Jaouad Mourtada

Jaouad Mourtada is an Assistant Professor in the Department of Statistics at ENSAE Paris, a founding member of the Institut Polytechnique de Paris, and a permanent member of CREST (Center for Research in Economics and Statistics). Since September 2020 he has held this faculty position, after completing a post-doctoral fellowship at the University of Genoa and earning his PhD from École Polytechnique.

Education

• PhD in Statistics, École Polytechnique (2016–2019)
• MSc in Mathematics, "Probability and Random Models", Université Pierre et Marie Curie (2016)
• MSc in Mathematics, "Fundamental Mathematics", Université Pierre et Marie Curie (2015)
• BSc in Mathematics, Université Pierre et Marie Curie & École Normale Supérieure (2013)
• Student at École Normale Supérieure (2012–2016)

Research Interests

Mourtada’s work lies at the intersection of statistics and learning theory, with a focus on understanding the fundamental complexity of prediction and estimation tasks. His interests span:

• High-dimensional statistics and minimax theory
• Statistical learning theory and generalization bounds
• Online learning, regret minimization, and expert aggregation
• Density estimation and robust statistics
• Random forests, kernel methods, and convex optimization

Research Output Trends

Across more than fifteen recent publications, Mourtada has systematically advanced the understanding of statistical and computational limits in learning. His contributions range from exact minimax analyses of linear least squares and novel robust regression guarantees to refined PAC-Bayesian bounds for aggregation and sharp asymptotics for ridge regression. A recurrent theme is the development of estimators that achieve optimal or near-optimal rates while remaining computationally tractable and adaptive to unknown parameters.

Scientific Awards & Recognition

While the provided materials do not list specific awards, his sustained publication record in top venues such as Annals of Statistics, Journal of Machine Learning Research, Journal of the European Mathematical Society, and leading ML conferences (NeurIPS, COLT, AISTATS) attests to significant peer recognition.

Teaching & Mentoring

Mourtada has extensive teaching experience at both undergraduate and graduate levels, covering probability, statistics, and machine learning. Courses delivered include:

• Statistical Learning Theory (M2 Data Science, École polytechnique & ENSAE)
• Probability Theory (ENSAE)
• Python for Probability, Statistics, and Machine Learning (École polytechnique)
• Optimization for Data Science (M2 Data Science, École polytechnique)

Laboratories & Collaborations

He is affiliated with CREST/ENSAE and has previously collaborated with the Laboratory for Computational and Statistical Learning at the University of Genoa, the Center for Applied Mathematics (CMAP) at École Polytechnique, and maintains ongoing research ties with international scholars in statistical learning and optimization.