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).
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.
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.
Sylvie Méléard is a Professor of Probability at École Polytechnique, where she chairs the Department of Applied Mathematics and leads the Modeling for the Evolution of Living Things team. Her career spans roles at the University of Paris X and École Polytechnique since 2006. Ecole Normale Supérieure de Fontenay-aux-Roses (sciences) University of Paris VI, Ph.D. in 1984 Her research focuses on probability theory, stochastic processes, and their applications in mathematical modeling. She has authored foundational textbooks in analysis and probability, bridging theoretical and applied mathematics education. She currently leads interdisciplinary research initiatives in evolutionary biology modeling and contributes to pedagogical frameworks in higher mathematics education.
Kevin MICHENEAU is a Teacher-Researcher at CESI School of Engineering, affiliated with the LINEACT research laboratory in Guipavas, France. His work bridges building energy systems and experimental particle physics, focusing on data-driven optimization of smart buildings and dark matter detection. Education: PhD in Subatomic Physics, University of Nantes (2018): "Study of residual electrons in the XENON100 experiment" Master's degree in Research in Subatomic Physics, University of Nantes (2014) Research Focus: Dr. MICHENEAU develops advanced models for building energy performance with emphasis on occupancy behavior impact and smart control systems . His methodology combines sensor fusion and multi-objective optimization to balance energy efficiency with occupant comfort. Previously, he contributed to XENON dark matter experiments through signal reconstruction and background modeling. Publication Evolution: His research trajectory shows a strategic pivot from particle physics (2017-2019) to building energy systems (2024), applying rigorous data analysis techniques across domains. The 2024 MPC optimization study demonstrates transferable methodology from high-precision physics to sustainable engineering. Mentorship: Currently supervising PhD candidate BOURGOIN on "Towards modeling the impact of occupancy on the energy behavior of smart buildings" (2023-2026). Research Ecosystem: Member of the "Engineering and Digital Tools" team within LINEACT, teaching Computer Science, Mechanics, and Physics across preparatory and engineering cycles while contributing to PhD training at University of Nantes.
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.
Stanislas Dehaene is a Professor of Experimental Cognitive Psychology at the Collège de France. He holds a doctorate in cognitive psychology from EHESS (1989) and conducted early training in mathematics at École Normale Supérieure (ENS) in 1984. His research focuses on the neural bases of numerical cognition, reading, and consciousness, employing cognitive psychology experiments and advanced neuroimaging techniques. He pioneered studies on the parietal lobe’s role in arithmetic and demonstrated how symbolic systems like language and mathematics evolved from core "neuronal recycling" processes. Education: Bachelor’s in Mathematics, ENS (1984) Master’s in Mathematics, UPMC (1985) PhD in Psychology, EHESS (1989) Research Interests: Dehaene explores the brain’s mechanisms for processing numbers, reading, and conscious thought. His theories include the global neuronal workspace model of consciousness and the neuronal recycling hypothesis explaining how evolution repurposes neural circuits for symbolic thought. He also investigates educational applications of cognitive science, such as improving math and literacy instruction. Key Awards: Chevalier de la Légion d'Honneur (2011) Inserm Grand Prix (2013) Jean Rostand Prize (1997) American McDonnell Foundation Centennial Fellowship Labs/Teams: Leads the Cognitive Neuroimaging Unit (INSERM-CEA) and collaborates with neuroimaging centers like NeuroSpin (Saclay). His work integrates fMRI, MEG, and EEG to study human and primate cognition.
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 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 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 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.
Christelle Lopes is a prominent academic specializing in ecotoxicology and environmental risk assessment. Her work focuses on bioaccumulation mechanisms, toxicokinetic modeling, and the development of biomonitoring tools using sentinel species such as Gammarus fossarum and zebra mussels. She has contributed to advancing methodologies for assessing contaminants in freshwater ecosystems, including heavy metals, pesticides, and emerging pollutants. Her research integrates mathematical modeling, Bayesian inference, and field studies to refine risk assessment frameworks and inform environmental policy. Key contributions include the development of the rbioacc R-package for analyzing toxicokinetic data, the refinement of integrative biomarker response thresholds (IBR-T), and studies on cadmium and mercury dynamics in aquatic organisms. Her work bridges laboratory experiments and large-scale field applications, emphasizing the importance of multispecies approaches in biomonitoring. Lopes collaborates with institutions like CNRS and has published extensively in journals such as Environmental Pollution , Environment International , and Ecotoxicology and Environmental Safety . Her recent focus includes climate change impacts on metal metabolism in marine cephalopods and improving diagnostic tools for freshwater genotoxicity using the comet assay. Lopes’ research highlights the translational application of models like MOSAIC and the importance of standardized protocols for biomarker thresholds in environmental surveillance.
Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur , Nice, France, and holds a Chair in Artificial Intelligence. He serves as Director of the Institut 3IA Côte d’Azur and leads the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. He is an associate editor for The Annals of Applied Statistics and founded the Statlearn workshops . Research Interests: Statistical learning in high dimensions Learning on networks and functional data Deep latent variable models Adaptive learning with uncertain labels Applications in Medicine, Image Analysis, Astrophysics, and Humanities Notable Contributions: Developed multiple R packages including HDclassif , FisherEM , and FunLBM . Created the Linkage.fr platform for network analysis with textual edges. PhD Students: Current: Seydina Niang (Deep Generative Models), Kilian Burgi (Marine Diversity Monitoring), Baptiste Pouthier (Multimodal Learning) Former: Giulia Marchello (Dynamic Networks), Rémi Boutin (Network Analysis), Dingge Liang (Recommender Systems), Nicolas Jouvin (Latent Variable Models), Alexandre Saint-Dizier (Image Aggregation), Warith Harchaoui (Optimal Transport), Pierre-Alexandre Mattei (Sparse Clustering), Rawya Zreik (Temporal Networks), Anastasios Bellas (Anomaly Detection), Camille Brunet (Sparse Clustering) Contact: Email: charles.bouveyron@univ-cotedazur.fr / charles.bouveyron@inria.fr Postal: Equipe Maasai, Inria Sophia Antipolis, 2004 route des Lucioles, 06902 France
Alain EHRLACHER is a Professor and Research Director at the Navier Laboratory, part of École des Ponts ParisTech (ENPC). He holds a Doctorat d’Etat from the University of Paris VI (1985) and has been active in research since 1978. His primary affiliations include the Mechanical and Materials Engineering department, focusing on interdisciplinary research in mechanics, materials science, and civil engineering. Education: Graduate of École Polytechnique and École nationale des ponts et chaussées. Completed Doctorat d’Etat in 1985 at University of Paris VI. Research Interests: Fracture mechanics and material failure analysis. Concrete technology and durability (e.g., alkali-silica reaction, thermal behavior). Thermal analysis of materials under extreme conditions. Computational modeling of grain growth and microstructural evolution. Mechanical behavior of composites and laminated materials. Key Contributions: Pioneered studies on alkali-silica reaction (ASR) in concrete, integrating fracture mechanics and poromechanics. Developed models for grain growth kinetics using Bayesian techniques and thermomechanical principles. Advanced computational methods for simulating coiling processes and structural fatigue in engineering systems. Lab & Affiliations: Active researcher at the Navier Laboratory, a leading center for mechanics and civil engineering research in France.
Ole Winther is a Professor at the Department of Biology , University of Copenhagen , and a Professor at the Section for Cognitive Systems , DTU Compute , Technical University of Denmark . He specializes in high-dimensional biological data analysis, machine learning, and data science. Academic Affiliations: University of Copenhagen (Computational and RNA Biology), DTU Compute (Cognitive Systems) Leadership Roles: Chief Research Officer at raffle.ai, CTO at FindZebra, Head of ELLIS Unit Copenhagen, co-PI of Machine Learning for Life Science (MLLS) Center Research Interests Bioinformatics: Protein sequence analysis (SignalP, DeepLoc, DeepTMHMM), single-cell gene expression, RNA/DNA level regulation Latent Variable Models: Variational autoencoders, diffusion models, deep generative models, structured mean-field, matrix factorization Natural Language Processing: Enterprise search applications, medical context LLMs, joint retrieval/generation models AI for Science: Surrogate models for physical models (density functional theory), fast scientific dataset generation Publication Trends His research focuses on deep learning methods for bioinformatics and AI applications, with a strong emphasis on variational inference, generative models, and probabilistic architectures. Publications span top venues like Bioinformatics , NeurIPS , ICML , and JMLR , addressing both theoretical and applied challenges. Teaching He teaches courses on deep learning at both institutions ( 02456 Deep Learning at DTU and NDAK24002U Deep Learning at University of Copenhagen). Research Group His current group includes PhD/postdoc researchers like Panagiotis Antoniadis, Felix G. Teufel, and Irene R. Rodriguez. Former advisees include Casper Kaae Sønderby, Marco Fraccaro, and Lars Maaløe, many of whom now hold prominent industry/academic positions.
