CV

Yingming Mao

Ph.D. student in Control Science and Engineering

maoyingming@sii.edu.cn
Shanghai / Xi'an, , CN

Summary

Ph.D. student at Xi'an Jiaotong University and Shanghai Innovation Institute focusing on AI infrastructure, large-scale network optimization, and intelligent power systems.

Education

  • Control Science and Engineering
    Xi'an Jiaotong University & Shanghai Innovation Institute
  • Automation
    2022
    Xi'an Jiaotong University

Skills

Data Center Networks

  • Traffic engineering
  • Topology engineering
  • Reconfigurable networks

Power and Energy Systems

  • Microgrid management
  • Optimal scheduling
  • Power flow modeling

AI Infrastructure

  • Large-scale network optimization
  • Data generation for power systems

Publications

  • NeuroRisk: Physics-Informed Neural Optimization for Risk-Aware Traffic Engineering
    2026
    arXiv preprint arXiv:2605.12862
    Physics-informed neural optimizer for risk-aware traffic engineering in WANs with correlated failure scenarios.
  • A Fast Solver-Free Algorithm for Traffic Engineering in Large-Scale Data Center Network
    2025
    arXiv preprint arXiv:2504.04027 (accepted by NSDI '26)
    Algorithm for traffic engineering in large-scale data center networks without heavy solvers.
  • ATRO: A Fast Algorithm for Topology Engineering of Reconfigurable Datacenter Networks
    2025
    arXiv preprint arXiv:2507.13717 (accepted by INFOCOM '26)
    Fast topology engineering approach for reconfigurable data center networks.
  • Duonet: Learning the Duality-based Topology-Agnostic Update Operator for Lightweight Traffic Engineering in Changing Topologies
    2025
    Accepted by NSDI '26
    Topology-agnostic update operator enabling lightweight traffic engineering across changing topologies.
  • A method for evaluating and improving linear power flow models in system with large fluctuations
    2023
    International Journal of Electrical Power & Energy Systems
    Evaluation and improvement of linear power flow models for systems with large fluctuations.
  • Data generation method for power system operation considering geographical correlations and actual operation characteristics
    2023
    Energy Reports
    Data generation approach for power system operations that captures spatial correlations and real-world characteristics.

Interests

  • Data center networks
  • Microgrid management
  • Power system analysis