My main research is on numerical optimization: theory, algorithms, and applications. I am currently focusing on efficient methods (both deterministic and stochastic) for convex optimization and nonconvex optimization, with applications in machine learning, engineering, and data science.

Currently I am a postdoctoral researcher at university of Helsinki, Finland, working in the department of Mathematics and Statistics with Tuomo Valkonen.

Area of intrerest:

I am broadly interested in exploring the following areas:

• Optimization: Theory and algorithms with a focus on structure exploitation, sparsity, convexity, stochasticity, and low-rank optimization.
• Development of efficient methods for convex and non-convex problem classes, including smooth and nonsmooth formulations.
• Second-order and higher-order optimization methods.
• Online optimization: problems that evolve over time.

I defended my Ph.D thesis in May 2024, supervised by Ion Necoara, within the prestigious Marie Skłodowska-Curie Actions (MSCA) as an Early Stage Researcher, TraDE-OPT H2020 ITN project, as ESR 10. My thesis “Higher-order methods for composite optimization and applications”, focuses on advanced optimization techniques and their applications.

I received a BSc degree in Applied Mathematics from University Cadi Ayyad in 2016. I obtained a MSc degree in Applied mathematics from Faculty of Science and Technology, Morocco in 2018.

Publications:

2025

• Y. Nabou, Lahcen El Bourkhisi, Sebastian U. Stich, Tuomo Valkonen, Monotone and nonmonotone linearized block coordinate descent methods for nonsmooth composite optimization problems, arXiv.
• Y. Nabou, Nonmonotone higher-order Taylor approximation methods for composite problems, arXiv.

2024

• Y. Nabou, Ion Necoara, Regularized higher-order Taylor approximation methods for composite nonlinear least-squares, arXiv.
• Y. Nabou, Ion Necoara, Moving higher-order Taylor approximations method for smooth constrained minimization problems, arXiv.
• Y. Nabou, Francois Glineur, Ion Necoara, Proximal gradient methods with inexact oracle of degree q for composite optimization, Optimization Letters, 2024, (pdf).
• Y. Nabou, Ion Necoara, Efficiency of higher-order algorithms for minimizing composite functions, Computational Optimization and Applications, 2024, (pdf).
• Y. Nabou, Lucian Toma, Ion Necoara, Modified projected Gauss-Newton method for constrained nonlinear least-squares: application to power flow analysis, European Control Conference, 2023, (pdf).

Talks & Conferences:

• October 31, 2024: Higher-Order Methods for Composite Optimization with Application, MOP Research Seminar, Saarland University, Germany (online).
• September 16, 2024: Efficient Algorithms for Composite Problems with Applications, ESAT KU Leuven, Belgium, invited by Hakan Ergun (online).
• January 23-26, 2024: Moving Higher-Order Taylor Approximations Method for Smooth Constrained Minimization Problems, Workshop on Analysis and Potential, event website, Bucharest, Romania.
• September 29-30, 2023: Moving Higher-Order Taylor Approximations Method for Smooth Constrained Minimization Problems, Conference on Statistical Modeling with Applications, event website, Bucharest, Romania.
• June 13-16, 2023: Modified Projected Gauss-Newton Method for Constrained Nonlinear Least-Squares: Application to Power Flow Analysis, European Control Conference (ECC23), conference website, Bucharest, Romania.
• July 4-8, 2022: Efficient Optimization Methods for Complex Systems, Workshop on Algorithmic and Continuous Optimization, event website, UCLouvain, Belgium.
• August 24, 2021: Higher-Order Algorithms for Composite Minimization Problems, MaLGa Machine Learning Genoa Center, Italy, invited by Silvia Villa (online).

Teaching experience:

• Spring 2025 – Course Assistant for MAT11015: Basics of Mathematics in Machine Learning II, University of Helsinki.