Ziyuan Ye

Ziyuan Ye

PhD Student

The Hong Kong Polytechnic University

Research Interests

Computational Neuroscience (primary)
Cognitive Science
Large Language Models
Explainable AI
Evolutionary Algorithms

About

I am a PhD student at the Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University (PolyU), advised by Prof. Jibin Wu and Prof. Kay Chen Tan. I also work closely with Prof. Yujie Wu and Prof. Guozhang Chen.

Prior to this, I obtained my M.Eng. degree in Biomedical Engineering from the Southern University of Science and Technology (SUSTech) in 2023, advised by Prof. Quanying Liu. Before that, I received my B.Eng. degree in Computer Science and Engineering from SUSTech in 2020, working with Prof. Jialin Liu and Prof. Xin Yao.

My current research mainly focuses on AI for Neuroscience.

Selected Publications

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KoopSTD: reliable similarity analysis between dynamical systems via approximating Koopman spectrum with timescale decoupling

Shimin Zhang, Ziyuan Ye, Yinsong Yan, Zeyang Song, Yujie Wu, Jibin Wu (equal contribution)

International Conference on Machine Learning (ICML)

A metric for measuring dynamical similarity between (neural/dynamical) systems by approximating the Koopman spectrum with timescale decoupling and spectral residual control.

SAME: Uncovering GNN black box with structure-aware shapley-based multipiece explanations

Ziyuan Ye, Rihan Huang, Qilin Wu, Quanying Liu (equal contribution)

Advances in Neural Information Processing Systems (NeurIPS)

A post-hoc explanation method for graph neural networks (GNNs) that uncovers structure-aware feature interactions using a Shapley-based multipiece explanation approach.

Explainable fMRI-based brain decoding via spatial temporal-pyramid graph convolutional network

Ziyuan Ye, Youzhi Qu, Zhichao Liang, Mo Wang, Quanying Liu

Human Brain Mapping

A biologically inspired architecture, Spatial Temporal-pyramid Graph Convolutional Network (STpGCN), to capture the spatial–temporal graph representation of functional brain activities for explainable fMRI-based brain decoding.