Qisen Ma

Qisen Ma is a master's student at the Institute of Automation, Chinese Academy of Sciences, where he began his studies in September 2024 with a focus on embodied AI. His research spans embodied world models, skin lesion segmentation, and offline reinforcement learning, and he has co-authored work on the SKIP sparse keyframe interpolation paradigm for efficient embodied world models. His prior publications include LCAUnet for skin lesion segmentation and contributions to boundary-aware interactive 3D image segmentation.