جست‌وجوی استاد و پژوهشگر

Hala Lamdouarمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Hala Lamdouar is a Research Fellow at the University of Oxford , specifically affiliated with the Institute of Biomedical Engineering (IBME). She works under the supervision of Professor Alison Noble and completed her DPhil (PhD) at Oxford's Visual Geometry Group (VGG), advised by Professor Andrew Zisserman and Professor Weidi Xie . Her academic journey began with an Engineering degree in signal and image processing from ENSEIRB-MATMECA , Bordeaux, France, followed by a Master's in applied mathematics focusing on machine learning and computer vision at Ecole Normale Superieure , Paris, where she also worked on autonomous driving perception solutions for Valeo . Education Engineering degree, ENSEIRB-MATMECA, Bordeaux MSc in Applied Mathematics (MVA), Ecole Normale Superieure DPhil (PhD), Visual Geometry Group, University of Oxford Lamdouar's research centers on Video Understanding through single and multi-modal learning, particularly in clinical settings and challenging datasets. Key contributions include motion segmentation techniques for detecting camouflaged objects, the creation of the MoCA dataset (Moving Camouflaged Animals), and scalable synthetic data pipelines for motion-based object segmentation. Her work combines ConvNets, Transformers, and optical flow analysis to address partial occlusion and motion absence in videos. Notable achievements include the Best Paper Award at the CVPR Workshop on Robust Video Scene Understanding (2021). She has published in top conferences like BMVC (2021), ICCV (2021), and ACCV (2020), with applications in biomedical imaging, autonomous systems, and unsupervised learning. Additional affiliations include the Centre for Doctoral Training in Autonomous Intelligent Machines & Systems (AIMS).

Pierre Duhamelمشاهده پروفایل

پژوهشگر

• Digital Signal Processing
• Communication Systems
• Image Compression
• +۶ مورد دیگر

Pierre Duhamel is a researcher affiliated with the Signals and Systems Laboratory, focusing on digital signal processing, communication systems, and multimedia security. His work spans theoretical and applied research in network coding, image compression, and channel coding. Primary affiliation: Signals and Systems Laboratory Research roles: Academic researcher, inventor (patents in image coding) Research interests: Duhamel's work addresses challenges in signal processing (e.g., wavelet transforms, L∞ norm compression), communication systems (e.g., robust decoding, WiMAX MAC protocols), and multimedia security (asymmetric watermarking, screen content coding). He explores optimization techniques for wireless networks and error resilience in multimedia transmission. Recent publications (2024–2025) reflect trends in image and signal processing , network optimization , and communication security , with applications to hyperspectral imaging, turbo coding, and cooperative wireless systems.

• Human Reinforcement Learning
• Neuroeconomics
• Decision-Making Under Uncertainty
• +۳ مورد دیگر

Stefano Palminteri is a researcher at the École Normale Supérieure in Paris, leading the Human Reinforcement Learning team at the Cognitive Neuroscience Laboratory (LNC2) since 2017. He holds a PhD in Cognitive Neuroscience from Université Pierre et Marie Curie (2012) and completed postdoctoral work at LNC2, University of Trento, and University College London. Education: Master in Pharmaceutical Biotechnology (University of Bologna, 2006); Master in Cognitive and Behavioral Neuroscience (Université Pierre et Marie Curie, 2007); PhD in Cognitive Neuroscience (UPMC, 2012). His research focuses on computational and neurobiological mechanisms of reinforcement learning, decision-making under uncertainty, and behavioral biases. He investigates how contextual factors (e.g., social influence, neurochemical states) shape learning processes in healthy and clinical populations (e.g., addiction, depression, Tourette syndrome). Recent work explores value normalization, metacognitive biases, and cross-cultural comparisons of reinforcement learning. Key trends in his publications include (1) computational modeling of reward/punishment sensitivity in psychiatric conditions, (2) neuroeconomic frameworks for decision-making under risk and ambiguity, (3) multi-agent reinforcement learning in financial market simulations, and (4) cross-domain comparisons of description vs. experience-based learning. He employs functional neuroimaging, pharmacological interventions, and behavioral experiments to uncover how brain circuits (e.g., striatum, prefrontal cortex) encode value signals. His lab offers internships in epistemic biases and reinforcement learning.

José PICHERALمشاهده پروفایل

استاد

• Signal Processing
• Acoustic Imaging
• Array Processing
• +۵ مورد دیگر

José Picheral is a Professor at CentraleSupélec, affiliated with the Laboratory of Signals and Systems (L2S). He holds a PhD (2003) and HDR (2017) in high-resolution signal processing methods and inverse problems. His research focuses on array processing, source localization, acoustic imaging, and vibration analysis, with applications in aeroacoustics, automotive systems, and industrial monitoring. He has supervised multiple PhD students and contributed to projects like Valeo’s smartphone-based car key replacement system. Education: Engineering Degree: Supélec (1999) and Politecnico di Milano (1999, Erasmus-TIME) PhD: Paris Sud University (2003) Habilitation (HDR): Université Paris Sud (2017) Research Interests: High-resolution methods for distributed sources, sparse signal processing, acoustic imaging, asynchronous measurements, and sensor array design. Current projects include spatial source covariance estimation, EEG spectrum analysis, and automotive applications using smartphone localization. Key Contributions: Over 50 publications in top journals/conferences (e.g., IEEE Transactions, ICASSP). Notable work on MUSIC algorithm robustness, DAMAS optimization, and sparse approaches for tip-timing signals. Advising & Collaboration: Supervised 7 PhD students. Collaborations with SAFRAN, Valeo, and academic teams in Bayesian inference and inverse problems. Active in L2S’s Inverse Problems Group and SYCOMORE team. Labs/Teams: Member of L2S’s Signal Processing and Statistics group, leading research in systems and control, telecommunications, and energy systems.

Gaël Richardمشاهده پروفایل

استاد

• Machine Learning
• Audio Signal Processing
• Artificial Intelligence
• +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.

Emmanuel Giguetمشاهده پروفایل

پژوهشگر

• Computer Science
• Computer Forensics
• Natural Language Processing
• +۴ مورد دیگر

Emmanuel Giguet is a Researcher at the French National Centre for Scientific Research (CNRS), affiliated with the GREYC Laboratory (Computer Science Department) at the University of Caen, Normandy, France. He holds an HDR (Accreditation to Supervise Research) from the University of Caen (2011) and a PhD in Computer Science (1998), both focusing on multilingual natural language processing and document analysis. Roles: Cybersecurity Researcher, Communication and Scientific Mediation Adviser (GREYC Lab), Former Computer Forensics Legal Expert (2005–2015). Expertise: Digital Forensics, Natural Language Processing, Document Structure Analysis, Open Source Intelligence, Competitive Intelligence. His research spans cybersecurity, digital forensics, and NLP applications in document analysis. He co-founded Semiotime (2012), a Competitive Intelligence startup, and has contributed to tools like the GREYC Digital Investigation Platform (G'DIP). He teaches cybersecurity modules at the University of Caen, emphasizing digital forensics and information retrieval. Key Projects: Development of open-source digital investigation tools (e.g., G'DIP). Analysis of deepfake videos and forensic video forgery detection. PDF document structure extraction for financial narrative processing (FinTOC). Affiliations: GREYC Lab (CNRS UMR 6072), University of Caen. Institute for Information Sciences, University of Caen. Communication: Advises GREYC on branding and media presence, including logo guidelines, virtual backgrounds, and scientific mediation resources.

Bastien Berretمشاهده پروفایل

استاد

• Neuroscience
• Biomechanics
• Robotics
• +۵ مورد دیگر

Bastien Berret is a Full Professor at the University of Paris-Saclay, where he leads the MHAPS research team within the Faculty of Sports Sciences. His academic career has progressed from Assistant Professor at Univ. Paris-Sud (2012-present) to Associate Professor with Habilitation à Diriger des Recherches (HDR) in 2017, and finally to Full Professor in September 2020. His research focuses on understanding how the central nervous system controls voluntary movements through a multidisciplinary approach combining neuroscience, biomechanics, and robotics. From a fundamental perspective, he identifies computational principles to explain human behavior in various motor tasks, with practical applications in human-exoskeleton interactions. His work employs computational tools including stochastic optimal control, numerical optimization, and machine learning methods, alongside experimental tools such as motion capture devices, force platforms, and electromyography systems. Berret's publication record shows consistent output since 2008, with particular productivity in recent years (2020-2023). His research trends indicate a strong focus on motor control theory, gravity effects on movement, human-exoskeleton interaction, and computational modeling of neural processes. Recent publications demonstrate increasing interdisciplinary collaboration across neuroscience, robotics, and biomechanics. Notable scientific achievements: Junior member of the prestigious Institut Universitaire de France (IUF) from 2017-2022 Coordinator of the ANR project "EXOMAN" (2020-2024) Extensive publication record in high-impact journals including Science Advances, PLoS Computational Biology, and Journal of Neurophysiology Berret maintains active research collaborations with institutions including INSERM, Istituto Italiano di Tecnologia, and various neuroscience laboratories. His work on stochastic optimal control and muscle activity decomposition has contributed significantly to computational neuroscience methodology. As team leader of MHAPS, he directs research on motor control principles and their applications in rehabilitation and robotics.

Marcelo Pereyraمشاهده پروفایل

دانشیار

• Mathematical Imaging
• Bayesian Analysis
• Computational Imaging
• +۳ مورد دیگر

Marcelo Pereyra is an Associate Professor in the School of Mathematics and Computer Science at Heriot-Watt University and the Maxwell Institute for Mathematical Sciences. He is a visiting professor at the Physics Laboratory of École Normale Supérieure de Lyon (ENS de Lyon) from April 8–29, 2023, hosted by Julián Tachella. His research focuses on Bayesian analysis, computational imaging, and inverse problems, with applications in signal processing and machine learning. Education: Electronic engineering degrees from universities in Buenos Aires and Toulouse, followed by a PhD in signal processing from the Université de Toulouse (2012). Postdoctoral fellowships included roles at the University of Bristol (2012–2016), funded by Marie Curie, Brunel, and French Ministry of Defense grants. In 2019, he held a visiting professorship at the Institut Henri Poincaré. Current collaboration with ENS de Lyon’s SiSyPh team involves developing Bayesian-deep learning methods for blind/semi-blind inverse imaging problems and uncertainty quantification in pandemic modeling (e.g., COVID-19 reproduction number estimation). His work bridges Bayesian inference, convex optimization, and Monte Carlo sampling techniques. Awards: Marie Curie Fellowship, Brunel Postdoctoral Fellowship, French Ministry of Defense Fellowship. Key Projects: Bayesian imaging with Plug-and-Play priors, empirical Bayesian regularization estimation, sparse Bayesian mass-mapping in astronomy. He delivered a seminar on April 26, 2023, titled “Machine Learning and Signal Processing.”

Ichiro Hasuoمشاهده پروفایل

استاد

• Formal Verification
• Mathematical Structures
• Cyber-Physical Systems
• +۳ مورد دیگر

Ichiro Hasuo is a Professor at the National Institute of Informatics (NII) in Tokyo, Japan, where he serves as Director of the Research Center for Mathematical Trust in Software and Systems. He holds a joint appointment at The Graduate University for Advanced Studies (SOKENDAI). Since 2016, he has been the Research Director of the JST ERATO Metamathematics for Systems Design Project, and founded Imiron Co., Ltd. in 2024. Education: PhD in Computer Science (cum laude) from Radboud University Nijmegen (2008) MSc in Mathematical and Computing Sciences from Tokyo Institute of Technology (2004) BSc in Mathematics from University of Tokyo (2002) His research focuses on foundational aspects of software science, particularly formal verification techniques using mathematical structures from category theory and coalgebra. He develops methods for ensuring reliability in cyber-physical systems and systems incorporating machine learning components. Current work emphasizes logical frameworks for autonomous vehicle safety and mathematical trust in complex systems. Hasuo's publications demonstrate consistent focus on theoretical foundations with practical applications. His recent work spans coalgebraic verification methods, temporal logic for hybrid systems, quantum programming semantics, and applications to autonomous driving systems. Key themes include compositional reasoning, probabilistic modeling, and the integration of discrete and continuous system verification. Awards and Honors: Best Paper Award at ICTAC 2024 Minister of Education, Culture, Sports, Science and Technology Commendation (2024) Distinguished Paper Award at CAV 2023 Outstanding Reviewer Award at EMSOFT 2022 Best Paper Award at ICECCS 2018 Best Paper Award at CONCUR 2014 Hiroshi Fujiwara Encouragement Prize (2012) PhD cum laude (2008) He leads multiple major research grants including: JST ASPIRE (2024-2029) for international collaboration on software trust JST START (2022-2025) for autonomous driving verification JST ERATO Metamathematics for Systems Design (2016-2025) Several JSPS KAKENHI grants As head of the MMM laboratory (Hasuo-Lab) at NII, he supervises PhD students and postdoctoral researchers in formal methods and mathematical systems design.

• Expressive Speech Processing
• Speech Synthesis
• Emotion Recognition
• +۳ مورد دیگر

Marie Tahon is a Professor at Le Mans University and Director of the LST (Langage, Signal et Texte) team at LIUM (Laboratoire d'Informatique de l'Université du Maine). Her research spans expressive speech processing with applications in speech synthesis, emotion recognition, and speaker identification, complemented by expertise in musical acoustics for automatic song analysis and organology. Education : Engineering degree from École Centrale de Lyon (2007), M.S. in Acoustics from École Centrale & INSA Lyon (2007), and Ph.D. in Computer Science from University of Paris-Sud (Orsay, 2012). Postdoctoral positions at LIMSI-CNRS (affective computing), LMSSC CNAM (acoustics), and IRISA (Expression team). Research Focus : Tahon's work centers on developing interpretable systems for expressive speech processing. Key contributions include the ALLIES corpus for speech segmentation/diarization and AlloSat for call-center emotion analysis. Her recent publications demonstrate strong emphasis on low-resource speech translation (e.g., Kurdish), speaker verification after resynthesis, and turn-taking analysis in French media using explainable AI techniques. She integrates acoustic and linguistic features for continuous emotion prediction and develops noise-robust models for digital holography. Collaborations & Infrastructure : Leads the COMMUTE and ESPERANTO projects while directing LIUM's LST team. Her work leverages specialized resources like the ALLIES corpus (segmentation, diarization, recognition) and AlloSat (satisfaction/frustration analysis). Current efforts focus on lifelong learning for MOS prediction, multilingual speech translation, and perceptual evaluation of turn-taking phenomena in broadcast media.

Pietro Goriمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Pietro Gori is a Professor in Artificial Intelligence and Medical Imaging at Télécom Paris, working within the IMAGES team of the LTCI laboratory. He has previously collaborated with Inria at the ARAMIS laboratory and NeuroSpin (CEA), focusing on computational anatomy and medical imaging. Education: MSc in Mathematical Modeling and Computation (DTU, Copenhagen), MSc in Biomedical Engineering (University of Padua), PhD in Bioinformatics (Université Pierre et Marie Curie - Paris VI), and HDR (Habilitation à diriger des recherches) in Medical Imaging (Institut polytechnique de Paris, 2024). His research spans machine learning , medical imaging , computational anatomy , shape analysis , and statistical learning . He contributes to open-source tools like deformetrica and Clinica , emphasizing algorithms for analyzing 2D/3D images and clinical neuroimaging studies. Recent publications highlight trends in contrastive learning for disease classification, self-supervised methods in digital pathology, and 3D modeling for surgical applications. His work integrates prior knowledge into AI models and explores biomarkers for psychiatric disorders. Scientific Awards: Habilitation à diriger des recherches (HDR), Institut polytechnique de Paris (2024). He collaborates with NeuroSpin (CEA) and contributes to pediatric surgery planning and psychiatric research , leveraging large datasets of healthy subjects for transfer learning. His patents focus on organ characterization and 3D anatomical model generation.

• Biophysics
• Gene Regulatory Networks
• Immune System Dynamics
• +۶ مورد دیگر

Aleksandra Walczak is a Professor of Biophysics at the École normale supérieure , leading research at the Laboratoire de Physique Théorique (LPENS). Her work bridges statistical physics and biology, focusing on understanding complex living systems through gene regulatory networks, immune system dynamics, and population genetics. Research Interests : Non-equilibrium biological systems, T-cell/B-cell receptor repertoires, stochastic molecular systems, and evolutionary dynamics. Funding & Collaborations : Supported by the Fondation Bettencourt Schueller, she co-organizes interdisciplinary workshops like the Paris Workshop on Immunology and contributes to GDRI Evolution, Regulation and Signaling network. Recent Publications explore gene regulatory principles, immune repertoire modeling, viral-immune coevolution, and collective behavior. She has secured competitive grants and mentors students in theoretical biophysics. Awards : Habilitation à diriger des recherches (2012), Princeton Center for Theoretical Physics Postdoctoral Fellowship (2007-2010). Students & Positions : Actively supervises PhD/Master projects; offers internships in biophysics and quantum engineering.

DJEMAI Mohamedمشاهده پروفایل

استاد

• Control Theory
• Hybrid Systems
• Multi-Agent Systems
• +۹ مورد دیگر

Dr. Mohamed Djemai is a Full Professor at École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA), Cergy, and INSA Hauts-de-France. He is affiliated with the Quartz Laboratory (EA 7393) and LAMIH UMR CNRS 8201 at University Polytechnic Hauts-de-France. His research focuses on nonlinear control systems theory, with emphasis on hybrid and variable structure systems, sliding mode approaches, fault detection, and applications to power systems, robotics, and vehicle dynamics. IEEE Senior Member Associate Editor for Nonlinear Analysis: Hybrid Systems Co-Facilitator of National Working Group GT-SDH (2014–present) Member of IFAC-TC-1.3 (Discrete Event and Hybrid Systems) since 2001 Member of IFAC-TC-2.1 (Control Design) since 2005 His recent publications address fractional-order control of multiagent systems, stability analysis on time scales, fault-tolerant satellite attitude control, and robust consensus algorithms for nonlinear systems. Key methodologies include sliding mode control, event-triggered control, and observer-based fault detection. Current teaching activities encompass diagnostics, linear systems, signal processing, and sensor conditioning. The trend in Dr. Djemai's research since 2022 involves advanced control strategies for cyber-physical systems, distributed fault detection mechanisms, and time scale theory applications to intermittent communication problems. Notable collaborations include work with Michael Defoort, Stefano Di Gennaro, and international institutions like Kyungpook National University and University of Reims. Scientific contributions include: IEEE Senior Member recognition Development of robust exact filtering differentiators Innovations in fixed-time consensus protocols Leadership in IFAC technical committees Editorial role in hybrid systems analysis His laboratory work at Quartz and LAMIH supports applications in aerospace systems, renewable energy conversion, and industrial risk management architectures.

Djamal Zeghlacheمشاهده پروفایل

استاد

• Network Virtualization
• Cloud Computing
• Software-Defined Networking
• +۵ مورد دیگر

Djamal Zeghlache is a Professor at Telecom SudParis (now part of Institut Polytechnique de Paris), where he leads research in network virtualization, cloud computing, and software-defined networking. His work spans multiple domains including wireless communications, network security, and AI applications in networking systems. Affiliated with the SAMOVAR laboratory (Services and Architectures for Multimedia and Ubiquitous Access to Resources), he has established himself as a prominent researcher in network and service management. Professor Zeghlache's research interests focus on the intersection of networking and artificial intelligence, with particular emphasis on network virtualization, cloud computing, and software-defined networking. His work addresses critical challenges in network function virtualization, resource allocation, and optimization in distributed systems. In recent years, he has expanded his research to include AI-driven approaches for network management, healthcare monitoring using wireless technologies, and optical network control. His publications demonstrate a consistent trajectory from traditional networking challenges to more complex, AI-integrated solutions for modern network infrastructures. Analysis of his recent publications (2023-2025) reveals a strong trend toward integrating machine learning, particularly deep reinforcement learning and graph neural networks, into network management systems. His work spans multiple application areas including healthcare monitoring (using RF-based techniques for vital sign detection), transportation systems (bike-sharing optimization), and optical networking (disaggregated control frameworks). The interdisciplinary nature of his research connects computer networking with biomedical engineering, urban mobility, and formal methods for system verification. Professor Zeghlache has supervised numerous PhD students and collaborated extensively with researchers across Europe and internationally. His work has resulted in significant contributions to network virtualization, cloud resource management, and network security. He has been involved in multiple research projects focusing on next-generation networking technologies, including 5G, network slicing, and cloud-native network functions.

• Medical Imaging
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Diana Mateus is a Researcher at Centrale Nantes, affiliated with the LS2N Laboratory. She holds a bachelor's in Electronic Engineering from the University of Javeriana (Colombia), a Master's in Control and Automation from Paul Sabatier University (France), and a PhD in Computer Vision from INRIA Grenoble. After postdoctoral work and eight years as an associate researcher at Technical University of Munich and Helmholtz Zentrum (Germany), she returned to France to join Centrale Nantes via the Connect Talent Program, securing a starting grant for interdisciplinary research in medical image analysis and machine learning. Her research focuses on Milcom —a project integrating multimodal imaging (MRI, CT, ultrasound, PET) with machine learning to advance computational medicine. Key objectives include computer-aided diagnosis, disease knowledge advancement, and improving imaging quality. Collaborations include the hospital's Nuclear Medicine Department, focusing on multiple myeloma, and companies Hera-Mi and Keosys for breast cancer research. She supervises six PhD students and two postdocs, emphasizing synergies between engineering and healthcare. At Centrale Nantes, she benefits from dynamic institutional support, quality students, and complementary team skills. Her lab recently acquired a research-dedicated ultrasound machine to develop machine learning-driven imaging optimization. Living in Nantes, she values its family-friendly environment and proximity to academic and medical networks.