My teaching spans mathematical foundations, theoretical computer science, optimization, and computational methods, with an emphasis on connecting theory with practical problem solving.

Mathematical Foundations
Algebra Probability & Statistics Signal Processing
Optimization Algorithms
MILP definition Branch & Bound: Branching and Searching Components Population-Based Algorithms
Computational Practice
The SCIP Optimisation Module The DEAP metheuristic tool Data Science Modules Jupyter Notebooks for practices

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University of Luxembourg · 2024–2025

Project Supervision

Supervised teams of three Master’s students on semester-long research projects, with approximately four hours of group work per week.

  • 2024 — Lower-bound strategies in Branch & Bound for the Permutation Flowshop Problem.
  • 2025 — Cutting-plane selection in Branch & Bound.

Lectures

Taught Master’s students in Information and Computer Sciences.

  • Winter 2024: 4 × 1.5-hour lectures · 58 students
  • Winter 2025: 6 × 1.5-hour lectures · 62 students

Topics and resources:

  • Introduction to Branch & Bound — Slides
  • Searching and Branching in Branch & Bound — Slides
  • Population-Based Algorithms — Slides
  • Solving Methods for the Travelling Salesman Problem — Exercises · Correction
  • Current research — presentation of ongoing work on machine learning for optimization

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IMT Atlantique · Rennes · 2022–2023

Practice Teaching

Supported the international Master’s program in Information Technology.

  • 2022: 49 hours · 12 students
  • 2023: 40 hours · 24 students

Teaching covered:

  • Mathematics: Algebra, Probability & Statistics
  • Computer Science: Python, Data Science
  • Engineering: MATLAB, Signal Processing
  • Operations Research: Optimization and problem solving