Teaching

I co-direct the DataAI Master’s program at Institut Polytechnique de Paris
and direct the MAIA apprenticeship Master at Télécom SudParis, where I help design curricula that combine solid theoretical foundations with practical AI training.

I teach across programming, data science, machine learning, and modern AI systems.

Programming & Algorithms

Java & Algorithms

First-year engineering — Télécom SudParis
I teach Java, object-oriented programming, and fundamental algorithms such as lists, trees, graphs, and heaps.
Topics: OOP, algorithmic complexity, recursion, graph traversal, data structures
Course page: https://www-inf.telecom-sudparis.eu/COURS/CSC3101/Supports/fise/

Big Data & Scalable Systems

Big Data

Third-year engineering — Télécom SudParis / IP Paris
Introduction to large-scale data processing systems and distributed computing.
Topics: Hadoop, Spark, Kafka, distributed architectures
Course page: https://www-inf.telecom-sudparis.eu/COURS/CSC5003/Supports/

Machine Learning Systems

Third-year engineering — Télécom SudParis
How to build reliable, scalable, production-ready machine learning systems.
Topics: pipelines, data management, serving, monitoring, reliability

Machine Learning & Deep Learning

Deep Learning

Third-year engineering — Télécom SudParis
Foundations of modern deep learning.
Topics: backpropagation, gradient descent, MLPs, CNNs, RNNs, attention
Course page: https://www-inf.telecom-sudparis.eu/COURS/CSC8607/Supports/

Advanced Concepts in Deep Learning

Third-year engineering — Télécom SudParis
A deeper look at recent architectures and generative methods.
Topics: diffusion models, GANs, explainability, modern architectures

Natural Language Processing & LLMs

Language Models

Third-year engineering — Télécom SudParis
How language models work and how to use them in modern applications.
Topics: embeddings, transformers, prompting, fine-tuning, evaluation

Data Science & Practical Tools

Toolbox for Data Scientists

Second-year engineering — Télécom SudParis
A practical course covering key tools used by data scientists.
Topics: Python, data collection, web scraping, traditional NLP, visualization, IR, recommendation
Course page: https://www-inf.telecom-sudparis.eu/COURS/CSC4538/Supports/

Project Supervision (Various years)

I supervise hands-on projects from first to third year, including software, data, and AI-focused topics.

Past courses

• Introduction to OS and Bash — Télécom SudParis
• Databases — Télécom SudParis
• Introduction to software engineering for OO applications — Télécom SudParis
• Data on the Web — Télécom Paris
• Natural Language Processing — Télécom Paris
• Knowledge Base Construction — Télécom Paris
• Mining of Large Datasets — Télécom Paris
• Information Extraction — Télécom Paris

Teaching philosophy

My teaching is guided by a few principles:

• Balance between theory and practice. Students should understand what they do, and be effective when joining industry.
• Active learning. I use pedagogical methods that encourage students to engage directly with concepts, which is even more important in the era of LLM assistants.
• Adaptability. I teach diverse audiences (engineers, master’s students, managers), and I adapt content and examples to match their needs and backgrounds.

I aim to help students develop both strong technical foundations and responsible professional practices.