Xiangyu Zhao

Xiangyu Zhao

PhD Candidate

Imperial College London

About me

Hi! I am a second-year PhD student at the Department of Electrical and Electronic Engineering, Imperial College London, supervised by Dr Aaron Zhao. I am also a memeber of the DeepWok Lab. My PhD research topic is to explore representation learning on higher-order graphs. My long-term research interest is to push the ability of machine learning systems in learning more complex data structures, while reducing their reliance on specialised human knowledge on specific tasks. In June 2022, I graduated as an MEng in Computer Science from Trinity College, University of Cambridge, with a result of distinction, and was awarded the Senior Scholarship.

Outside of academics, I am a keen clarinettist, a 1-kyu beginner kendoka, and a goalkeeper. I am a former member and soloist of the Cambridge University Chinese Orchestra Society, and performed in the orchestra’s Michaelmas Recitals and Annual Concerts in 2018, 2019, 2020 and 2022. I am also a member of the Imperial College Kendo Club and a former member of the Cambridge University Kendo Society, where I won the 2nd Place in the team matches in the 2020 University Taikai, and the 1st Place in the kyu-mixed individual matches in the 2023 Oxbridge Kendo Varsity matches.

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Interests
  • Graph Representation Learning
  • Complex Networks
  • Self-Supervised Learning
  • Reinforcement Learning
  • Generative Models
Education
  • PhD in Electrical and Electronic Engineering, 2023 –

    Imperial College London

  • MEng in Computer Science, 2021 – 2022

    University of Cambridge

  • BA in Computer Science, 2018 – 2021

    University of Cambridge

Publications

(2023). Will More Expressive Graph Neural Networks do Better on Generative Tasks?. In Proceedings of the 2nd Learning on Graphs Conference (LoG 2023).

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(2023). Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs. arXiv preprint arXiv:2306.05108.

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(2023). Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration. 11th International Conference on Learning Representations (ICLR 2023) Machine Learning for Drug Discovery (MLDD) Workshop.

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(2022). Towards a Competitive 3-Player Mahjong AI using Deep Reinforcement Learning. In 2022 IEEE Conference on Games (CoG).

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(2022). Building a 3-Player Mahjong AI using Deep Reinforcement Learning. arXiv preprint arXiv:2202.12847.

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