Chaouki ben Issaid

Chaouki ben Issaid

Postdoctoral Fellow Researcher

University of Oulu


Chaouki Ben Issaid is a Postdoctoral Researcher at the Intelligent Connectivity and Networks/Systems Group (ICON), the University of Oulu, Finland. He received the Diplôme d’Ingénieur from l’École Polytechnique de Tunisie, La Marsa, Tunisia, in 2013. He obtained his M.Sc. degree in applied mathematics and computational science and his Ph.D. degree in statistics from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia in 2015 and 2019, respectively. His current research interests include communication-efficient distributed machine learning and efficient Monte Carlo simulations for rare event estimation.


  • Distributed Optimization
  • Federated Learning
  • Machine Learning
  • Efficient Monte Carlo Simulations


  • Ph.D. in Statistics, 2019


  • M.S. in Applied Mathematics and Computational Science, 2015


  • Engineering Degree, 2013

    Ecole Polytechnique de Tunisie

Recent Publications

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I was involved in teaching the following courses:

  • AMCS 241: Probability and Random Processes, KAUST (August 2017 - December 2018). My main tasks were preparing/grading homeworks, quizzes and exams and giving tutorials. The main topics discussed in the course are:

    1. Basic Probability
    2. Introduction to Random Processes
    3. Spectral Characteristics of Random Processes
    4. Analysis and Processing of Continuous Time Random Processes
  • AMCS 211: Numerical Optimization, KAUST (January - May 2015). My main tasks were grading homeworks, quizzes and exams. The course focus was about:

    1. Formulation of optimization problems (e.g. in the primal and dual domains), study feasibility, assess optimality conditions for unconstrained and constrained optimization.
    2. Numerical methods for analyzing and solving linear programs (e.g. simplex), general smooth unconstrained problems (e.g. first-order and second-order methods), quadratic programs (e.g. linear least squares), general smooth constrained problems (e.g. interior-point methods).