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About

Marco Zullich is a researcher at the forefront of neuroevolution and soft robotics, specializing in the intersection of artificial neural networks (ANNs) and robotic control. His work focuses on developing efficient, robust controllers for modular soft robots—machines with flexible bodies and distributed sensors and actuators that demand highly complex neural architectures. Zullich’s major contribution lies in systematically investigating how pruning—the removal of redundant neurons and connections—can optimize evolved neural controllers. His 2021 paper, with 15 citations, demonstrated that pruning not only reduces network complexity but can enhance controller performance, challenging the assumption that larger networks are always better. In his 2022 work (11 citations), he advanced this by merging pruning with neuroevolution, creating controllers that are both robust to damage and computationally efficient. A subsequent corrigendum (3 citations) refined these findings, underscoring his commitment to methodological rigor. Zullich’s research is pivotal for scaling soft robotics, where efficient control is critical for real-world applications like search-and-rescue or medical devices. By bridging evolutionary algorithms and network optimization, he is shaping the future of adaptive, resilient robotic systems.