Research experience

Assistant Professor
Sep 2025 (Starting soon)
Université Paris-VIII-Vincennes, France
  • Teaching: Artificial Intelligence, Neural Networks, Statistical Learning, Information theory, Complexity theory
  • Research areas: Computational learning theory, Neural Network Compression, Generative models, Dynamic and Continual Learning, Various Applications of AI
Ph.D. in Computer science
Mar 2022 - May 2025
CEA Paris-Saclay - Université Paris-Saclay, France
  • Main supervisor: Pr. Jean Martinet
  • Co-supervisors: Dr. Karim Ben Chehida, Dr. Thibault Allenet
  • PhD topic:Exploring dynamic compression in Vision Transformers for image classification
Deep Learning & Computer Vision Engineer
Feb 2021 - Nov 2021
Société nationale des chemins de fer français (SNCF), France
Research Internship
Jun 2020 - Nov 2020
Laboratoire d’Ingénierie des Systèmes de Versailles (LISV) - Université Paris-Saclay, France
  • Supervisor: Pr. Sylvain Chevallier
  • Topic: Deep learning for Unsupervised Anomaly detection in time series
Research Assistant
Oct 2019 - Mar 2020
Laboratoire d’Ingénierie des Systèmes de Versailles (LISV) - Université Paris-Saclay, France
  • Supervisor: Pr. Sylvain Chevallier
  • Topic: Transfer learning for Brain-Computer Interfaces (BCI) using Riemannian similarities

Education

Ph.D. in Computer science
Mar 2022 - May 2025
EDSTIC Université Côte d'Azur, France
  • Main supervisor: Pr. Jean Martinet
  • PhD topic: Exploring dynamic compression in Vision Transformers for image classification
M.S. in Robotics, signal & image processing
Sep 2018 - Nov 2020
Université Paris-Saclay, France
B.S. in Applied mathematics, Opt: Control theory
Sep 2016 - Jun 2018
University of Béjaia, Algeria
Prep classes for engineering schools, opt: Math-Physics
Sep 2014 - Jun 2016
École préparatoire aux sciences et techniques de Annaba (EPST), Algeria

Teaching

Artificial Intelligence and Data
14 hours
École nationale supérieure de techniques avancées (ENSTA - IP Paris), France
  • Course Code: IA01
  • Description: This course covers the basics of data mining techniques and statistical learning, including KNNs and decision trees, as well as the fundamentals of neural networks and key architectures. It also introduces data privacy concepts such as k-anonymity, l-diversity, and t-proximity.
Algorithmic and Data Structures
50 hours
École nationale supérieure de techniques avancées (ENSTA - IP Paris), France
  • Course Code: IN103
  • Description: This course focuses on building fundamental data structures such as stacks, queues, linked lists, trees, graphs, and apply them to solve various algorithmic problems

Technical Skills

  • Programming: Python, C/C++, Julia, Matlab
  • Frameworks: PyTorch, TensorFlow, Keras
  • Tools: Git, Docker

Languages

  • French: Fluent
  • English: Fluent
  • Spanish: Intermediate

Hobbies

  • Classical guitar (18 years, including 6 years at the conservatory)
  • Basketball (14 years)