Ye XUE 薛烨

Associate Professor, Ph.D. Advisor School of Intelligent Systems Engineering Sun Yat-sen University Email: xuey57@mail.sysu.edu.cn Personal Site: https://yokoxue.github.io/ GitHub Google Scholar

Introduction

I am currently an associate professor at the School of Intelligent Systems Engineering, Sun Yat-sen University (SYSU). Before joining SYSU, I was a research scientist at the Shenzhen Research Institute of Big Data and an adjunct assistant professor at the School of Data Science at The Chinese University of Hong Kong, Shenzhen. I received my Ph.D. in Electronic and Computer Engineering from the Hong Kong University of Science and Technology (HKUST), and my B.S. in Communication Engineering from Southeast University (Chien‑Shiung Wu Honor College, Advanced Class). My research spans sparse/efficient AI, physically grounded field world models, and AI for mathematical optimization.

Research

I work on sparse/efficient AI, World Models (I call it FieldMind） for electromagnetics/wireless, and learning-based optimization (L2O) for real-world communication and network decisions.

• World Models (WM) for Radio Environments
  Physically grounded, data-driven models of radio/EM environments that fuse geometry and materials (BIM, point clouds, maps), contextual signals (vision/language, IMU, GNSS), and RF measurements (sweeps/arrays). We combine neural representations (3DGS/NeRF variants, diffusion/energy models, GNNs, neural operators such as FNO/DeepONet/GNO) with PDE and boundary consistency, constitutive relations, and structured sparsity/low-rank priors. The models support editable inversion, uncertainty quantification, and active re-measurement for localized statistical channel modeling and environment-aware communication.

• Learning-based Optimization (L2O)
  Differentiable, learning-augmented solvers (RL/GNN/neural combinatorial optimization) operating on WM for constrained decisions: UAV sensing plan, site placement, beam/power/spectrum allocation, routing/formation planning. We pursue feasibility guarantees, learned warm-starts, fast cross-scenario adaptation, and edge/federated execution for real-time, certifiable decisions.

• Sparse and Efficient AI
  Structured sparsity, low-rank, and tensorization across data/features/models/gradients with unified compression/distillation for edge–cloud collaboration and federated training. HW/SW co-acceleration (CPU/GPU/FPGA) enables reliable on-device deployment in wireless applications.

• Explainable artificial intelligence (XAI)
  Characterize the generalization bound and convergence properties of the AI method using high-dimensional statistics and nonconvex optimization.

Recently, I have focused on building FieldMind (our FM instantiation), which couples physics-informed field representations with algorithm–system co-design (C++/CUDA/FPGA; edge/federated) to map structural sparsity into solvers and hardware, delivering end-to-end speed/energy gains and rapid replanning.

Current Openings

• Positions: Postdoc Fellow, PhD, Master, Research Assistants (rolling).

• Application: CV, transcripts, representative work (papers/code/demos/competitions); optional personal site or GitHub.

• Email subject: Apply-Name-Position-StartTime

Selected Recent Publications

1. Xinyu Qin, Qi Yan, Shutao Zhang, Bingsheng Peng, Ye Xue^†, and Tsung-Hui Chang, “A Measurement Report Data-Driven Framework for Localized Statistical Channel Modeling,” IEEE Transactions on Mobile Computing, 2026. doi: 10.1109/TMC.2026.3667749.

2. Bingsheng Peng, Shutao Zhang, Xi. Zheng, Xinyu Qin, Ye Xue^†, and Tsung-Hui Chang, “RF-LSCM: Pushing Radiance Fields to Multi-Domain Localized Statistical Channel Modeling for Cellular Network Optimization,” accepted by IEEE Transactions on Mobile Computing, Mar. 2026.

3. Yiheng Wang, Ye Xue^†, Shutao Zhang, and Tsung-Hui Chang, “RadCloudSplat: Scatterer-Driven 3D Gaussian Splatting with Point-Cloud Priors for Radiomap Extrapolation,” accepted by IEEE INFOCOM 2026.

4. Xuanhao Pan^*, Chenguang Wang^*, Chaolong Ying, Ye Xue, and Tianshu Yu, “Beyond the Heatmap: A Rigorous Evaluation of Component Impact in MCTS-Based TSP Solvers,” accepted by ICLR 2026.

5. Yeqing Qiu, Ye Xue^†, Akang Wang, Yiheng Wang, Qingjiang Shi, and Zhi-Quan Luo, “ROS: A GNN-based Relax-Optimize-and-Sample Framework for Max-k-Cut Problems,” International Conference on Machine Learning (ICML 2025).

6. Yiheng Wang^*, Ye Xue^†, Shutao Zhang, and Tsung-Hui Chang, “GNN-based Structured Bayesian Inference for Multi-grid Localized Statistical Channel Modeling,” IEEE Transactions on Wireless Communications, 2025. doi: 10.1109/TWC.2025.3547705.

7. Shutao Zhang, Ye Xue^†, Zhiwei Tang, Hao Wang, Chao Shen, Qingjiang Shi, and Tsung-Hui Chang, “Robust Network Optimization by Deep Generative Models and Stochastic Optimization,” IEEE Transactions on Wireless Communications, 2025. doi: 10.1109/TWC.2025.3551316.

8. Yeqing Qiu, Chengpiao Huang, Ye Xue^†, Zhipeng Jiang, Qingjiang Shi, Dong Zhang, and Zhi-Quan Luo, “Relaxation-free Min-k-partition for PCI Assignment in 5G Networks,” IEEE Transactions on Signal Processing, 2025. doi: 10.1109/TSP.2025.3604409.

9. Yeqing Qiu, Ye Xue^†, Zhipeng Jiang, and Qingjiang Shi, “Relaxed Gradient Projection for PCI Assignment in 5G Network,” The 14th IEEE/CIC International Conference on Communications in China (ICCC 2025).

10. Xinyu Qin, Shutao Zhang, Bingsheng Peng, Ye Xue^†, Chao Shen, Qiliang Xie, Yuk Ngai Lee, and Tsung-Hui Chang, “A Deep Learning Framework for Large-Scale Localized Statistical Channel Modeling,” IEEE Global Communications Conference (GLOBECOM 2025) Workshop.

11. Yiheng Wang, Shutao Zhang, Ye Xue^†, and Tsung-Hui Chang, “Multi-Modal Neural Radio Radiance Field for Localized Statistical Channel Modelling,” IEEE Global Communications Conference (GLOBECOM 2025) Workshop.

[^* equal contribution, ^† corresponding author]   Full list of publications.

Funding

• NSFC Fund (62301334), 2024–2026, PI.
• National Key R&D Program of China (2023YFB2904800), 2023–2026, Sub-project PI.
• SRIBD Project (J00220230002), 2023–2024, PI.
• National Key R&D Program of China (2022YFA1003900), 2022–2027, Key member.
• Guangdong Major Project (2023B0303000001), 2023–2028, Core member.

Honors and Awards

• Specific Talent, Shenzhen “Pengcheng Peacock Program,” 2023.
• Postgraduate Studentship, Hong Kong University of Science and Technology (HKUST), 2017.
• First Prize, Excellent Undergraduate Thesis of Jiangsu Province, 2017 (one of 5 awardees across SEU).
• Baosteel Scholarship, 2016.
• Microsoft Research Asia Young Scholar Award, 2015 (one of 32 awardees across Mainland China).
• RoboCup Kidsize, National Level, 2nd Place, 2015.