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About

Yun Sil Chang is a leading roboticist whose research focuses on autonomous navigation, multi-robot simultaneous localization and mapping (SLAM), and spatial perception in extreme, GPS-denied environments. As a key member of TEAM CoSTAR, she contributed to the DARPA Subterranean Challenge, developing the NeBula autonomy framework (105 citations) that enabled robots to navigate complex tunnels and urban ruins. Her work on underground SLAM (38 citations) provides a comprehensive benchmark of state-of-the-art algorithms across six competition teams, establishing best practices for perceptually-degraded settings. Chang’s recent contributions include LAMP 2.0, a robust multi-robot SLAM system for large-scale underground operations, and Hydra, a real-time system for constructing and optimizing 3D scene graphs—a high-level representation that bridges geometric and semantic understanding. She has also advanced distributed SLAM resilience through improved Kimera-Multi systems and developed loop closure prioritization techniques to maintain computational efficiency in multi-robot teams. With over 150 total citations and a growing portfolio of high-impact publications, Chang is shaping the future of autonomous exploration in hazardous, unstructured environments.