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About

Zongyuan Ge is a leading researcher at the intersection of computer vision, robotics, and agricultural automation, with a focus on deep learning for real-world applications. His most influential work, "DeepFruits: A Fruit Detection System Using Deep Neural Networks" (2016), has garnered over 1,079 citations, establishing a foundational approach for accurate, real-time fruit detection essential for autonomous agricultural robotics and yield estimation. Ge has also made significant contributions to sim-to-real transfer in robotics, notably through his work on adversarial discriminative transfer for visuo-motor policies, which enables robots to learn in simulation and adapt to real-world environments without requiring labeled real-world data—a critical advancement for scalable robotic deployment. His recent projects include OphNet, a large-scale video benchmark for ophthalmic surgical workflow understanding, and MoRE, a novel vision-language-action model for quadruped robots that integrates reinforcement learning to enhance versatility. Ge’s research consistently bridges simulation and reality, driving progress in both agricultural robotics and surgical automation, and his work continues to shape how deep learning systems are deployed in complex, unstructured environments.