My journey began with operating systems internals. At Ford, I implemented product designs by customizing Android. Later at Google , I focused on makiing this customization smooth, unified and secure.

After transferring to UW-Madison, my focus has shifted to systems research, specifically, distributed systems research, as I became more interested in the challenges of scale and coordination across machines.

My systems researches are primarily about bandwidth issues in modern data-intensive applications. At UW-Madison, I worked on ways to make consensus protocols bandwidth-adaptive, advised by Prof. Remzi Arpaci-Dusseau and Prof. Andrea Arpaci-Dusseau. Currently, I’m working with Prof. Greg Ganger at Carnegie Mellon University’s Parallel Data Lab , where I contribute to re-designing IO interfaces in data center file systems to better utilize bandwidth.

Building on this systems foundation, I have also explored problems at the intersection between systems and machine learning.

On the machine learning side, I was working on improving LLMs’ planning and tool use abilities in software engineering and infra ops domains at Microsoft Research, fortunately supervised by Dr. Xuan Feng and Dr. Sameh Elnikety.

Currently, I’m also collaborating with Prof. Zhihao Jia at Carnegie Mellon University’s Catalyst Group about improving LLM' inference with sparse attention. We are taking an end-to-end approach -- optimize LLMs’ inference from model structure to kernel implementations.