Farid DINAR is a Ph.D. candidate and teaching assistant at the university of Toulon. His research focuses on Non-Intrusive Load Monitoring (NILM) systems for residential and community energy management, as part of the SUNLEAVS project. His work explores electrical signature acquisition, feature engineering, and the application of machine learning models to disaggregate appliance-level energy consumption from aggregated power signals. Farid also serves as a teaching assistant in an engineering school, Seatech, where he leads courses and labs on Python programming and object-oriented design. His broader research interests include smart grids, IoT-based sensing, edge AI, and sustainable energy analytics.