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About

Daniel H. Stolfi is a computational researcher specializing in swarm robotics, evolutionary computation, and autonomous systems optimization. His work sits at the intersection of artificial intelligence and robotics, with a particular focus on developing robust, self-organizing multi-robot formations for demanding real-world applications in space, aerospace, and defense. Stolfi's most significant contributions center on the design and optimization of distributed UAV swarm formation systems. His landmark 2022 paper introducing the Distributed Formation Algorithm 3 (DFA3), optimized via hybrid evolutionary algorithms, has garnered 19 citations and established a foundational framework for efficient swarm coordination. Complementing this, his research on surrogate model-based optimization addresses the computational bottlenecks inherent in large-scale swarm simulation, broadening the practical scope of swarm deployment. A distinctive strength of Stolfi's research is its commitment to real-world applicability. His 2023 work bridging simulations and physical robots demonstrates rigorous validation across environments, while his development of an E-Puck2 plug-in for the ARGoS simulator (6 citations) provides the broader robotics community with valuable open tools. Applications explored across his portfolio — including asteroid observation, convoy escort, and counter-drone systems — reflect both scientific depth and practical ambition, making his research increasingly relevant to emerging autonomous systems challenges.