Systems for AI workloads
Efficient infrastructure for AI workloads, from on-device inference with Sereno to large-scale LLM reinforcement learning with UniExact.
PhD Student at SJTU IPADS
Institute of Parallel and Distributed Systems (并行与分布式系统研究所, IPADS)
Shanghai Jiao Tong University (上海交通大学, SJTU)
xin_tong (AT) sjtu (dot) edu (dot) cn
My research sits at the intersection of AI and systems. I primarily focus on two complementary directions:
Efficient infrastructure for AI workloads, from on-device inference with Sereno to large-scale LLM reinforcement learning with UniExact.
Correctness and long-term maintenance when AI writes system artifacts, from LLM-derived knowledge with LLMake to file-system code with FSCrash.
I am a PhD student at the Institute of Parallel and Distributed Systems (并行与分布式系统研究所, IPADS), Shanghai Jiao Tong University (上海交通大学, SJTU), advised by Haibo Chen (陈海波) and Mingkai Dong (董明凯). I received my B.Eng. degree in Information Engineering from SJTU in 2025.
Across both directions, I work at the intersection of ML systems and operating systems, with an emphasis on dependable, efficient, and deployable systems.
A talk on the design and practice of Sereno for managing memory-bandwidth contention in on-device LLM inference.
Oral presentation at the 20th USENIX Symposium on Operating Systems Design and Implementation.
Presentation page| Jul 14, 2026 | I gave the oral presentation of Sereno at OSDI ‘26 in Seattle. |
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| Jul 06, 2026 | Our paper StarfishOS has been accepted to SOSP 2026! |
| Apr 24, 2026 | Our paper Sereno has been accepted to OSDI 2026! |
| Jan 07, 2026 | I was elected Chairman of the CCF SJTU Student Chapter. |
| Sep 01, 2025 | I began my PhD journey at the Institute of Parallel and Distributed Systems (并行与分布式系统研究所, IPADS), Shanghai Jiao Tong University (上海交通大学, SJTU). |
| Aug 23, 2025 | Our paper SwitchFS has been accepted to EuroSys 2026! |
| Jun 30, 2025 | I received my B.Eng. degree from Shanghai Jiao Tong University (上海交通大学, SJTU) and was honored as a Shanghai Outstanding Graduate (上海市优秀毕业生). |