Tuo Shi

Tuo Shi

Assistant Professor

Faculty of Computer Science and Artificial Intelligence
Shenzhen University of Advanced Technology

Email: shituo [AT] suat-sz.edu.cn

Address: Yuheng Building 2, 1 Gongchang Road, Guangming District

About

Hi, I am Tuo Shi. I am a tenure-track Assistant Professor in the Faculty of Computer Science and Artificial Intelligence at Shenzhen University of Advanced Technology (深圳理工大学). Before joining SUAT, I was a postdoctoral researcher in the Department of Computer Science at Aalto University, where I worked with Prof. Mario Di Francesco and Prof. Bo Zhao. Earlier, I was a postdoctoral researcher at the Department of Computer Science, City University of Hong Kong, supervised by Prof. Jianping Wang, and an associate research fellow at the College of Intelligence and Computing, Tianjin University.

I received both my Ph.D. and Bachelor’s degrees in Computer Science from Harbin Institute of Technology (Ph.D. in 2021), where I was advised by Prof. Jianzhong Li at the Massive Data Computing Lab. I also worked as a visiting student at George Washington University from 2019 to 2020, advised by Prof. Xiuzhen Cheng.

Openings

I am building my research group at Shenzhen University of Advanced Technology (深圳理工大学), and I am looking for motivated Ph.D. students (2027 enrollment), postdocs, and Research Assistant Professors (RAP) to join my group. If you are interested, feel free to email me your CV and one or two representative works.

News

  • Aug 2026I joined Shenzhen University of Advanced Technology (深圳理工大学) as an Assistant Professor (tenure-track).
  • May 2026Our paper Vista appears at CAIS 2026.
  • Jan 2026Our paper SHARP is accepted to VLDB 2026.
  • Oct 2025Our paper EARL appears at the SOSP 2025 Workshop on Systems for Agentic AI.
  • May 2025Invited talk at the Helsinki CS Theory Seminar, Aalto University.

Research Interests

Mobile Computing MLSys LLM Infra

Standing at the intersection of machine learning and computer systems, I see intelligent computing at a turning point. I aim to build resource-aware intelligent systems that unite algorithmic intelligence with system-level orchestration, making AI more efficient, reliable, and sustainable.

Guided by this vision, my research focuses on resource-efficient computing for intelligent systems across diverse hardware and system scales, ranging from highly constrained edge devices, through real-time autonomous platforms, to large-scale machine learning infrastructures.

More concretely, my work centers on three directions:

  • Efficient Agentic AI Systems agent orchestration, multi-agent collaboration, scalable agent runtimes
  • Edge Intelligence on-device LLM inference, cloud–edge collaborative intelligence
  • Efficient Autonomous Systems autonomous driving, real-time embodied intelligence, energy-efficient autonomy
Resource scale

Across these scales, resources are always the bottleneck — only the nature of the bottleneck changes with scale. Hover over a scale to see how.

At small scale, resources are too limited to reliably complete the task. My goal is to enable task execution under limited resources.

At moderate scale, there are some resources, but not enough to meet users’ real-time demands. My goal is to improve task performance under moderate resources.

At large scale, resources are abundant, yet the tasks are huge and just as resource-hungry. My goal is to optimize cost efficiency for resource-hungry tasks.

Publications

Selected Conference Papers (Google Scholar)

Selected Journal Papers (Google Scholar)

Projects & Awards

Projects

Awards

Teaching & Supervision

Teaching

Student Thesis Supervision

Talks & Services

Talks

Editorial Board

Program Committees

Peer Review