I am a Research Scientist at Meta’s AI System Co-Design team, working on performance projection for GenAI training and hardware/software co-design. I was also a Research Intern at Microsoft’s AI Framework team, where I worked on automatic model parallelization for LLM serving.

I obtained my Ph.D. in Electrical Engineering at University of Southern California, advised by Prof. Viktor Prasanna. My dissertation focused on developing Automated Graph ML Systems. I received my B.S. in Electrical Engineering from National Taiwan University (NTU).

In addition to research interests, I’m also into video editing and photography. Check out my vlogs and photos.

Selected Work

APEX — A LLM serving simulator that is designed to identify the optimal parallelism configuration. Paper available in JPDC, 2026. [paper] [code]

ARGO — An auto-tuning runtime system for scalable GNN training on multi-core processors. Speeds up DGL by up to 5x and is now integrated into the Deep Graph Library. IPDPS 2024 [Best Paper Nominee]. [paper] [code]

Latest News

Jul 21, 2025: Started working at Meta.

Jun 24, 2025: Defended my dissertation!

May 20, 2024: Started my internship at Microsoft.

Apr 6, 2024: ARGO is nominated as the best paper of IPDPS 2024 (4 out of 88 papers). ARGO is now publicly available on the Deep Graph Library.