I am a Ph.D. student at the National University of Singapore and a member of the Alibaba Platform for AI (PAI) team, jointly advised by Prof. Jialin Li and Wei Lin. My primary research interest is developing highly efficient distributed systems for machine learning.
During my undergraduate studies, I had the privilege (and fun) of spending four years with the NTU HPC club, where we won the Overall Championship at SC'17 and set the LINPACK World Record. I am co-organizing a MLSys seminar.
Outside of my academic pursuits, I enjoy cooking (menu), jogging, and skateboarding. And my Erdős number is 5.
Download my resumé.
Doctor of Philosophy, Computer Science, 2021 - Current
National University of Singapore
Bachelor of Engineering in Computer Science, 2015 - 2019
Nanyang Technological University
Visiting Student, Fall 2016
New York University
We present a framework that drastically speeds up the process of deriving the tensor parallel schedule for large neural networks.
We present ParaGAN, a cloud-training framework for GAN, which demonstrates near optimal scaling performance on BigGAN.
Whale is a highly scalable and efficient distributed training framework for deep neural networks, introducing a hardware-aware parallel strategy and user-enabled model annotations for optimising large-scale model training, demonstrating its prowess by successfully training a multimodal model with over ten trillion parameters on a 512-GPU setup.
We propose an efficient parameter sharing strategy for Transformer architecture by replacing FFN with MoE layer and sharing the trainable parameters except the normalization layers. Competitive performance across CV and NLP tasks were achieved with up to 6x reduction in the numbers of unique parameters.