جستجوی استاد راهنمای دانشگاهی

Yohan PETETINمشاهده پروفایل

دانشیار

• Monte Carlo methods
• Bayesian filtering
• Hidden Markov models
• +۵ مورد دیگر

Yohan PETETIN is an Associate Professor at Telecom SudParis (Institut polytechnique de Paris) in the CITI Department. His research focuses on Bayesian filtering, Monte Carlo methods, hidden Markov models, and multi-object tracking. He has authored over 20 peer-reviewed articles since 2011, with notable contributions in IEEE Transactions on Signal Processing and other top venues. His work bridges statistical signal processing with machine learning applications. PhD: Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles Markoviens (2013, Telecom SudParis) HDR: Generative models for time series data (2023, Institut polytechnique de Paris) Research interests emphasize sequential Monte Carlo algorithms, particle filtering optimizations, and deep learning integration for time-series analysis. Recent work explores expressivity comparisons between recurrent neural networks and hidden Markov models. Teaching includes courses on probabilistic graphical models, Bayesian filtering, and deep learning across undergraduate and graduate programs at Telecom SudParis and affiliated institutions.

Stéphanie Cailliesمشاهده پروفایل

استاد

• Cognitive Psychology
• Social Cognition
• Theory of Mind
• +۴ مورد دیگر

Stéphanie Caillies is a Professor of Cognitive Psychology at the University of Reims Champagne-Ardenne and serves as Deputy Director of the C2S Laboratory (Cognition, Santé, Socialisation EA 6291). Her research focuses on social cognition, language processing, and theory of mind from developmental, neuropsychological, and psychopathological perspectives. Research Interests: Pragmatic language comprehension and figurative language processing Neural correlates of social cognition in clinical populations (schizophrenia, bipolar disorder) Developmental trajectories of theory of mind in neurotypical and at-risk children Neurocognitive mechanisms of irony and metaphor understanding Parent-child interactions in premature infants Her recent publications demonstrate strong emphasis on neuroimaging approaches (fMRI, ERP) to study social cognition, with recurring themes in clinical populations and developmental disorders. Research frequently bridges psycholinguistics, clinical psychology, and cognitive neuroscience. Current Research Projects: POSTURE (2023-2027): Psychologie et réalité virtuelle - ANR-funded project on dynamic posture recognition EDIRE (2022-2025): Horizon Europe project on educational development PHRC CALIN: Hospital clinical research on sensory-tonic stimulation for premature infants Teaching Activities: Regularly teaches courses in Statistics, Language Development, Academic Learning, and Social Cognition Disorders. Organized multiple scientific conferences including the 59th Congress of the French Society of Psychology. Doctoral Supervision: Currently supervising theses on executive functions in premature children and parent-child interactions. Previously supervised dissertations on pragmatic language in reading comprehension and neurofunctional correlates of theory of mind in schizophrenia.

Aws Albarghouthiمشاهده پروفایل

پژوهشگر

• Program Synthesis
• Formal Verification
• Quantum Computing
• +۵ مورد دیگر

Aws Albarghouthi is affiliated with the University of Wisconsin-Madison, USA. He is an active researcher with significant contributions to program synthesis, formal verification, and machine learning. Key roles: Author, Session Chair, Committee Member in conferences like PLDI, POPL, VMCAI, SPLASH, and ICFP. Research spans quantum computing, differential privacy, and static analysis. Research Trends include: Quantum Circuit Compilation and Optimization Probabilistic Verification of Fairness and Privacy Synthesis of Datalog and MapReduce Programs Neural-Augmented Static Analysis Bias Detection in Data Security Robustness in Machine Learning

Yves Le Traonمشاهده پروفایل

استاد

• Machine learning systems
• Software testing
• Software security
• +۸ مورد دیگر

Dr. Yves Le Traon is a Full Professor of Computer Science at the University of Luxembourg, where he serves as Vice-Director of the Interdisciplinary Centre for Security, Reliability and Trust (SnT). He leads the 25-member SerVal research group (SEcurity, Reasoning and VALidation), focusing on software testing, security, and data-intensive systems. Previously, he chaired the CSC Research Unit (2013-2016) and pioneered model-driven engineering at INRIA. PhD and engineering degree in Computer Science from Institut National Polytechnique, Grenoble (1997) Former Associate Professor at University of Rennes (1998-2004) His research spans three main areas: innovative software testing and repair , Android security through static analysis and machine learning , and robust machine learning system design . Collaborations include industry leaders like PayPal, CREOS, and Cebi in fintech, smartgrid, and industry 4.0 domains. Awarded IEEE Fellow (2022) and Facebook Testing & Verification Research Award (2019) , he chairs editorial boards for STVR, SoSym, and IEEE Transactions on Reliability. His team has produced 20+ PhD graduates including Li Li (Monash University), Donia El Kateb (European Investment Bank), and Alexandre Bartel (SnT Research Associate). Commercial impact includes co-founding Datathings for runtime AI decision systems.

Frédéric PASCALمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Adaptive Radar Detection
• +۵ مورد دیگر

Frédéric Pascal is a Full Professor at CentraleSupélec, part of the University of Paris-Saclay, and a member of the Laboratoire des Signaux et Systèmes (L2S). His research focuses on statistical signal processing, machine learning, and robust estimation techniques, with applications in radar detection, covariance matrix estimation, and information geometry. He has held roles including Coordinator of AI activities at CentraleSupélec and Head of the "Signals and Statistics" group at L2S. His academic journey includes a PhD from University Paris X – Nanterre (2006) and an HDR (2012) from University Paris-Sud. His work emphasizes adaptive signal processing in non-Gaussian environments, with contributions to robust covariance estimation, M-estimators, and applications in radar systems and biomedical signal processing. He has authored over 100 journal/conference papers and serves as an Associate Editor for IEEE Transactions on Signal Processing and Elsevier Signal Processing. Current research interests include AI transparency, data-driven methods for industry 4.0, and health-related signal analysis.

Hédi Hadijiمشاهده پروفایل

دانشیار

• Online Decision-Making
• Adaptivity in Bandits
• Online Learning
• +۳ مورد دیگر

Hédi Hadiji is an Associate Professor at the Laboratoire des Signaux et Systèmes (L2S) at CentraleSupélec. His research focuses on the mathematics of online decision-making, particularly adaptivity in bandits and online learning. Prior to this role, he was a postdoctoral researcher at the University of Amsterdam under Tim van Erven. He holds a PhD in Mathematics from Université Paris-Saclay, supervised by Gilles Stoltz and Pascal Massart. Education: PhD in Mathematics, Université Paris-Saclay (2020) Masters in Probability and Statistics, Université Paris-Saclay (2017) Part III Mathematical Tripos, University of Cambridge (2016) École Polytechnique (2012–2016) Research Interests: Hédi’s work addresses theoretical foundations of online learning, including bandit algorithms, reinforcement learning, and optimization. He explores adaptive strategies in dynamic environments, with applications to stochastic and adversarial settings. Key themes include minimax optimal algorithms, regret analysis, and the interplay between exploration and exploitation. Recent Publications: His recent work includes minimax optimal algorithms for linear bandits, tracking solutions in time-varying systems, and diversity-preserving strategies in K-armed bandits. These contributions advance theoretical understanding of adaptive decision-making in complex systems. Teaching: He teaches courses on the theoretical principles of deep learning, reinforcement learning, and mathematical foundations at the graduate level. His lectures emphasize rigorous analysis of generalization, optimization, and the neural tangent kernel. Labs & Affiliations: His primary affiliation is with L2S, where he engages in collaborative research groups such as MODESTY, COMEDY, and SYCOMORE. His work intersects signal processing, control systems, and communication networks.

Alain Trouvéمشاهده پروفایل

استاد

• Shape Spaces and Applications
• Imaging Processing and Computational Vision
• Computational Biology
• +۳ مورد دیگر

Alain Trouvé is a Professor at the Center for Mathematics and Their Applications (CMLA) within the Ecole Normale Supérieure de Cachan , France. His research focuses on Shape Spaces , Computational Anatomy , Imaging Processing , and Biological Imaging , with applications in medical and computational fields. He directs the Mathematics Department at ENS Cachan and contributes to neuroanatomical studies through diffeomorphometry techniques. Key roles include membership in the Conseil National des Universités (Section 26) and teaching responsibilities such as courses on Geometry and Shape Spaces and Probability Theory . His work spans from theoretical frameworks (e.g., Hamiltonian modeling of shape evolution) to practical applications like 3D cell imaging and white matter fiber analysis. Publications emphasize interdisciplinary methods, including diffeomorphic registration, varifold-based image analysis, and stochastic shape evolutions. Current projects explore multi-scale modeling of biological systems and AI-driven medical diagnostics. Key Research Themes: Diffeomorphic mappings, computational vision, and functional shape analysis. Teaching: Courses on geometric modeling and probability at undergraduate and graduate levels. Tools Developed: xIV-LDDMM Toolkit for multi-modal biomedical data analysis.

Rémi Flamaryمشاهده پروفایل

استاد

• Machine Learning
• Optimal Transport
• Domain Adaptation
• +۷ مورد دیگر

Rémi Flamary is a Professor in the Applied Mathematics department at École Polytechnique, France, and a member of the CMAP Laboratory. Previously, he held an Associate Professor position at Université Côte d'Azur in the Department of Electronics and Lagrange Laboratory. He completed his PhD at Rouen University under Alain Rakotomamonjy, focusing on statistical signal processing and optimization. His research interests span machine learning, optimal transport, domain adaptation, and their applications in biomedical engineering, energy, and remote sensing. He leads the development of the POT (Python Optimal Transport) library and contributes to projects like SKADA for domain adaptation. Education: PhD in Applied Mathematics, Rouen University (LITIS Laboratory) Research Focus: Flamary's work emphasizes leveraging optimal transport theory for machine learning tasks such as graph prediction, signal normalization, and cross-domain adaptation. His contributions include novel algorithms for unbalanced transport, semi-relaxed Gromov-Wasserstein distances, and end-to-end graph generation frameworks. He actively collaborates on applications in neuroscience, astronomy, and energy systems. Professional Activities: He has presented at NeurIPS and other top conferences, supervised PhD students like Cédric Vincent-Cuaz, and contributed to open-source software. His teaching includes courses on signal processing and machine learning at École Polytechnique.

Ammar Mianمشاهده پروفایل

دانشیار

• Statistical Signal Processing
• Machine Learning
• Riemannian Geometry
• +۴ مورد دیگر

Ammar Mian is an Associate Professor at Université Savoie Mont Blanc, affiliated with the LISTIC lab and Polytech Annecy-Chambéry. He holds a PhD from CentraleSupélec (2016-2019) and conducted postdoctoral research at Aalto University (2019-2020). His research focuses on statistical signal processing, machine learning, and Riemannian geometry with applications in remote sensing and frugal computations. He leads the Qanat project, an experiment tracking tool for reproducible research. Research interests include covariance-based methods for SAR image analysis, robust detection algorithms for sonar and GPR systems, and optimization on Riemannian manifolds. His work emphasizes reproducibility in ML and efficient computational techniques for resource-constrained environments. Key contributions include real-time SAR time-series change detection, robust classification using second-order deep learning models, and novel methods for handling missing data in EEG signals. His recent articles (2023-2025) explore reproducibility frameworks, GPR-based object classification, and Riemannian geometry applications. No awards listed, but maintains active collaborations through LISTIC and industry partnerships. Advises students via internship programs (e.g., Federated ML energy cost analysis). Lab work involves developing open-source tools like Qanat for experiment management and reproducibility.

Catuscia Palamidessiمشاهده پروفایل

استاد پژوهش

• Privacy
• Data Security
• Information Flow
• +۵ مورد دیگر

Catuscia Palamidessi is a Research Professor and head of the Comete project team (Concurrence, Mobilité et Transactions) at the Inria Saclay - Île-de-France research center. Her work focuses on privacy, security, and information flow on social networks, with particular emphasis on protecting sensitive data while preserving utility. Her primary research interests include differential privacy, location data protection, and the use of machine learning techniques for privacy preservation. She has pioneered work on Local Differential Privacy (LDP) and its integration with generative adversarial networks to balance privacy protection with data utility. Her research addresses the critical challenge of securing personal data without destroying its usefulness for services and statistical analysis. Her most notable achievement is receiving the ERC Advanced Grant in 2018 for her HYPATIA project (Privacy and Utility Allied), which seeks to optimize the security and utility of personal digital data. This €2.2 million grant has enabled her to expand her research team and develop practical privacy solutions. ERC Advanced Grant 2018 for HYPATIA project As leader of the Comete team, she oversees research that has led to practical applications like Location Guard, a browser extension that adds controlled noise to location data to protect user privacy while maintaining service functionality. Her work has established important partnerships with industry leaders like Renault and numerous academic collaborations worldwide.

• Human-robot physical interaction
• Elderly people
• Smart-walker
• +۳ مورد دیگر

Ludovic Saint-Bauzel is a lecturer at Sorbonne University and head of the IRIS team at the Institute of Intelligent Systems and Robotics (ISIR). His work focuses on improving physical human-robot interaction for individuals with autonomy loss through disability or aging. Specializes in computational models of disabilities Develops user intent detection through sensor fusion Active in IFRH and Fedrha federations IEEE and True Life Lab member Research interests His research centers on Human-robot physical interaction with applications in Elderly care , Smart-walker development, and Walking exoskeleton systems. Key methodologies include: Sensor fusion (depth cameras, force sensors, IMUs) Adaptive robot control systems Pathological movement modeling Motor intent prediction algorithms Article trends show consistent focus on haptic communication (6/15), assistive robotics (9/15), and sensorimotor interaction (11/15) across 2013-2022 publications. Laboratory involvement : Leads the IRIS team at ISIR, part of Fedrha (Federation for Research on Disability and Autonomy) with 50+ research teams.

• Materials Science
• Computational Chemistry
• Nanoalloys Thermodynamics
• +۵ مورد دیگر

Jérôme Creuze is a Professor of Chemistry at Université Paris-Saclay, affiliated with the Institute of Molecular Chemistry and Materials of Orsay (ICMMO - UMR 8182) under the SP2M unit. He co-leads the Synthesis, Properties and Modeling of Materials team, focusing on thermodynamics of metallic nanoalloys , environmental effects on alloy surfaces , and metal-on-metal heteroepitaxy using atomic-scale simulations. Research Themes : Nanoalloys Thermodynamics, Surface Segregation, Heteroepitaxy, Ab Initio Modeling, Defects and Diffusion in Solids Recent Publications : 15+ studies on nanoalloy surface energy, dislocation loops, Vegard’s rule deviations, and environmental impacts on alloys. Collaborations : Partners include teams from ONERA (Châtillon), CEA Saclay, IWD-SINAP (Shanghai), and universities in Marseille, Montpellier, and Paris. Teaching : Coordinates CPGE L'Essouriau chemistry program and leads courses on thermodynamics, defects, and diffusion in crystalline solids at Master's level.

Thi Phuong Khanh Nguyenمشاهده پروفایل

پژوهشگر

• Prognostics and Health Management (PHM)
• Predictive Maintenance
• Condition-Based Maintenance
• +۸ مورد دیگر

Dr. Thi Phuong Khanh Nguyen is a researcher at the Ecole Nationale d'Ingénieurs de Tarbes (ENIT) , affiliated with the College of Engineering and Department of Systems . Her work focuses on Prognostics and Health Management (PHM) , predictive maintenance, and industrial data analytics, combining machine learning with physics-informed modeling to address uncertainty in system degradation. Teaching: Mathematics for engineers, Probability, Statistics, Operating safety Research: Health indicators, diagnostics, prognostics, multimodal data fusion Methods: Data mining, physical and data-driven models, decision support systems Tools: FAST, Petri nets, UML, HMM, RNN, CNN, Transformer architectures Her recent publications highlight advancements in explainable AI , physics-informed neural networks , and multimodal learning for fault detection, battery RUL prediction, and robotic inverse dynamics. She also explores blockchain and federated learning for decentralized prognostics.

• Bayesian Statistics
• Bayesian Machine Learning
• Environmental Science
• +۸ مورد دیگر

Julyan Arbel is an Associate Researcher (chargé de recherche) at Inria Grenoble in the Statify team and a member of Laboratoire Jean Kuntzmann at Université Grenoble Alpes. He holds a PhD in Applied Mathematics from Université Paris-Dauphine and graduated from École Polytechnique and ENSAE. He defended his Habilitation à Diriger des Recherches (HDR) in 2019. Research Interests His work spans Bayesian Statistics (approximations, computation, nonparametrics, extreme value theory, objective Bayes) and Bayesian Machine Learning (Bayesian neural networks, variational inference), with applications in environmental science and neuroscience. Key focus areas include: Statistical methods for ecological modeling and species distributions Integration of biotic interactions in predictive frameworks Bayesian approaches to machine learning challenges Publications Overview Recent articles (2022-2025) demonstrate a strong focus on statistical ecology, including species distribution modeling, biotic interactions, community ecology, and biodiversity conservation. Methodological innovations center on joint species distribution models and Bayesian computational techniques applied to environmental datasets. Academic Service Associate Editor: Statistics and Computing (2025–present) Associate Editor: Bayesian Analysis (2019–present) Associate Editor: Australian and New Zealand Journal of Statistics (2019–present) Associate Editor: Statistics & Probability Letters (2019–present) Associate Editor: Statistical Methods & Applications (2024–present) Former Associate Editor: Computational Statistics & Data Analysis (2020–2023) Students Supervised PhD students: Giovanni Poggiato Daria Bystrova Minh Tri Lê Théo Moins Teams Statify Research Team, Inria Grenoble Laboratoire Jean Kuntzmann, Université Grenoble Alpes

Tâm Le Minhمشاهده پروفایل

پژوهشگر

• Statistics
• Ecological Networks
• Nonparametric Statistics
• +۲ مورد دیگر

Tâm Le Minh is a postdoctoral researcher in statistics at Inria Grenoble Rhône-Alpes, focusing on model-based statistical methods and nonparametric models with latent variables. His work bridges statistical theory with applications in life sciences, particularly ecological network analysis. PhD in Applied Mathematics (2023), Université Paris-Saclay Master in Applied Mathematics (2020), Institut Polytechnique de Paris Background in aeronautics and software engineering His research spans U-statistics, row-column exchangeable matrices, and Bayesian nonparametric approaches, with applications to ecological networks and high-throughput sequencing data. Recent publications emphasize bipartite network analysis and variational annealing for optimization. He has presented at major conferences including the International Conference on Bayesian Nonparametrics, Bernoulli-IMS World Congress, and ISBA World Meeting. His work appears in journals like ESAIM: Probability and Statistics and the Electronic Journal of Statistics.