جست‌وجوی استاد و پژوهشگر

Didier Theilliolمشاهده پروفایل

استاد

• Fault Tolerant Control
• Control Engineering
• Aerospace Systems
• +۳ مورد دیگر

Didier Theilliol is a Professor of Control Engineering at the University of Lorraine, France, since 2004. He holds a Ph.D. in Control Engineering from Nancy-University (1993). He was awarded the CAS Visiting Professorship by the Chinese Academy of Sciences (CAS) in 2012, during which he collaborated with the Shenyang Institute of Automation (SIA) on flight control and fault-tolerant systems. His research focuses on model-based fault diagnosis (FDI), fault-tolerant control (FTC) for complex systems, and reliability analysis, with applications in aerospace, industrial automation, and robotics. Education: Ph.D. in Control Engineering (Nancy-University, 1993). Research interests include advanced control strategies for linear and nonlinear systems, multi-agent coordination, and safety-critical applications. His work integrates theoretical advancements with practical implementations across industries such as steel production, wastewater treatment, and aerospace. Notable contributions include methodologies for degradation management, distributed observer design, and health-aware control. Recent article trends emphasize fault-tolerant control in multi-agent systems, reinforcement learning for safety-critical tasks, and integration of physics-informed neural networks for system modeling. His publications span topics from model-based diagnostics to real-world applications in UAVs and propulsion systems. Awards: CAS Visiting Professorships for Senior International Scientists (2012). Collaborations include co-working with SIA’s rotorcraft UAV project team and leading European R&D initiatives. He serves as Associate Editor for ISA Transactions and Unmanned Systems , and chairs conferences on fault-tolerant control systems. His research also extends to Bayesian networks for system reliability and particle filter-based prognostics in industrial settings. Labs/Teams: Active in the State Key Laboratory of Robotics (visited during his CAS tenure) and coordinates projects within the German-French Institute for Automation and Robotics.

• Fuzzy Logic
• Artificial Intelligence
• Neural Networks
• +۶ مورد دیگر

Professor Mahdi Mahfouf holds a Chair in the School of Electrical and Electronic Engineering at the University of Sheffield. He has held academic roles since 1997, progressing from Lecturer to Professor in 2005. His research focuses on Fuzzy Logic, Control Systems, and their applications in biomedical and industrial contexts. Mahfouf leads the Intelligent Systems Research Laboratory and has contributed over 370 publications, including influential work on fuzzy modeling and predictive control. Education: Ing.Dipl. (Hons) in Control Systems MPhil in Control Systems (University of Sheffield, 1988) PhD in Control Systems (University of Sheffield, 1991) Research Interests: Fuzzy Logic applications, Artificial Intelligence, Neural Networks, Model-Based Predictive Control, Biomedical Engineering (e.g., ICU Decision Support Systems), and Manufacturing Systems (e.g., granulation processes, surface metrology). Key Achievements: Recipient of the IEE Hartree Premium Award (1992) and MEDIPEX Innovation Award. His work integrates fuzzy logic into real-time systems for aviation, healthcare, and robotics. Grants & Labs: Leads the Intelligent Systems Research Lab. Active in collaborative projects with industry (e.g., pharmaceuticals, aerospace). His research bridges theory and practice, emphasizing data-driven solutions for complex systems.

Mohammed Nabil EL KORSOمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Detection and Estimation Theory
• +۵ مورد دیگر

Mohammed Nabil EL KORSO is a Professor at CentraleSupélec, part of the University of Paris-Saclay. He is affiliated with the Laboratoire des Signaux et Systèmes (L2S). His research focuses on statistical signal processing, machine learning, detection/estimation theory, and robust signal processing, with applications in radioastronomy, radar systems, and source localization. He has contributed extensively to methodologies like Kalman filtering, covariance estimation, and array processing. His work emphasizes robust techniques for handling non-Gaussian noise and interference, particularly in radio interferometry and SAR imaging. Key contributions include algorithms for array calibration, RFI mitigation, and subspace estimation. His research also addresses challenges in distributed and adaptive signal processing, with applications to vital signs monitoring and robotic systems. He has published over 50 journal articles and conference papers, focusing on performance bounds (Cramér-Rao, Weiss-Weinstein), Bayesian methods, and practical implementations for large-scale systems. His recent work includes advancements in low-cost interferometric imaging and phase estimation for SAR time series. EL KORSO collaborates with international projects like the Square Kilometre Array (SKA), contributing to technological developments in radio astronomy. He supervises research in signal processing labs and actively participates in academic conferences such as EUSIPCO and ICASSP.

Kristof Coussementمشاهده پروفایل

استاد

• Deep Learning
• Machine Learning
• Text Mining
• +۳ مورد دیگر

Kristof Coussement is a Full Professor and Academic Director of the MSc in Big Data Analytics for Business at IÉSEG School of Management. He holds a HDR in Business Administration from University of Paris Dauphine and Ph.D. in Applied Economics from Ghent University. He directs the IESEG Center for Marketing Analytics (ICMA). His research focuses on big data analytics, machine learning applications in marketing, and explainable AI. He develops advanced analytical frameworks for customer behavior prediction, financial forecasting, and algorithmic decision-making. Coussement's recent work shows strong emphasis on ethical AI development, algorithmic bias mitigation, and industry-specific language modeling. His publications increasingly address the intersection of AI technology and business decision-making processes.

Frédéric BERTRANDمشاهده پروفایل

استاد

• Statistical Modelling
• Statistical Learning
• Artificial Intelligence
• +۵ مورد دیگر

Frédéric BERTRAND is a Full Professor at the Université de Technologie de Troyes (UTT), part of the Computer Laboratory and Digital Society (LIST3N). He previously held positions at the Université de Strasbourg and Institut de Recherche Mathématique Avancée (IRMA). His academic qualifications include a habilitation to direct research (HDR) from the Université de Strasbourg, a Ph.D. from Université Louis Pasteur, and membership in the École Normale Supérieure de Lyon. He specializes in statistical modeling, machine learning, and artificial intelligence, with applications in bioinformatics, biostatistics, and medical data analysis. Roles and Affiliations: Full Professor at UTT Director of the Mastère Spécialisé in Big Data Analytical and Decisional Aspects Responsible for the OSS ED361 SPI specialty Responsible for the PEA Impact program Member of the LIST3N laboratory Research Interests: His work focuses on statistical and machine learning methods applied to complex systems, including bioinformatics, medical data analysis, and process mining. Key areas include Bayesian networks, partial least squares regression (PLS), variable selection algorithms, and handling missing data in large-scale datasets. He has developed multiple R packages, such as selectBoost , bootPLS , and Patterns , which are widely used in statistical modeling and data analysis. Publications and Contributions: He has authored/co-authored over 38 scientific articles, 14 books, and 14 R packages. His research emphasizes methodological advancements in statistics and their practical applications in biology, healthcare, and industry. Notable contributions include developing algorithms for high-dimensional data analysis and collaborative projects on cancer genomics and proteomics. Teaching: He teaches advanced courses in statistics, mathematics, and data analysis at the graduate and postgraduate levels, including modules on uncertainty analysis and complex systems at UTT and the Université de Strasbourg.

• Econometrics
• Machine Learning
• Artificial Intelligence
• +۸ مورد دیگر

Anna Simoni is a Senior Researcher at CNRS/CREST and Professor of Econometrics and Statistics at ENSAE and École Polytechnique. She is a CNRS Research Fellow and Fellow of Hi! Paris and Institut Louis Bachelier. Her research spans econometrics, machine learning, and AI, focusing on high-dimensional models and Bayesian inference. Education: PhD in Economics, Toulouse School of Economics (2009) Habilitation à Diriger de Recherche (HDR), Toulouse School of Economics (2017) Research Interests: Her work integrates econometrics with machine learning to develop statistical methods for big data, including Google search data for macroeconomic forecasting and causal inference with minimal assumptions. Grants and Awards: She received the CNRS Bronze Medal in 2019 and leads the ANR-funded project "Moment Conditions Models and Bayesian Inference for Policy Evaluation" (2021-2026).

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +۵ مورد دیگر

Alain Durmus is a Professor at École Polytechnique, affiliated with the CMAP (Applied Mathematics Department). His research focuses on computational statistics, machine learning, and stochastic methods for Bayesian inference and generative models. Key areas include Monte Carlo methods, stochastic optimization, and Riemannian geometry in optimization. He has contributed to theoretical advancements in Markov processes and their applications in sampling algorithms. Affiliations: École Polytechnique (CMAP), ENS Paris-Saclay (teaching roles), and other academic collaborations. Teaching: Courses on mathematical statistics, stochastic methods, probability theory, and optimization at ENS Paris-Saclay and École Polytechnique. Research Interests: Development of Monte Carlo algorithms, stochastic approximation schemes, and theoretical analysis of Markov chain convergence. Specializes in Bayesian statistics, generative models, and high-dimensional inference. His work bridges theory and application, with notable contributions to Langevin dynamics, nonreversible MCMC methods, and federated learning. He has received a Best student paper award at ICASSP 2020 for collaborative research on Bayesian computation using Sliced-Wasserstein distances. Alain’s publications span prestigious journals like the Annals of Applied Probability and SIAM, with a focus on algorithmic innovation and rigorous mathematical foundations.

• Production Systems Engineering
• Supply Chain Design and Optimization
• Operational Research
• +۳ مورد دیگر

Professor Alexandre Dolgui is the Director of the Department of Automation, Production, and Computer Science (DAPI) at IMT Atlantique, France. A Fellow of IISE and Highly Cited Researcher (Clarivate), he serves as Editor-in-Chief of the International Journal of Production Research. His expertise spans production systems, supply chain engineering, and operational research, with over 360 journal articles and 37 books. Education: HDR (University of Technology of Compiègne, 2000), PhD in Cybernetics (Academy of Science of Belarus, 1990) Research focuses on production system design , supply chain resilience , and discrete optimization , addressing challenges in human-robot collaboration, Industry 5.0, and yield uncertainty. His work integrates machine learning with stochastic programming for adaptive manufacturing solutions. Key contributions include constraint programming models for assembly lines, robust optimization frameworks for biorefineries, and digital twin methodologies for supply chain risk management. He has received multiple best paper awards and led international research projects like DREAMS and DISC. Scientific leadership: Board member of IFPR, Senior advisor to Chinese Academy of Sciences, founder of EVI European Virtual Institute

François Desbouvriesمشاهده پروفایل

استاد

• Statistical Signal Processing
• Data Science
• Bayesian Statistics
• +۳ مورد دیگر

Prof. François Desbouvries is a Full Professor at Telecom SudParis and Director of the SAMOVAR lab, part of the Institut Polytechnique de Paris. He holds degrees from Telecom Paris and a HDR from Marne la Vallée University. His expertise spans statistical signal processing, Bayesian statistics, and data science, with a focus on hidden data models and Monte Carlo methods. He has held leadership roles including heading the TIPIC team and serving as an IEEE Senior Member since 2007. His research contributions include over 150 publications in top journals and conferences, with recent advancements in Bayesian filtering and machine learning integration. He actively contributes to academic governance through roles in ANR committees and the GdR ISIS network. Education: Engineer Degree in Telecommunications (1987, Telecom Paris), Ph.D. in Signal and Image Processing (1991, Telecom Paris), and Habilitation à Diriger des Recherches (HDR, 2001, Marne la Vallée University). Research interests include sequential Monte Carlo methods, variational inference, and applications in signal processing. Recent work explores Bayesian classification and neural network modeling comparisons. His articles emphasize algorithmic efficiency and theoretical rigor in dynamic systems. Leadership: SAMOVAR lab director (2020–present), former head of TIPIC team (2011–2019), and member of national scientific committees like ANR and GdR ISIS. Grants secured through collaborative projects in signal/image processing and data science. Labs/Teams: SAMOVAR lab (specializing in ICT) and former leadership of the TIPIC team, fostering interdisciplinary research in signal processing and communication systems.

• Computational Medicine
• Medical Image Analysis
• Cardiac Modeling
• +۱۲ مورد دیگر

Hervé Delingette is a Research Director at Inria, a leading national research institute in France, where he is a key member of the Asclepios research team. He also holds a Chair at the 3IA Côte d'Azur Institute and leads the CardioSense3D Large Initiative Action, focusing on advanced modeling of the heart's electro-mechanical behavior. His work bridges artificial intelligence, medical imaging, and computational biology. His research interests are centered on computational medicine and include: Image Segmentation Surface Regularization and Simplex Mesh Surgery Simulation Cardiac and Brain Modeling Joint biological and imaging biomarkers in oncology Unsupervised deep learning for medical data Uncertainty quantification and sparse Bayesian feature selection His recent work emphasizes integrating imaging and biological data—particularly in lung cancer—to improve diagnosis and treatment planning by addressing confounding factors and leveraging AI. Although no specific publications or awards are listed in the source, his leadership in major research initiatives underscores his significant contributions to AI in medicine. He mentors researchers within his team and leads interdisciplinary projects funded through Inria and the 3IA Institute. He is based at Inria Sophia Antipolis and actively contributes to France's national AI for health strategy.

Louis Duvivierمشاهده پروفایل

مدرس ارشد

• Network modeling
• Graph analysis
• Community detection
• +۹ مورد دیگر

Louis Duvivier is a Senior Lecturer at the University of Grenoble Alpes since October 2024, affiliated with the Science Department Drôme Ardèche. He is associated with the Coordination, Cooperation & Control of Complex Systems (CO4SYS) team within the Systems Design and Integration Laboratory (LCIS). Former ATER at École Centrale de Lyon (2023–2024) Postdoctoral Fellow at ENS Lyon (2022–2023) ATER at Institute of Financial and Insurance Science (2021–2022) His research focuses on network modeling , graph analysis , and statistical inference , particularly in stochastic block models , community detection , and temporal graph analysis . His recent work explores probabilistic validation techniques and geometric interpretations of network structures. Publications demonstrate expertise in Bayesian inference , model selection , and sensor network estimation . Earlier contributions to distributed estimation and link prediction date back to 2019. He teaches courses in complementary computing , algorithms , numerical analysis , and statistics at both bachelor's and master's levels.

François Bachocمشاهده پروفایل

استادیار

• Statistics
• Machine Learning
• Mathematical Statistics
• +۳ مورد دیگر

François Bachoc is an Assistant Professor at the Toulouse Mathematics Institute and the University Paul Sabatier, where he has held a tenured position since 2015. He is a Junior Member of the Institut universitaire de France (IUF) (2024–2029). His research focuses on Statistics, Machine Learning, and Gaussian Processes , with applications in industrial and interdisciplinary domains. He earned his Ph.D. in Statistics from the CEA and Université Paris VII (2013), followed by a postdoctoral position at the University of Vienna (2013–2015). He completed a Habilitation (HDR) at University Paul Sabatier in 2018. His work emphasizes uncertainty quantification, Bayesian methods, and optimal design of experiments. Bachoc leads several funded projects, including the ANR Project GAP (€205k) and the AI Chair UQPhysAI (€350k). He teaches courses on asymptotic statistics, machine learning, and Gaussian processes at the graduate level. His contributions span theoretical advancements and practical applications in coastal flood modeling, sensitivity analysis, and computational statistics.

Nicolas Jouvinمشاهده پروفایل

پژوهشگر

• Model based clustering
• Dimension reduction
• Variational inference
• +۴ مورد دیگر

Nicolas Jouvin is an INRAE researcher at the MIA-Paris laboratory specializing in applied mathematics and statistics. He is based at AgroParisTech Saclay (Bureau E4.512, Bâtiment E, 22 place de l'agronomie, 91120 Palaiseau) and works within the MIA-Paris-Saclay research unit. His educational background includes a PhD in Applied Mathematics (2017-2020) from Université Paris 1 Panthéon-Sorbonne and an MVA degree (2016) from ENS Paris Saclay. His doctoral research focused on high-dimensional data and graph clustering with discrete latent variable models under the supervision of Pr. Pierre Latouche, Pr. Charles Bouveyron, and Dr. Alain Livartowski, with collaboration at Institut Curie. Jouvin's research centers on probabilistic models for unsupervised learning, particularly model-based clustering and dimension reduction techniques. His work incorporates variational inference, sparse regularisation, and high-dimensional statistics with applications to medical data analysis. He has developed methodologies for hierarchical clustering using discrete latent variable models and the integrated classification likelihood criterion. His publications demonstrate expertise in developing algorithms for clustering count data through multinomial PCA mixtures and discriminative Gaussian subspace clustering using Bayesian approaches. The Greed R package, which implements some of his methodological contributions, was developed in collaboration with Etienne Côme. Charged de cours for Master 2 Data Science (Evry) - Unsupervised learning Master 2 data science (Evry) - Introduction à Python Master 1 data science (Evry) - Analyse des données TD de M1 MAEF Python TD de L3 MIAGE Technique de calcul TD de L1 MIASHS

Xuefei LUمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Uncertainty Quantification
• Big Data Problems
• +۹ مورد دیگر

Xuefei LU is an Associate Professor at SKEMA Business School (France), affiliated with the SKEMA Center for Analytics and Management Science and the Digitalization Research Center. Her academic journey includes a Ph.D. in Statistics from Bocconi University (Italy) and an MSc in Analytics from the University of Manchester (UK). Prior roles include Assistant Professorships at SKEMA Business School and the University of Edinburgh Business School, along with a postdoctoral position at Politecnico di Milano (Italy). Her research focuses on Statistical Machine Learning , Uncertainty Quantification , and Big Data Problems , with applications in epidemiological modeling, decision analysis, and industrial systems. She has developed frameworks for identifying critical components in complex systems and pioneered methods like the Cohort Shapley value for fairness in SME financing. Her work has been recognized with prestigious awards, including the 2024 INFORMS Data Mining Best Paper Award (Runner-Up) and multiple Excellence in Reviewing Awards from the European Journal of Operational Research. She actively supervises doctoral students in areas like anomaly detection and pandemic modeling. Xuefei LU collaborates across disciplines, contributing to labs focused on Reliability Engineering , Molten Salt Reactors , and Transportation Systems . Her research bridges theoretical advancements with practical applications in public health, infrastructure resilience, and sustainable energy systems.

Thierry Kleinمشاهده پروفایل

استاد

• Probability Theory
• Statistics
• Uncertainty Quantification
• +۴ مورد دیگر

Thierry Klein is a Professor at ENAC (École Nationale de l'Aviation Civile) since November 2016, previously serving as a Lecturer and Senior Lecturer at the Toulouse Institute of Mathematics from 2004 to 2016. His research focuses on probability theory, statistics, and uncertainty quantification, with a strong emphasis on sensitivity analysis (Sobol indices) and applications in aviation, environmental science, and mathematical modeling. He has co-supervised multiple doctoral theses, including those of Nabil Rachdi, Edouard Fournier, and José Bétancourt. His work includes contributions to Gaussian process regression, large deviation principles, and metamodeling techniques for coastal flooding prediction. Education: PhD in Probability and Statistics from the University of Versailles Saint-Quentin-en-Yvelines (2004), HDR (Habilitation à Diriger des Recherches) since 2016. His research interests span concentration inequalities, martingales, and random trees. Research Highlights: Developed methodologies for statistical inference in complex systems, including aircraft trajectory analysis and environmental reconstruction using pollen data. Contributions to open-source tools like the FunGp R package for Gaussian processes. International collaborations with institutions in Vietnam, Cambodia, and Colombia. Grants & Projects: Member of ANR-funded projects Riscope and Pepito . Scientific cooperation with the University of Havana and the Toulouse Mathematics Institute. Teaching & Roles: Responsible for the statistical research axis at ENAC's DEVI group. Contributed to doctoral training committees and administrative roles at ENAC.