I am a final-year Ph.D. candidate in Computer Science at The University of Texas at Austin, advised by Aditya Akella and Sanjay Shakkottai. I build system abstractions, frameworks, and generalizable AI components, grounded in theoretical analysis, that let real-world systems efficiently approximate optimal behavior as workloads, environments, and objectives change.

My work spans networking, operating systems, and machine learning. I was part of the founding proposal team for the NSF Expedition on Learning-Directed Operating Systems (LDOS), where I now lead the undergraduate research program. Before UT, I received my B.S. in Electrical and Computer Engineering from Shanghai Jiao Tong University. I have also spent time at Microsoft Research, Google, and Intel.

I am on the 2026–27 academic job market for tenure-track faculty positions in computer systems and networking. My application materials: CV · Research Statement · Teaching Statement

Research

Latent information should be a first-class object in system design.

Today's systems expose isolated measurements, delayed feedback, and the effects of only the deployed policy. The information that matters most for good decisions stays hidden. My research recovers this latent structure with lightweight AI, exposes it through reusable system interfaces, and uses theory to characterize what it can and cannot buy.

Thrust 1

Representing latent workload and system dynamics

Build compact learned representations that recover workload and system state that raw measurements hide.

UNUMMore to come
Thrust 2

Guiding policy search with system knowledge

Exploit system knowledge to guide near-optimal policy search with smaller search space.

DarwinMore to come
Thrust 3

Characterizing limits and safeguarding

Theory for when extra information helps, and runtime guardrails that keep learned policies safe.

Read my research statement →

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Selected Publications

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