معرفی
Mohamed Ndaoud is an Associate Professor of Statistics at ESSEC Business School and a member of the Statistics Department at CREST. Previously, he held a tenure-track position as Assistant Professor in the Department of Mathematics at the University of Southern California (USC) from August 2019. He earned his PhD in theoretical statistics under the supervision of A.B. Tsybakov.
His educational background includes:
- PhD in Theoretical Statistics, supervised by A.B. Tsybakov.
Dr. Ndaoud's research centers on high dimensional statistics, with core contributions in variable selection, estimation, and community detection. He also actively explores robust statistics, stochastic processes, harmonic analysis, random matrix theory, and spiked models, often bridging theoretical statistics with machine learning applications.
Analysis of his publication record (2018-2024) reveals a consistent focus on developing robust and adaptive methods for high-dimensional data. Key themes include outlier-robust regression, clustering algorithms for mixture models, minimax optimal procedures, and harmonic analysis techniques for Gaussian processes. His work frequently introduces non-convex and tuning-free approaches to address challenges in sparse modeling and statistical learning.
Scientific Awards: No awards were listed in the provided information.
Research funding includes support from the CY Initiative of Excellence Paris-Seine. There is no information available regarding student advising or additional grants.
Dr. Ndaoud is an integral member of CREST's Statistics Department and serves on the organizing committee for the Meeting in Mathematical Statistics (2023-2025) in Luminy, France. He actively participates in the international statistics community through workshops and tutorials, such as the upcoming Heidelberg-Paris workshop on mathematical statistics in January 2025.
