Xiaomin Liu

Xiaomin Liu

刘晓敏

Shanghai Jiao Tong University (SJTU)

I’m a Ph.D candidate for Information engineering at the Department of Electronic engineering (电子系), Shanghai Jiao Tong University, Shanghai, China, and under the supervision of Professor Qunbi Zhuge. I graduated from Shanghai Jiao Tong University with a bachelor’s degree of Information Enginerring (AI Special Class) from the Department of Electronic engineering (电子系).

My research focus on the modeling, monitoring, and optimization for the long-haul WDM optical networks (especially the Backbone networks), aim at building self-driving optical networks with the assistance of data-driven machine learning techniques. I am also working on the control and optimization for the multi-band optical system.

My CV is also available to download here.

I am looking for job opportunities from the fall of 2025.

Ciao! Now I am a visiting student to Politecnico di Milano, working with Prof. Massimo Tornatore.

🔥 News

🎖 Honors and Awards

  • 2024.12, Academic Star at Shanghai Jiao Tong University (～0.03%, only 10 each year, the highest honor for graduate students in SJTU)
  • 2024.11, First Prize of the Outstanding Ph.D. Student of State Key Laboratory of Advanced Optical Communication Systems and Networks
  • 2024.09, China National Scholarship (3 Times in Ph.D., Top 2%)
  • 2024.03, Corning Woman Scholar (3 worldwide each year, awarded in OFC, 2024)
  • 2023.06, SPIE Optics and Photonics Education Scholarship (6 in China and 72 worldwide each year)
  • 2022.10, Intel Scholarship (5 in Shanghai Jiao Tong University and 65 in China)
  • 2022, First Prize of the Outstanding Ph.D. Student of State Key Laboratory of Advanced Optical Communication Systems and Networks
  • 2021.10, Best Student Paper Award of Asia Communications and Photonics Conference
  • 2020, 2021, China National Scholarship (2 Times, Top 2%)
  • 2020.06, Excellent Bachelor Thesis of Shanghai Jiao Tong University (Top 1%)
  • 2020, Shanghai Outstanding Graduates (Top 5%)
  • 2017, 2018, 2019, The Academic Scholarship of Shanghai Jiao Tong University

📝 Publications

I have pubslihed 7 first-authored journal papers and 8 first-authored conference papers. Here are some selected publications. For full publication list, please to my CV. †:Joint first-author.

🚀 Autonomous Driving Optical Networks

OFC2025
sym

First field trial of LLM-powered AI agent for lifecycle management of autonomous driving optical networks
Xiaomin Liu, Qizhi Qiu, Yihao Zhang, Yuming Cheng, Lilin Yi, Weisheng Hu, Qunbi Zhuge*

  • Accepted by Optical Fiber Communication Conference (OFC) 2025
  • Build the world first field trial demonstration of the LLM-powered AI agent for the network lifecycle.
  • Support diverse network events, including wavelength add/drop,failure management, and power optimization.
  • Show the potentials of LLM to control and manage the autonomous optical networks.
OFC2024
sym

Auto-DTWave: Digital Twin-Aided Autonomous Optical Network Operation with Continuous Wavelength Loading
Xiaomin Liu, Qizhi Qiu, Yihao Zhang, Meng Cai, Yichen Liu, Lilin Yi, Weisheng Hu, Qunbi Zhuge*

  • Accepted by Optical Fiber Communication Conference (OFC) 2024
  • Develop joint online digital twin (DT) construction and amplifier configuration with continuous wavelength loading in a commercial testbed. The DT achieves an RMSE of 0.37dB, assisting near-optimal amplifier configuration with <0.1dB average Q-factor deviation.
Advanced Photonics 2024
sym

Digital Twin Modeling and controlling of Optical Power Evolution Enabling Autonomous-driving Optical Networks: A Bayesian Approach
Xiaomin Liu, Yihao Zhang, Yuli Chen, Yichen Liu, Meng Cai, Qizhi Qiu, Mengfan Fu, Lilin Yi, Weisheng Hu, Qunbi Zhuge*

  • Accepted by Advanced Photonics （IF= 20.6）
  • Propose a Bayesian inference framework (BIF) to construct the digital twin of OAs and control OPE in a data-efficient manner. Only the informative data are collected to balance the exploration and exploitation of the data space, thus enabling efficient autonomous-driving optical networks (ADONs).
JLT2024 & ECOC2023
sym

SMOF: Simultaneous Modeling and Optimization Framework for Raman Amplifiers in C+L-band Optical Networks
Xiaomin Liu, Yihao Zhang, Meng Cai, Yichen Liu, Lilin Yi, Weisheng Hu, Qunbi Zhuge*

🚀 Digital Twin and Physical Layer Modeling

JOCN 2023
sym

Building A Digital Twin for Intelligent Optical Networks [Invited Tutorial]
Qunbi Zhuge*†,Xiaomin Liu†, Yihao Zhang, Meng Cai, Yichen Liu, Qizhi Qiu, Xueying Zhong, Jiaping Wu, Ruoxuan Gao Lilin Yi, Weisheng Hu

  • Accepted by Journal of Optical Communication and Networking JOCN.
  • Introduce and discuss three key technologies, including modeling, telemetry, and self-learning, to build a DT for optical networks.
JLT2022 & ACP2021
sym

Fusing Physics to Fiber Nonlinearity Model for Optical Networks Based on Physics-Guided Neural Networks
Xiaomin Liu, Yunyun Fan, Yihao Zhang, Meng Cai, Lei Liu, Lilin Yi, Weisheng Hu, Qunbi Zhuge*

  • Accepted by Journal of Lightwave Technology (JLT), extended from ACP 2021 as the Best student paper.
  • Propose a fiber model based on the physics-guided neural networks (PGNN).The PGNN-based model is compared with traditional neural networks in an experimental link to further illustrate its superior accuracy and generalizability.
JLT2022 & ECOC2020
sym

A Meta-Learning-Assisted Training Framework for Physical Layer Modeling in Optical Networks
Xiaomin Liu, Huazhi Lun, Lei Liu, Yihao Zhang, Yichen Liu, Lilin Yi, Weisheng Hu, Qunbi Zhuge*

  • Accepted by Journal of Lightwave Technology (JLT), extended from ECOC 2020.
  • Propose a meta-learning-assisted training framework for machine-learning-based physical layer models. This framework can improve the model robustness to agnostic uncertain parameters during offline training and enables the model to efficiently adapt to the real system with fewer data.

🚀 Network Telemetry

JLT 2021
sym

A Data-Fusion-Assisted Telemetry Layer for Autonomous Optical Networks [Invited Paper]
Xiaomin Liu, Huazhi Lun, Ruoxuan Gao, Meng Cai, Lilin Yi, Weisheng Hu, Qunbi Zhuge*

  • Accepted by Journal of Lightwave Technology (JLT).
  • A data-fusion-assisted telemetry layer between the physical layer and control layer is proposed. The data fusion methodologies are elaborated on three different levels: Source Level, Space Level and Model Level.

📖 Educations

  • 2020.09 - Now, PhD student, Shanghai Jiao Tong University, Shanghai.
  • 2016.09 - 2020.06, Undergraduate, Shanghai Jiao Tong University, Shanghai.

💬 Invited Talks

  • 2022.07, Xiaomin Liu, “Fusion of physics and AI for building self-driving optical networks” Advanced Photonics Congress (APC), Photonic Networks and Devices Conference (NETWORKS), Maastricht, The Netherlands, 2022. (The only student invited speaker)
  • 2022.09, Xiaomin Liu, “Combining AI and physics for digital-twin optical networks”, Optoelectronics Global Conference (OGC 2022), Shenzhen, China, 2022.
  • 2021.09, Xiaomin Liu, “Design and deployment of the data-driven fiber nonlinearity estimation for dynamics optical networks”, Optoelectronics Global Conference (OGC 2021), Shenzhen, China, 2021.

This is forked from open-sourced template version AcadHomepage .