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About

Stefano Squartini is a leading researcher at the intersection of artificial intelligence, cognitive computing, and combinatorial optimization. His work primarily focuses on advancing deep reinforcement learning and adaptive dynamic programming (deep RL/ADP), where he has made significant contributions to enabling AI systems to generate control signals directly from visual inputs—a breakthrough that merges the perceptual strengths of deep learning with sophisticated decision-making. He has also pioneered the integration of cognitive and emotional information processing into human–machine interaction, exploring how machines can interpret and respond to human affective states. More recently, Squartini has turned his attention to combinatorial optimization, developing novel graph neural network approaches to tackle the notoriously difficult linear sum assignment problem, with applications spanning logistics, robotics, and telecommunications. His most cited work, the 2018 special issue on deep RL/ADP, has garnered 28 citations and helped shape the field’s trajectory. With additional contributions in graph-based neural methods and cognitive computing, Squartini’s research continues to bridge theoretical advances with practical, real-world AI systems.