Research

We study efficient Vision-Language-Action (VLA) models, efficient action diffusion models, and efficient embodied world models, enabling agents to perceive, reason, and act efficiently under resource-constrained conditions.

We develop methods for model compression, model pruning, sparse inference, neural architecture search, and efficient training strategies to enable efficient deployment and acceleration of general-purpose deep learning models.