Teaching
Courses & Student Mentorship
Teaching at EPITA
I teach advanced courses in Artificial Intelligence and Deep Learning at EPITA School of Engineering. My teaching focuses on cutting-edge topics in machine learning and emerging paradigms.
I try to keep the courses practical and research-aware, so students do not only learn how to use a method but also when it is appropriate, what its limitations are, and how to evaluate it carefully. When possible, I connect lecture material to current papers, benchmark practices, and implementation choices that matter in real projects.
Deep Learning
Master's Degree ("Data Science, AI and Graphs", "Industry of the Future", "Healthcare and AI")2024/25 - 2025/26 - 2026/27
Advanced course covering modern deep learning architectures, optimization techniques, and practical applications. Topics include convolutional neural networks, recurrent networks, attention mechanisms, and transformers.
Reinforcement Learning
Master's Degree ("Data Science, AI and Graphs")2025/26
Introduction to reinforcement learning, including Markov decision processes, value iteration, policy iteration, and Q-learning. Covers both theoretical foundations and practical applications.
Differential Programming Paradigm and Implementation
Master's Degree ("Data Science, AI and Graphs")2025/26
Explores the paradigm of automatic differentiation and its applications in machine learning. Covers differentiation theory, implementation techniques, and modern frameworks for building differentiable systems.
Research Elective: Benchmarking
Bachelor's Degree2025/26
Provides the basics on benchmarking and introduction to scientific research.
Teaching at Sapienza University of Rome
I acted as teaching assistant during my PhD at Sapienza University of Rome, where I supported the teaching of courses in Artificial Intelligence and Machine Learning. I assisted students in understanding complex concepts, provided guidance on practical exercises, and contributed to the development of course materials.
Reinforcement Learning
Master's Degree ("AI and Robotics")2025/26
Introduction to reinforcement learning, including Markov decision processes, value iteration, policy iteration, and Q-learning. Covers both theoretical foundations and practical applications.
Programming Foundations with Python
Bachelor's Degree2025/26
Introduces students to programming concepts and techniques using Python. Covers basic syntax, data structures, control flow, functions, and object-oriented programming.