My journey began with operating systems internals. I started by working at Ford, where I customized Android system components relevant to multi-media and USB. Later at Google, I contributed to designing a unified customization interface for OEMs to make such modifications more smoothly and securely. After transferring to UW-Madison, I started to focus on systems research.

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.