David Gianazza

David Gianazza is a Researcher at the National School of Civil Aviation (ENAC), focusing on air traffic management and trajectory prediction. His work combines machine learning and operational research to address challenges in aircraft conflict resolution, airspace configuration, and performance modeling.

• Specializes in neural networks and metaheuristic optimization
• Key research areas: air traffic complexity, 3D trajectory planning, and workload modeling

His 15 most recent articles (2024-2012) demonstrate expertise in ADS-B data analysis, mass/thrust estimation, and hybrid deterministic-stochastic search algorithms. Collaborations include researchers like Nicolas Durand, Richard Alligier, and Jean-Marc Alliot across institutions in France, Netherlands, and USA.

Publications highlight applications of ant colony optimization, A* algorithms, and gradient boosting machines to air traffic problems. His work bridges theoretical computer science and practical ATM solutions, with emphasis on safety and efficiency.