Matteo Gaeta

University of Salerno, Centro di Ricerca in Matematica Pura ed Applicata

Papers

Total Citations

H-Index

About

Matteo Gaeta’s research bridges artificial intelligence, optimization, and computer vision, with a focus on developing practical algorithms for control and logistics. His most cited work, “Fitted Q-iteration by Functional Networks for control problems” (2016, 6 citations), introduces a novel reinforcement learning approach that leverages functional networks to solve complex control tasks, demonstrating his contribution to advancing machine learning methods for decision-making systems. Earlier, Gaeta applied optimization techniques to healthcare logistics in “Study on man power planning of hospital transportation department by using VRPSTW” (2005, 2 citations), addressing real-world scheduling challenges with vehicle routing problems. His work also extends to computer vision, where he explores the recognition of biological individuals—such as human and animal detection—a growing field with applications in security, wildlife monitoring, and autonomous systems. Though his citation counts are modest, Gaeta’s research reflects a commitment to solving interdisciplinary problems, from AI-driven control to operational efficiency. His ability to integrate functional networks, reinforcement learning, and optimization underscores a versatile approach, making his contributions valuable for students and researchers interested in applied AI and computational logistics.