Ziwei Niu

MIRTH transforms VLA models by enabling them to effectively leverage historical dynamics and optimize action trajectories, leading to unprecedented error recovery in robotic control.

By incorporating language guidance into federated learning, SurgFed tackles the long-standing problem of tissue and task heterogeneity in surgical video understanding, leading to improved segmentation and depth estimation across diverse surgical settings.

Achieve state-of-the-art ECG analysis by disentangling modality-specific biases and capturing spatial-temporal dependencies, outperforming existing multimodal approaches.

MedVAR leapfrogs existing methods by achieving state-of-the-art medical image generation with an autoregressive architecture designed for both speed and scalability.