My research focuses on traffic flow modeling and network-level traffic management, with particular emphasis on mixed and heterogeneous traffic conditions. I work at the interface of macroscopic traffic flow theory, data-driven modeling, and intelligent transportation systems (ITS) to develop scalable and deployable models for real-world urban networks. A key component of my work is the theoretical development and empirical calibration of Macroscopic Fundamental Diagrams (MFDs) and continuum models, including area-occupancy-based formulations and higher-order traffic flow models. My recent research addresses network-wide traffic state estimation under sparse and heterogeneous data, combining traffic sensors, probe data, and machine learning techniques. As part of the EU Horizon ACUMEN project, I contributed to AI-enabled auto-calibration frameworks and digital twin applications for multimodal traffic management. My ongoing research aims to integrate physics-based models with artificial intelligence for robust traffic monitoring, prediction, and control, supporting sustainable and efficient urban mobility systems.