Salim BOUZEBDA Enseignant-chercheur

Here is the profile with both editions specified:

Professor Salim Bouzebda

Professor Salim Bouzebda received his doctorate in 2007 from the Université Pierre et Marie Curie (Paris VI), a lineage that situates him within one of the most venerable traditions of French mathematical statistics. His scholarly oeuvre is distinguished both by its analytical depth and by its remarkable breadth, encompassing the inferential treatment of censored and incomplete observations, the asymptotic theory of functional and infinite-dimensional data, multivariate dependence modelling, extreme-value theory, and resampling methodology, as well as the rigorous deployment of artificial-intelligence and statistical-learning algorithms for the interrogation of large-scale and high-dimensional data. The unifying thread across this corpus is a sustained commitment to establishing the probabilistic foundations—limit theorems, invariance principles, and concentration phenomena—upon which modern statistical practice ultimately rests.

He is the author of more than one hundred and seventy peer-reviewed articles in leading international journals, a body of work that bears witness to an enduring and influential presence at the interface of theoretical and applied statistics. In recognition of the sustained impact of this scholarship, he has been ranked among the top 2% of scientists worldwide in both the 2024 and 2025 editions of the Stanford University–Elsevier global research-impact rankings, an acknowledgement of the breadth and consistency of his citation footprint across the discipline. Beyond his prolific scholarship, Professor Bouzebda has placed his expertise at the service of the wider scientific community through his membership on the editorial boards of distinguished periodicals, including Dependence Modeling and Communications in Statistics. He currently holds the chair of Full Professor of Mathematical Statistics and directs the Laboratory of Applied Mathematics (LMAC) at the Université de Technologie de Compiègne, where he continues to extend the frontiers of contemporary statistical science and to mentor a new generation of researchers.

His principal research interests include:

• the statistical analysis of stochastic processes;
• empirical and U-empirical process theory;
• bootstrap and resampling methodology;
• strong invariance (approximation) principles;
• semiparametric and nonparametric inference;
• functional data analysis;
• multivariate analysis and copula-based dependence modelling;
• high-dimensional probability;
• change-point analysis;
• limit theorems for dependent random variables;
• and the theory and applications of statistical machine learning.