Chengyu Fang

Achieve superhuman performance in robotic ultrasound-guided needle insertion by unifying vision, language, and action in a single end-to-end model.

Medical VLMs can achieve state-of-the-art visual reasoning without any human annotations, thanks to a novel reinforcement learning framework that leverages model uncertainty and rollout agreement.

Achieve state-of-the-art real-world image dehazing by jointly reconstructing the clear scene and scattering variables, even with non-uniform haze and complex lighting.

Stop training your image restoration models to mimic flawed ground truth; instead, explicitly optimize for perceptual quality using a plug-and-play module guided by No-Reference Image Quality Assessment.