I'm a software engineer with 4+ years of experience building AI infrastructure at AWS — specifically the capacity layer that decides how GPU resources get reserved, allocated, and delivered to ML training and inference workloads at scale.

Before AWS, I interned at Apple on strategic data infrastructure and did ML research at Boston University Department of Medicine. I studied CS & Math at Boston University and then at Carnegie Mellon University.

This blog is where I write about GPU scheduling, capacity planning for AI workloads, distributed systems patterns, and the infrastructure that makes large-scale ML possible. Bilingual (中文/English), depending on the topic.