About

Welcome to my homepage!

I am an Applied Scientist at AWS Neuron Science team in the Annapurna Labs, suppprting AWS Trainium/Inferentia chips. I received my Ph.D. degree in Computer Science from Cornell University, advised by Chris De Sa. I’m also grateful to work with Vitaly Shmatikov on machine learning privacy. Prior to Cornell, I obtained bachelor degree in Mathematics (ZhiYuan Honors) from Shanghai Jiao Tong University, where I am fourtunate to be advised by John E. Hopcroft and Huan Long.

I’m interested in efficient machine learning system to address the growing computational demands. My focus lies in i) designing data-aware representations, ii) optimizing computational efficiency, and iii) advancing system performance to enable scalable learning. Topics of interest include low-precision training, inference, and learning with non-Euclidean representations. My research interests extend to private and robust machine learning algorithms.

Recently, I’m particularly passionate about algorithm–hardware co-design — understanding the limitations of hardware & software, leveraging their features, and designing supports for efficient and reliable training and inference. My goal is to simplify the development and deployment of foundation models on such specialized hardware.