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About

Yanchao Yang is a dynamic researcher whose work spans 3D computer vision, human motion understanding, medical robotics, and embodied AI. His research addresses fundamental challenges at the intersection of perception, prediction, and control — areas critical to the advancement of intelligent autonomous systems. Among his most recognized contributions is his work on domain adaptation for 3D point clouds, where he developed geometry-aware implicit representations to bridge geometric variations across different scanning conditions — a breakthrough relevant to autonomous driving and robotics, garnering over 64 citations. His gaze-informed human motion prediction framework, GIMO, has similarly gained strong traction (59 citations), demonstrating how integrating gaze signals with scene context yields significantly more accurate human behavior forecasts for AR/VR and assistive robotics applications. Yang has also made notable strides in medical robotics, contributing to the IRISS system — a semi-automated, OCT-guided robotic platform for cataract surgery that earned over 61 citations. His more recent innovations, including Text2Reward for automated reinforcement learning reward shaping and COPILOT for egocentric collision prediction, reflect a broadening research vision toward language-guided and safety-aware embodied intelligence. Across all these domains, Yang's work consistently pushes the boundary between perception and action.