جست‌وجوی استاد و پژوهشگر

Olivier Cappéمشاهده پروفایل

استاد مدعو

• Machine Learning
• Statistical Signal Processing
• Bayesian Methods
• +۳ مورد دیگر

Olivier Cappé is a CNRS Research Director at the Department of Computer Science of École normale supérieure (DI ENS - CNRS/ENS/Inria) and an Adjunct Professor at Université PSL. He is affiliated with the Centre Sciences des Données (CSD) at ENS and serves as a Chair holder and member of the Executive committee of the Pr[AI]rie-PSAI (Paris School of AI) project. Previously, he served as deputy scientific director at INS2I (2017-2023) and headed the Information Processing and Communication Laboratory (LTCI) from 2013 to 2016. Dr. Cappé's research focuses on statistical signal processing and machine learning. His work spans several areas including Bayesian methods, Markov Chain Monte Carlo, online learning, multi-armed bandit models, and differential privacy for machine learning. Starting in speech and audio processing in the 1990s, he contributed to natural language processing applications in the 2000s, and has focused extensively on online learning and bandit algorithms since 2010. His recent work also addresses privacy issues in machine learning systems. Cappé teaches Reinforcement Learning and Differential Privacy for Machine Learning courses in the IASD master program at Université PSL. His publication record shows consistent output across multiple research areas, with recent work focusing on bandit algorithms, online learning, and privacy-preserving machine learning. His research bridges theoretical foundations with practical applications in digital advertising, recommendation systems, and pandemic response analysis. Grand Prize of the EADS Corporate Foundation (Information Sciences) from the French Academy of Sciences (2013) Co-author of 'Tout comprendre (ou presque) sur l'intelligence artificielle' with Claire Marc Dr. Cappé holds a Supélec engineering degree (1990) and a doctorate from ENST (currently Télécom ParisTech, 1993). He joined CNRS as a researcher in 1996 and has maintained a productive research career spanning over 30 years, with significant contributions to both theoretical and applied aspects of statistical signal processing and machine learning.

• Bayesian computation
• Monte Carlo methods
• Sequential Monte Carlo
• +۴ مورد دیگر

Nicolas Chopin is a Professor of Data Sciences at ENSAE, Institut Polytechnique de Paris. He joined ENSAE in 2006 after serving as a lecturer at the University of Bristol (2003-2006). He holds a PhD from Université Paris VI (2003) and an HDR (habilitation) earned in 2010. His research centers on Bayesian computation methodologies, including: Sequential Monte Carlo (particle filters) Markov chain Monte Carlo Variational inference Probabilistic Machine Learning He develops computational frameworks for complex statistical inference problems. Analysis of his recent publications (2022-2025) reveals strong emphasis on: Monte Carlo innovations (e.g., waste-free SMC, quasi-Monte Carlo), scalable Bayesian modeling, debiasing techniques for sequential inference, and applications in optimization/bandit problems. Theoretical rigor combined with computational efficiency is a consistent theme. Awards: Savage Award for Best Doctoral Dissertation in Bayesian Statistics (2002)

• Wireless Communications
• Network Security
• Information Theory
• +۷ مورد دیگر

Benoît Geller is a Full Professor at IP Paris (Institut Polytechnique Paris)-ENSTA in the Computer Science and Systems Engineering Unit (U2IS). He heads the Specialized Master on autonomous systems for Defense (ILEMs) and has extensive experience in wireless communications research, particularly focusing on security and safety for defense applications. Accreditation to Lead Research (HDR) in Information Sciences from University of Paris (2004) PhD in wireless communication networks from INPG (1992) Engineering Master in Telecommunications from ENSERB (1988) Professor Geller's research focuses on wireless network security, with expertise in protecting communications against environmental interference and intentional attacks. His work spans from theoretical foundations in information theory and coding to practical applications in underwater acoustic channels, tactical communications, and IoT networks. He has developed innovative approaches using finite fields algebraic error correcting codes, turbo and LDPC codes, cryptography, and Bayesian inference techniques. His publication record shows a consistent focus on underwater communication systems, signal processing techniques, and security mechanisms. The research demonstrates a progression from theoretical foundations to practical implementations, with recent work emphasizing energy efficiency and resistance to jamming in tactical communication scenarios. His work has significant applications for the French Ministry of Defense. Knight of Academic Palms (2018) IEEE Senior member (2010) French Navy Reserve Superior Officer (2006) Professor Geller has led numerous European projects including Mast AIDA, Medea+ INCA, Medea+ MIDAS, Newcom++, Horizon 2020 Bridges, and MarTERA Bioglider. With approximately 100 international publications and 4 pending international patents, his research has made significant contributions to the field of secure wireless communications. He previously served as head of the Multisensor and Information Team at SATIE lab (Ecole Normale Superieure Paris Saclay) before joining ENSTA Paris in 2007-08.

Gilles Blanchardمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Statistical Learning Theory
• +۵ مورد دیگر

Gilles Blanchard is a Professor at Paris-Saclay University , affiliated with the Institute of Mathematics in Orsay. His research focuses on statistical learning theory , multiple testing , kernel methods , and high-dimensional statistics . Key Contributions: Decontamination of mutually contaminated models Novelty detection with semi-supervised learning Non-Gaussian component analysis (NGCA) for dimension reduction Scientific Awards: PhD thesis award from University of Paris 6 (2004) Google Scholar h-index of 49 with over 8,000 citations Recent Research Trends: False Discovery Rate (FDR) control in structured hypothesis testing Conformal prediction for link analysis Adaptive sampling in restless bandits Statistical learning on measure spaces His work bridges theoretical statistics with practical machine learning applications, including genome-wide association studies , persistent homology in topological data analysis , and random feature moments for compressive learning . He is involved in open-source software development like μTOSS for multiple testing standardization and has advised on projects related to flow cytometry and Hadrontherapy applications.

• Minimal Surfaces
• Geometric Analysis
• Conformal Geometry
• +۳ مورد دیگر

Samuel Bronstein is a PostDoctoral Research Fellow at the Max Planck Institute for Mathematics in the Sciences (MPI MiS) in Leipzig, Germany. Previously, he completed his PhD at the Département de Mathématiques et Applications (DMA) of École Normale Supérieure (ENS) in Paris under Nicolas Tholozan and remains a member of the GDR Platon research group in Geometry and Groups. Education: PhD in Mathematics, École Normale Supérieure (completed August 2024) His research centers on geometric structures with expertise in Minimal Surfaces, Geometric Analysis, Conformal Geometry, Higgs Bundles, Higher Kleinian Groups, and Negative Curvature. Work integrates representation theory, dynamical systems, and Teichmüller theory to solve problems in geometric analysis. Analysis of his 2023-2025 publications reveals dominant themes: almost-Fuchsian structures in symmetric spaces, asymptotic Plateau problems, and Anosov representations, with cross-disciplinary contributions to mathematical biology via epidemic modeling. He actively shapes the academic landscape through workshop organization (e.g., 2025 Universal Teichmüller Theory Workshop) and participation in major conferences including IHP Program 'Highrags' (2025) and '20 Years of Anosov Representations' (2024).

Kaniav Kamaryمشاهده پروفایل

پژوهشگر

• Bayesian Inference
• Bayesian Model Choice
• Prior Distribution
• +۳ مورد دیگر

Kaniav Kamary is a researcher specializing in Bayesian inference and mixture distribution modeling, affiliated with the Laboratory of Mathematics and Computer Science for Complexity and Systems. Their work bridges computational statistics, probabilistic modeling, and applied mathematics. Research Interests Kamary's research focuses on Bayesian model choice Non-informative prior development Reparameterization techniques for mixture models Statistical validation of computational methods Scientific Contributions Recent publications address methodological challenges in Bayesian inference and applications in health informatics and environmental modeling.

Emmanuel VAZQUEZمشاهده پروفایل

استاد

• Bayesian Statistics
• Computer Experiments
• Uncertainty Quantification
• +۳ مورد دیگر

Emmanuel Vazquez is a Professor and researcher at CentraleSupélec, specializing in Bayesian design and analysis of computer experiments. He coordinates Data Science projects and leads research in Gaussian processes, uncertainty quantification, and optimization. He holds a PhD (2005) and HDR (2015) from Université Paris-Saclay, and has taught Bayesian statistics at CentraleSupélec. His work focuses on sequential design strategies, multi-fidelity simulations, and risk assessment in engineering and microbiology. He collaborates with institutions like the GdR MascotNum and PGMO, advancing methodologies for efficient simulation-based decision-making. His research includes applications in TBM excavation modeling, renewable energy integration, and food safety through quantitative microbial risk analysis. Education: École Normale Supérieure de Cachan (1997–2001), DEA/M2 in Mathematics (2001), PhD in Applied Mathematics (2005), Habilitation (2015). Research interests span Bayesian optimization, Gaussian process metamodeling, and stepwise uncertainty reduction (SUR) strategies. He develops algorithms for efficient sampling in expensive-to-evaluate systems, with applications to engineering, environmental modeling, and public health. His recent articles emphasize quantile set inversion, risk assessment in food safety, and scalable Gaussian process methods. Collaborations include projects on railway infrastructure digital twins (MINERVE) and multi-year renewable energy planning. He has contributed to open-source tools like the STK toolbox for kriging.

Alexander Tereninمشاهده پروفایل

استاد پژوهشی

• Machine Learning
• Artificial Intelligence
• Decision Theory
• +۷ مورد دیگر

Alexander Terenin is an Assistant Research Professor at Cornell University , specializing in machine learning and artificial intelligence. His work focuses on decision-making under uncertainty, Bayesian optimization, and Gaussian processes, particularly in non-Euclidean spaces. He has contributed to geometric learning, scalable Gaussian process methods, and applications in robotics, plasma science, and legal AI. His research integrates theoretical foundations with practical algorithms, emphasizing principles like the Gittins Index for optimal decision-making. Notable projects include the GeometricKernels software package for manifold learning and the Cambridge Law Corpus for legal AI. His work bridges statistics, geometry, and computer science to address challenges in autonomous systems, energy optimization, and data-driven decision-making. Recent Talks and Contributions: An Adversarial Analysis of Thompson Sampling (INFORMS Applied Probability Society 2025) Cost-aware Bayesian Optimization (NeurIPS 2024) Stochastic Poisson Surface Reconstruction (ICML 2025) Key Research Themes: Bayesian Optimization for multi-objective problems (e.g., plasma-driven energy systems) Geometric Gaussian Processes for robotics and 3D modeling Statistical guarantees for Gaussian processes on manifolds Grants and Collaborations: His work involves interdisciplinary projects with institutions like Carnegie Mellon University, ETH Zürich, and the University of Cambridge, reflecting a global network in AI and statistical 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.

KOUNTOURIS Mariosمشاهده پروفایل

استاد

• Communication Theory
• Machine Learning for Wireless Communications
• Low Latency Networking
• +۳ مورد دیگر

Marios Kountouris is a Professor at EURECOM in the Department of Communication Systems. He holds a Ph.D. in Electrical Engineering from Telecom ParisTech (France), an M.S. from Telecom ParisTech, and a Diploma in Electrical & Computer Engineering from the National Technical University of Athens (Greece). His career includes roles as Principal Scientist at Huawei Technologies (France), Assistant/Associate Professor at CentraleSupélec (France), Adjunct Professor at Yonsei University (South Korea), and Post-doctoral researcher at the University of Texas at Austin (USA). His research focuses on communication theory, machine learning for wireless communications, low latency networking, and stochastic modeling. Notable contributions include work on ultra-reliable low-latency communication (URLLC), network densification, and semantic communication frameworks. Distinctions: IEEE Communication Society Young Author Best Paper Award 2020 2016 IEEE ComSoc Communication Theory Technical Committee Early Achievement Award 2013 IEEE ComSoc Outstanding Young Researcher Award (EMEA Region) Recent research trends emphasize semantic-aware resource allocation, federated learning in wireless networks, and next-generation waveforms like AFDM. His articles explore optimization frameworks, real-time systems, and AI-driven telecom innovations.

• High-dimensional statistics
• Statistical learning theory
• Online learning
• +۳ مورد دیگر

Jaouad Mourtada is an Assistant Professor in the Department of Statistics at ENSAE Paris, a founding member of the Institut Polytechnique de Paris, and a permanent member of CREST (Center for Research in Economics and Statistics). Since September 2020 he has held this faculty position, after completing a post-doctoral fellowship at the University of Genoa and earning his PhD from École Polytechnique. Education PhD in Statistics, École Polytechnique (2016–2019) MSc in Mathematics, "Probability and Random Models", Université Pierre et Marie Curie (2016) MSc in Mathematics, "Fundamental Mathematics", Université Pierre et Marie Curie (2015) BSc in Mathematics, Université Pierre et Marie Curie & École Normale Supérieure (2013) Student at École Normale Supérieure (2012–2016) Research Interests Mourtada’s work lies at the intersection of statistics and learning theory, with a focus on understanding the fundamental complexity of prediction and estimation tasks. His interests span: High-dimensional statistics and minimax theory Statistical learning theory and generalization bounds Online learning, regret minimization, and expert aggregation Density estimation and robust statistics Random forests, kernel methods, and convex optimization Research Output Trends Across more than fifteen recent publications, Mourtada has systematically advanced the understanding of statistical and computational limits in learning. His contributions range from exact minimax analyses of linear least squares and novel robust regression guarantees to refined PAC-Bayesian bounds for aggregation and sharp asymptotics for ridge regression. A recurrent theme is the development of estimators that achieve optimal or near-optimal rates while remaining computationally tractable and adaptive to unknown parameters. Scientific Awards & Recognition While the provided materials do not list specific awards, his sustained publication record in top venues such as Annals of Statistics , Journal of Machine Learning Research , Journal of the European Mathematical Society , and leading ML conferences (NeurIPS, COLT, AISTATS) attests to significant peer recognition. Teaching & Mentoring Mourtada has extensive teaching experience at both undergraduate and graduate levels, covering probability, statistics, and machine learning. Courses delivered include: Statistical Learning Theory (M2 Data Science, École polytechnique & ENSAE) Probability Theory (ENSAE) Python for Probability, Statistics, and Machine Learning (École polytechnique) Optimization for Data Science (M2 Data Science, École polytechnique) Laboratories & Collaborations He is affiliated with CREST/ENSAE and has previously collaborated with the Laboratory for Computational and Statistical Learning at the University of Genoa, the Center for Applied Mathematics (CMAP) at École Polytechnique, and maintains ongoing research ties with international scholars in statistical learning and optimization.

Lefort Jean-Philippeمشاهده پروفایل

مدرس

• Decision Theory
• Ambiguity in Economics
• Game Theory
• +۳ مورد دیگر

Lefort Jean-Philippe serves as a Lecturer at Paris Dauphine University, where he is affiliated with the LEDa research center (Laboratoire d'Economie de Dauphine). His academic work centers on theoretical economics with a specialized focus on decision-making under uncertainty and ambiguity, contributing significantly to the advancement of non-expected utility frameworks. His research spans Decision Theory , Ambiguity Modeling , and Game Theory , particularly examining neo-additive capacities, dynamic rationality, and probabilistic information processing. Key contributions include the development of generalized pricing rules under ambiguity, dynamic extensions of the Ellsberg paradox, and foundational work on belief updating mechanisms. His methodological approach integrates rigorous mathematical economics with experimental validation, yielding insights applicable to financial economics and strategic interaction analysis. Analysis of his publication trajectory (2008-2021) reveals consistent innovation in ambiguity modeling, with increasing focus on dynamic applications and financial contexts. His work demonstrates strong continuity in exploring how agents process probabilistic information under uncertainty, bridging theoretical economics with practical market phenomena through sophisticated non-additive probability frameworks. Lefort maintains active research collaboration within the LEDa center, contributing to its mission of advancing economic theory through interdisciplinary approaches that combine mathematical rigor with behavioral insights.

Josselin Garnierمشاهده پروفایل

استاد

• Waves in Random Media
• Imaging in Complex Media
• Uncertainty Quantification
• +۶ مورد دیگر

Josselin Garnier is a Professor at Ecole Polytechnique, France, affiliated with the Center for Applied Mathematics. His research focuses on wave propagation in random media, imaging techniques, uncertainty quantification, and inverse problems. He has authored/co-authored multiple influential books including Wave Propagation and Time Reversal in Randomly Layered Media (2007) and Multi-Wave Medical Imaging (2017). His work bridges mathematical theory with applications in optics, seismology, and nuclear engineering. Research interests emphasize stochastic dynamics, nonlinear wave interactions, and Bayesian methods for parameter estimation. He leads a large research group with over 30 PhD students, many working on interdisciplinary projects such as thermalization in optical fibers and seismic fragility analysis. His contributions include developing reduced order modeling approaches for inverse problems and advancing methodologies for uncertainty quantification in nuclear reactor simulations. Key collaborations involve institutions like the French Mathematical Society and the Ciroquo Research & Industry Consortium. His educational contributions include the widely used All-in-one Mathematics textbook series for undergraduate students.

• Electrical Engineering
• Energy Systems Optimization
• Building Energy Efficiency
• +۵ مورد دیگر

Benoit Delinchant is a Professor at Université Grenoble Alpes, affiliated with the National School of Energy, Water and the Environment (Ense3) and the Department of Electrical Engineering and Industrial Computing. He serves as co-head of the MAGE research team (Models, Methods and Methodologies Applied to Electrical Engineering) at G2ELab since 2015 and co-facilitator of the Thematic Group for Complex Systems within GdR SEEDS. His educational leadership includes roles as Director of Studies and co-head of the GREEN thematic program at GS@UGA. Education: 2011: Habilitation to Direct Research (HDR), University of Grenoble 2003: Ph.D. in Electrical Engineering, INP Grenoble 2000: DEA in Electrical Engineering (Master's equivalent), INP Grenoble 2000: Engineering Degree in Electrical Engineering, INP Grenoble His research develops methods for modeling, simulation, and optimization of electrical energy systems, spanning electromagnetic components to multi-energy networks and building efficiency. Key focus areas include: Automatic modeling strategies for energy system design Uncertainty management in complex energy networks Optimization techniques for building-grid interactions Human-in-the-loop control systems His work integrates computational intelligence with practical applications in sustainable urban systems. Recent publications emphasize data-driven approaches to energy flexibility, carbon-aware systems, and hybrid modeling. Dominant themes include machine learning applications for load forecasting, lifecycle assessment methodologies, and experimental validation of demand response strategies. Cross-cutting focus on sustainability links building-scale optimizations with grid-level decarbonization challenges. Scientific Awards: No awards mentioned in source materials. Academic Supervision & Projects: Supervised 19 completed PhD theses and 6 ongoing doctoral candidates Led 4 study engineers, 6 post-docs, and managed 28+ Master's internships Major projects: Singapore NRF CREATE DesCartes (2021-26), PEPR TASE HyMES (2022-27), H2020 COLLECTiEF (2021-25), Grenoble IDEX OTE Observatory (2022-25), ANR COSIMPHI/INTENS/PRECCISION Leads research within G2ELab's MAGE team, collaborating with ENEDIS, EDF, ENGIE, Schneider Electric, CEA, and CSTB. Coordinates international partnerships including Brazil (UFSC) and Vietnam (USTH) for energy transition research.

Dirk Slockمشاهده پروفایل

استاد

• Wireless Communications
• Signal Processing
• MIMO Systems
• +۶ مورد دیگر

Dirk Slock is a Professor at EURECOM's Communication Systems department. His research focuses on advanced signal processing for wireless communications, including transmitter/receiver design for 4G/5G systems, Massive MIMO, stochastic geometry, and audio signal processing. He has contributed to areas like interference management, compressive sensing, and Bayesian methods. Slock teaches courses on statistical signal processing and wireless communication techniques. His notable awards include IEEE Fellow (2006) and EURASIP Fellow (2015). Collaborations with students like Christo Kurisummoottil Thomas have yielded Best Student Paper Awards at SPAWC 2018. His work addresses challenges in cell-free MIMO, semi-blind channel estimation, and secure communication systems. Recent research trends explore ultra-massive MIMO signal detection, dynamic channel prediction with tensor methods, and cell-free network optimization. His publications span 639 entries, emphasizing practical implementations of theoretical signal processing advancements.