Experience

  1. PhD candidate in Artificial Intelligence

    CRIL, Univ. Artois & CNRS

  2. Teaching Assistant

    Faculty of Sciences Jean Perrin, Artois University

    Responsibilities include:

    • Practical work and directed studies of Algorithms using Python.
    • Practical work and directed studies of Functional programming using Haskell.
    • Practical work on the basics of web development (HTML/CSS).
    • Practical work of Unix commands.

Education

  1. PhD Artificial Intelligence

    Artois University
    Thesis on Managing inconsistency in partially ordered information: application to dynamic and explainable access control models. Supervised by Prof Salem Benferhat, Prof Karim Tabia and Dr. Sihem Belabbes. Presented papers at JELIA 2023, DL2024, and SUM2024.
    Thesis description

  2. Research Master in Artificial Intelligence

    Artois University

    GPA: 15.31/20.00

    Courses included:

    • Deep Learning, Data Mining
    • Knowledge Representation and Reasoning
    • Operational Research and Constraint Satisfaction Problems (SAT/CSP)
    • Dynamics of Decision and Game/Voting Theories

  3. Master in Networks & Distributed Systems

    the University of Constantine 2

    GPA: 15.56/20.00

    Courses included:

    • Networks Eng & Networks Security, Mobile & Wireless Networks
    • Distributed Systems & Algorithms, Data Mining
    • Parallel Architecture, Advanced Databases & Big Data
    • Complex Systems, Complexity, Modelling & Simulation
    • Distributed Operating Systems & Cloud Architecture

  4. BSc Computer Science

    the University of Constantine 2

    GPA: 14.3/20.00

    Courses included:

    • Algorithms, OOP, Logic, Databases
    • Networks, Software Eng, Operating Systems
    • Operational Research, Language Theory & Compilation
    • Information Sec, Web & Android apps development
Skills & Hobbies
Technical Skills
Python
Data Science
SQL
Hobbies
Hiking
Cycling
Swiming
Awards
Google Cloud Big Data and Machine Learning Fundamentals
Coursera / Google Cloud ∙ April 2021
Certificate issued by Coursera to prove a basic understanding of Big Data and Machine Learning. Acquired skills: Tensorflow, Bigquery, Google Cloud Platform, Cloud Computing.
Machine Learning
Coursera / Stanford University ∙ October 2020

This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include:

  • Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
  • Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
  • Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course draws from numerous case studies and applications, in order to apply learning algorithms to build smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Languages
90%
English
90%
French
90%
Arabic