Shuowei Jin

Shuowei Jin

PhD Candidate at University of Michigan CSE

University of Michigan


I am a fourth-year PhD Candidate in the Computer Science and Engineering Department at the University of Michigan, Ann Arbor under the supervision of Prof. Morley Mao. I received my bachelor’s degree from the School of the Gifted Young at the University of Science and Technology of China, majoring in computer science and advised by Prof. Wei Gong.

My current research interests lie in creating systems to support machine learning model deployments (Systems for ML) and building intelligent and reliable network systems with machine learning models (ML for Systems).

I am a research intern at Microsoft Research Redmond, in the summer of 2023, fortunately working with Dr. Francis Y. Yan on building systems to optimize the performance of machine learning models in 5G VRAN. I also interned at Alibaba DAMO Academy, in the summer of 2020, collaborating with Dr. Hong Wu and Dr. Tieying Zhang on applying machine learning to automatically tune database systems. I also work with Prof. Nanyun Peng as a research intern at the USC Information Sciences Institute in 2019 summer to apply language models on extracting event hierarchy.

  • Systems for ML
  • ML for Network Systems
  • Mobile Network Systems
  • PhD in Computer Science and Engineering, 2020-Present

    University of Michigan

  • BEng in Computer Science, 2016-2020

    School of the Gifted Young, University of Science and Technology of China


Research Intern
May 2023 – Present Redmond, Washington, United States
Work on building system to optimize machine learning model deployments in 5G VRAN.
Research Intern
May 2020 – July 2020 Hangzhou, China
Work on applying machine learning to automatically tune database systems.

Recent Publications

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(2022). Vivisecting Mobility Management in 5G Cellular Networks. SIGCOMM 2022.

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(2021). ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases. SIGMOD 2021.


(2021). A Variegated Look at 5G in the Wild: Performance, Power, and QoE Implications. SIGCOMM 2021.

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