Kaiqiang (Andy) Xu

Research

My research studies the system foundations of large-scale AI — from new system abstractions to scheduling algorithms and parallelization strategies that enables more accessible and efficient AI computing infrastructure. I have led the design and operation of multiple production AI clusters.

My work has been published in top-tier conferences across computer networks, operating systems, computer architecture, and databases, including NSDI, OSDI, ASPLOS, and SIGMOD. I was a researcher at Google, where I worked on large model inference acceleration.

I received my PhD in Computer Science from HKUST, advised by Prof. Kai Chen, where my PhD dissertation received the Honorable Mention of the CSE Best PhD Dissertation Award.

Selected Papers (Full publication list and Google Scholar)

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GREEN: Carbon-efficient Resource Scheduling for Machine Learning Clusters

Kaiqiang Xu, Decang Sun, Han Tian, Junxue Zhang, Kai Chen

NSDI '25 USENIX Symposium on Networked Systems Design and Implementation

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Design and Operation of Shared Machine Learning Clusters on Campus

Kaiqiang Xu, Decang Sun, Hao Wang, Zhenghang Ren, Xinchen Wan, Xudong Liao, Zilong Wang, Junxue Zhang, Kai Chen

ASPLOS '25 ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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Sequoia: An Accessible and Extensible Framework for Privacy-Preserving Machine Learning over Distributed Data

Kaiqiang Xu, Di Chai, Junxue Zhang, Fan Lai, Kai Chen

SIGMOD '25 ACM International Conference on Management of Data

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Enabling Efficient GPU Communication over Multiple NICs with FuseLink

Zhenghang Ren, Yuxuan Li, Zilong Wang, Xinyang Huang, Wenxue Li, Kaiqiang Xu, Xudong Liao, Yijun Sun, Bowen Liu, Han Tian, Junxue Zhang, Mingfei Wang, Zhizhen Zhong, Guyue Liu, Ying Zhang, Kai Chen

OSDI '25 USENIX Symposium on Operating Systems Design and Implementation