Qiong Liu

LIP6, Sorbonne Université. 4 Place Jussieu, 75005 Paris.

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Maîtresse de conférences (Assistant Professor)

Sorbonne Université – LIP6

Paris, France

I am a Maîtresse de conférences (Assistant Professor) at Sorbonne Université, within the LIP6 laboratory (NPA team) in Paris, France.

My research focuses on the intersection of Artificial Intelligence and Networked Systems. I am particularly interested in operationalizing AI in high-speed softwarized infrastructures and developing tractable mathematical models using Stochastic Geometry and Queueing Theory.

Previously, I was an Assistant Professor at CY Cergy Paris University and a Postdoctoral Researcher at Télécom Paris. I received my Ph.D. from INSA Rennes in 2022.

Research Interests

  • AI for Networked Systems: MLOps, Performance Intelligence
  • Softwarized Infrastructures: NFV/SDN Architectures, High-Speed Software Data Planes
  • Stochastic Modeling: Stochastic Geometry, Queueing Theory

Education

  • PostDoc. in Computer Science, Telecom Paris, Institution polytechnique de Paris, France, 2024.
  • Ph.D. in Telecommunications, INSA Rennes, France, 2022.
  • M.Sc. in Telecommunication, Xidian University, China.
  • B.Sc. in Electronic Information, Shandong University, China.

news

Jan 14, 2026 🎉 Our paper “Queue-Aware RL Scheduling with Stability Bounds in Large-Scale Cellular Networks” has been accepted for publication at IEEE WCNC 2026! This work investigates adaptive transmission scheduling with theoretical stability guarantees. Stay tuned for the full paper!
Jan 14, 2026 We are looking for a motivated Master student for an internship on MLOps in Softwarized Networks. You can find the detailed Internship Proposition (PDF) here. Applications are open until March 2026. PhD tracks are also possible.
Jan 02, 2026 🎉 Our paper “Fast Collaborative Inference via Distributed Speculative Decoding” has been accepted by the Journal of Information and Intelligence, 2026.
Dec 20, 2025 Check out our latest work on Uplink RSMA Performance Analysis now available on arXiv (arXiv:2512.20883)! We provide a tractable stochastic geometry approach for rate adaptation in NGMA networks.