Zi-Jiang Yang

The Cartesian Hand is introduced, a 7-DoF end-effector that rethinks dexterous manipulation by combining independent grasping and relative manipulation within a single end-effector using only linear motion.

Copying human head-neck kinematics leaves over 60% of reachable manipulation space unobservable; independently actuating onboard RGB-D cameras surges visible-reachable coverage to 97% while cutting manipulation energy by 19%.

MoNo's innovative approach to optimal transport ensures stable latent spaces, preventing token collapse and enabling efficient learning of long-range physical interactions in PDEs.

FastTPS accelerates LLM inference by 6x while preserving memory efficiency, revolutionizing how we handle long-sequence inputs on AI accelerators.

Reward models can be made more robust to spurious cues like length and sycophancy by explicitly training them to understand the *intent* behind a prompt.

Achieve formally certified collision risk guarantees for robot manipulators in complex, uncertain environments with a novel risk-bounded motion planning framework.

Decoupling nuclei detection and classification in histopathology unlocks significant performance gains in pathology foundation models while drastically reducing the number of trainable parameters.