NDAOUD Mohamed

I have received a PhD in theoretical statistics, under the supervision of A.B. Tsybakov. Prior to that I graduated from Ecole Polytechnique majoring in applied mathematics.

My research interests are in high dimensional probability and statistics. In particular, I have worked during my PhD on variable selection, estimation and community detection in the high dimensional setting. I am also interested in robust statistics, stochastic processes, harmonic analysis, random matrix theory and spiked models.

• 2019 : Doctorate in Mathematical Statistics (University Paris-Saclay, France)
• 2016 : Master of Science, Finance (Université Pierre et Marie Curie (UPMC), France)
• 2015 : Master of Engineering, Mathematics (École Polytechnique, France)

Full-time academic appointments

• 2021 - Present : Assistant Professor (ESSEC Business School, France)

Other Academic Appointments

• 2021 - 2025 : Chaired Professor « Data Science » (ESSEC Business School, France)

• 2020 - 2021 : Zumberge Individual Award 2020 (University of South California (USC), United States of America)

• 2020 - 2021 : IMS New Researcher Travel Award
• 2019 : Best Student Paper Award

• NDAOUD, M., SIGALA, S. and TSYBAKOV, A. (2022). Improved clustering algorithms for the Bipartite Stochastic Block Model. IEEE Transactions on Information Theory, 68(3), pp. 1960-1975.
• NDAOUD, M. (2022). Sharp optimal recovery in the two Component Gaussian Mixture Model. Annals of Statistics, 50(4), pp. 2096-2126.
• NDAOUD, M. (2022). Harmonic analysis meets stationarity: A general framework for series expansions of special Gaussian processes. Bernoulli: A Journal of Mathematical Statistics and Probability, In press.
• COMMINGES, L., COLLIER, O., NDAOUD, M. and TSYBAKOV, A. (2021). Adaptive robust estimation in sparse vector model. Annals of Statistics, 49(3), pp. 1347-1377.
• MINSKER, S. and NDAOUD, M. (2021). Robust and efficient mean estimation: an approach based on the properties of self-normalized sums. The Electronic Journal of Statistics, 15(2), pp. 6036-6070.
• NDAOUD, M. and TSYBAKOV, A. (2020). Optimal variable selection and adaptive noisy Compressed Sensing. IEEE Transactions on Information Theory, 66(4), pp. 2517-2532.
• BUTUCEA, C., NDAOUD, M., STEPANOVA, N. and TSYBAKOV, A.B. (2018). Variable selection with Hamming loss. Annals of Statistics, 46(5), pp. 1837-1875.

• NDAOUD, M. (2019). Interplay of minimax estimation and minimax support recovery under sparsity. In: Algorithmic Learning Theory (ALT). Proceedings of Machine Learning Research.

• MINSKER, S., NDAOUD, M. and SHEN, Y. (2021). Minimax Supervised Clustering in the Anisotropic Gaussian Mixture Model: interpolation is all you need. In: 2021 Mathematical Statistics and Learning. Barcelona.

• 2021 Analysis of Variance and Design ( University of South California (USC) United States of America)
• 2020 Foundations of Statistical Learning Theory ( University of South California (USC) United States of America)
• 2019 Statistical Inference and Data Analysis ( University of South California (USC) United States of America)