PhD student, National University of Singapore
3 papers at NeurIPS 2025
We propose HASTE, which combines holistic alignment (feature and attention) with early termination to accelerate diffusion transformer training by 28× while maintaining quality.
Generate high-performing LoRA parameters from prompts that is unseen in training.
Concentric Ring Parallelism that reduces P2P communication volume and avoid inter-node communication.
