Resume
Academic Achievements
- Successfully defended his PhD Thesis; multiple papers accepted for publication at NeurIPS 2025, ICML 2025, and other key conferences; involved research won the best poster award at Deep Learning Indaba.
Research Experience
- Collaborated with Siemens on bringing scalable multi-agent RL into industrial production scheduling; recently working with a London-based start-up, Inephany, on leveraging RL for hyperparameter optimization in LLMs.
Education
- PhD Candidate at Politecnico di Milano, supervised by Prof. Marcello Restelli, part of the RL^3 Group.
Background
- Research interests: Reinforcement Learning (especially unsupervised RL), multi-agent systems, partial observability, and decision-making under general utility functions. Professional field: Developing methods that can handle real-world messiness, including pre-training models to make RL agents more general and adaptable.
Miscellany
- Personal interests and hobbies not mentioned.