Quanxin Wang

Quanxin Wang

About me

I am Quanxin Wang, a senior undergraduate student majoring in Communication Engineering at the University of Electronic Science and Technology of China (Glasgow College, UESTC) and the University of Glasgow (James Watt School of Engineering, UoG). I am an undergraduate research assistant in Prof. Zhao Kang’s group, working on graph machine learning especially on understanding the geometric properties of relational data and developing efficient, transferable models for cross-domain graph learning.
I am currently pursuing a dual B.Eng. degree, expected to graduate in June 2027 and seeking Ph.D. opportunities.

Research Interests

  • Graph Neural Networks (GNN)/ Graph Learning
  • Prompt Learning & Graph Foundation Models (GFMs)

Education

DegreeInstitutionMajorPeriodNotes
B.Eng. (Hons)University of Glasgow (UoG)Electronics and Electrical Engineering2023 – 2027 (Expected) 
B.Eng.UESTCCommunication Engineering2023 – 2027 (Expected) 

Publications

Under Review

  • [1] S. Wang, X. Wang, Q. Wang, B. Wu, B. Wang, S. Huang, B. Deng, H. Liu, R. Fang, Z. Xu, B. Wang, and Z. Kang, “The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning,” submitted to NeurIPS 2026. arXiv PROJECT PAGE

  • [2] X. Xie, S. Wang, Q. Wang, X. Yu, R. Fang, B. Deng, E. Pan, Z. Kang, and Y. Fang, “Decoupling Structure and Features: Sequence-Centric Graph Foundation Models via Late Fusion,” submitted to NeurIPS 2026.

  • [3] Q. Wang, X. Xie, B. Li, X. Yu, S. Wang, R. Fang, and Z. Kang, “Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts,” submitted to AAAI 2027. arXiv

Research Experience

Undergraduate Research AssistantProf. Zhao Kang’s Group, UESTC (Jun 2025 – Present)

Teaching Experience

Teaching AssistantUESTC3036 - Artificial Intelligence & Machine Learning, UESTC × Glasgow (Fall 2026)

Honors

Scholarships

  • University Innovation Excellence Scholarship (2024–2025), Oct 2025
  • University Innovation Excellence Scholarship (2023–2024), Oct 2024
  • James-Watt Innovation First-class Scholarship (Top 4.7%), Oct 2024

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