Badeau specializes in statistical modeling of non-stationary signals, with core expertise in adaptive high-resolution spectral analysis and Bayesian extensions to Non-negative Matrix Factorization (NMF). His work spans room acoustics (stochastic reverberation models), data representation (dimensionality reduction, time-frequency analysis), probabilistic latent variable modeling, and algorithm development (Bayesian estimation, optimization methods, fast adaptive algorithms). Applications focus on audio/music processing including source separation, denoising, dereverberation, multipitch estimation, and automatic music transcription, with extensions to biomedical data analysis and digital communications. Key Trends in Publications: Recent work centers on Statistical Wave Field Theory, establishing mathematical frameworks for reverberation modeling using energy-stress tensor formalism and Riemannian geometry. His publications demonstrate a progression from foundational signal processing algorithms (e.g., YAST, ESPRIT) to physics-informed approaches for polyhedral rooms and frequency-dependent attenuation, with strong emphasis on Bayesian and alpha-stable distribution methods for robust audio separation. Academic Leadership: Badeau supervises doctoral and master’s theses while leading teaching units at Télécom Paris. He serves as the TSIA study track supervisor (Signal Processing for Artificial Intelligence) and Master ATIAM correspondent. His team (S2A) develops tools like DESAM for joint source separation and multi-track coding.

Antoine Tonnoirمشاهده پروفایل

مدرس

• Analysis
• PDE
• Modelling
• +۷ مورد دیگر

Antoine Tonnoir is a Lecturer at INSA Rouen Normandie, affiliated with the INSA Mathematics Laboratory (LMI), where he focuses on numerical modeling, partial differential equations (PDEs), numerical analysis, inverse problems, and wave propagation in unbounded domains. His work includes applications in ultrasonic non-destructive testing (NDT), medical imaging, and geophysical studies. His research spans collaborations with institutions such as University of Geneva POEMS LMI M3DISIM CEREMA INRIA and projects like ANR COSS (Control on Stratified Structures), DEFHY3GEO (coastal erosion), and INSA Exploratory Project (NDT simulation tools). Recent publications highlight his work on reciprocity gap methods for source identification infinite element modeling for geophysics Schwarz decomposition for Helmholtz equations transparent boundary conditions in waveguides non-local traffic flow models with keywords spanning applied mathematics, computational physics, and inverse problems. He co-supervises doctoral students in inverse problems and wave propagation, and develops the 2D/3D finite element code PyLGRIM for linear PDEs and geophysical applications. His teaching includes courses in numerical analysis, ODEs, topology, and optimization at INSA Rouen’s STPI and GM departments.

Geoffroy Peetersمشاهده پروفایل

استاد

• Audio Signal Processing
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Geoffroy Peeters is a Full Professor at Télécom Paris since 2018, affiliated with the Signal, Statistics and Learning (S2A) team within the Image, Data, Signal (IDS) department of the Laboratoire Traitement et Communication de l'Information (LTCI) . He received his PhD (2001) and Habilitation (2013) in audio signal processing from University Paris-VI . Education : PhD in Computer Science (2001), HDR (2013) from University Paris VI; Master in electronic engineering (1995) from Polytechnique Louvain-la-Neuve His research focuses on signal processing and machine learning (including deep learning) applied to audio and music data analysis , covering areas such as: Timbre description and audio feature extraction Singing voice detection and source separation Beat/downbeat/tempo estimation Chord/key/multi-pitch estimation Music structure/summary analysis Audio identification and auto-tagging Corpus creation and evaluation methodologies With over 200 publications and several patents, his work contributed to the ISO MPEG-7 audio standard . He has held leadership roles in academic conferences including: Co-general-chair: DAFx-2011, ISMIR-2018 Co-program-chair: ISMIR-2019 ISMIR Board member (2016-2017, 2018-2019) Elected ISMIR President (2022-2023) IEEE Task Force on Computational Audio Processing His teaching focuses on signal processing , deep learning , and music information retrieval through various projects and workshops.

Gael Thomasمشاهده پروفایل

استاد

• Distributed Systems
• Operating Systems
• Virtualization
• +۴ مورد دیگر

Prof. Gael Thomas is a Professor at Telecom SudParis, focusing on distributed systems, operating systems, and high-performance computing. His work emphasizes virtualization, concurrency control, and secure execution environments. He has contributed extensively to projects like VMKit, I-JVM, and J-NVM, addressing challenges in garbage collection, system scalability, and trusted computing. His research interests include: Virtualization techniques for improved system isolation Optimizing garbage collectors for multicore architectures Trusted execution environments using SGX and HTM Performance analysis of MPI/OpenMP applications Notable contributions: Developed PALLAS trace format for HPC analysis (2025) Created NVCache for NVMM-based I/O optimization (2021) Pioneered secure code partitioning techniques (2023-2024) His work has been presented at top conferences including EuroSys, Middleware, and DSN.

• Cyber-Physical Systems
• Interval Analysis
• Robotics
• +۵ مورد دیگر

Alexandre Chapoutot is a Lecturer and researcher at ENSTA Paris within the Computer Science and Systems Engineering Unit (U2IS). He leads the Semantics of Hybrid Systems (SSH) research group and serves as Head of the 2nd year specialization of the engineering cycle in Computer Science. His academic work bridges theoretical computer science with practical engineering applications, focusing on the rigorous analysis and verification of complex systems. Chapoutot's research interests span the verification of cyber-physical systems, with a strong emphasis on interval analysis and mobile robotics. He has made significant contributions to the static analysis of programs through abstract interpretation, particularly for improving the accuracy of floating-point arithmetic calculations. His work bridges theoretical computer science with practical engineering applications, developing methods that ensure the reliability and safety of autonomous systems. The research has direct applications in robotics, control systems, and safety-critical embedded software where numerical precision and system correctness are paramount. His recent publications reveal a strong trend toward the integration of formal methods with practical robotics applications. The work spans theoretical foundations in hybrid systems verification, practical implementations for robotics navigation and control, and innovative approaches to combining different verification techniques. A significant portion of his recent work focuses on Signal Temporal Logic applications, trajectory optimization, and the development of verified algorithms for autonomous systems. The publications demonstrate a consistent progression from theoretical foundations to practical implementations with real-world robotics applications. Chapoutot actively supervises multiple PhD students working on cutting-edge research topics at the intersection of formal methods and robotics. His research group collaborates extensively with other institutions and researchers, as evidenced by the numerous co-authored publications across various domains. He contributes to the development of the DynIbex software library, which provides validated solutions for ordinary differential equations using Runge-Kutta methods. His laboratory work centers around the Semantics of Hybrid Systems (SSH) group, which develops theoretical frameworks and practical tools for the analysis and verification of cyber-physical systems. The group's research spans formal methods, numerical analysis, and robotics, with a strong emphasis on developing mathematically rigorous approaches that can be applied to real-world engineering problems.

Jean-Louis Gutzwillerمشاهده پروفایل

پژوهشگر

• Image Processing
• Signal Processing
• Computer Vision
• +۳ مورد دیگر

Jean-Louis Gutzwiller is a Researcher at the Lorrain Laboratory for Research in Computer Science and its Applications (LORIA), a joint research unit of CNRS, Inria, and the University of Lorraine, focusing on advanced image processing and compression techniques for hyperspectral data and transportation systems. His work bridges theoretical signal processing with practical applications in remote sensing and intelligent infrastructure. His research spans hyperspectral image compression, wavelet transforms, vehicle-to-road communication systems, and speaker diarization algorithms. Key contributions include developing exogenous quasi-optimal spectral transforms for MERIS satellite data, optimizing SPIHT coders for hyperspectral imaging, and pioneering electromagnetic loop-based vehicle detection systems. His methodology emphasizes low-complexity solutions suitable for onboard satellite processing and real-time applications. Analysis of his 11 publications (2022-2025) reveals a dominant focus on hyperspectral compression techniques, particularly using exogenous transforms and zero-tree coding for remote sensing data. Secondary themes include vehicle infrastructure communication systems and neural gas algorithms for audio processing. His work consistently addresses computational efficiency challenges in resource-constrained environments like satellite platforms. Gutzwiller operates within LORIA's collaborative framework, contributing to France's national research ecosystem through partnerships with CNRS and Inria. His laboratory affiliation enables interdisciplinary work spanning computer science, aerospace engineering, and transportation technology, with practical implementations in earth observation and smart mobility systems.