Welcome to my webpage
I am an applied scientist and researcher working at the intersection of numerical optimization and machine learning. My work spans optimization theory, algorithm design, and scalable implementations, with a focus on both convex and nonconvex problems. I specialize in bridging mathematical foundations with practical, high-performance solutions, developing efficient methods that improve the speed, robustness, and scalability of machine learning models. I am interested in applying optimization-driven approaches to complex R&D challenges in areas such as AI, energy systems, and large-scale decision-making.
Currently, I am a postdoctoral researcher at the University of Helsinki, Finland, in the Department of Mathematics and Statistics, working with Tuomo Valkonen.
Education & Research Background
I defended my Ph.D thesis in May 2024, supervised by Ion Necoara, within the 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.
Area of interest
I am broadly interested in exploring the following areas:
Optimization
- Efficient convex and nonconvex optimization, including nonsmooth formulations
- Structure-aware methods (composite objectives, sparsity, stochasticity)
- Second- and higher-order methods, bilevel optimization, and online optimization
Machine Learning
- Optimization for ML and AI
- Distributed and federated learning (including Byzantine-resilient methods)
- Communication- and computation-efficient algorithms