Xiangyu Zhao

Xiangyu Zhao

PhD Student

Imperial College London

About me

Hi! I am a first-year PhD student at the Department of Electrical and Electronic Engineering, Imperial College London, supervised by Dr Aaron Zhao. I am also working at the Department of Computer Science and Technology, University of Cambridge as a visiting student, where I am co-supervised by Prof Pietro Liò. My PhD research topic is to design principled self-supervised learning methods on complex hypergraphs that do not require any augmentations. 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 2-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 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
  • Self-Supervised Learning
  • Graph Representation 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). Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration. 11th International Conference on Learning Representations (ICLR 2023) Machine Learning for Drug Discovery Workshop.

PDF Cite Code Project

(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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