Basic Information


The Hong Kong Polytechnic University (PolyU)
Hong Kong

Email: ziyuanye9801@gmail.com
LinkedIn: Ziyuan's LinkedIn

Biography

I am a first-year PhD student in Computer Science at The Hong Kong Polytechnic University (PolyU) since Sep. 2023, advised by Prof. Jibin Wu and Prof. TAN Kay Chen.

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

I am now interested in NeuroAI (both AI for Neuroscience and Neuroscience for AI).

Publications

Hypergraph Transformer for Semi-supervised Classification

Zexi Liu, Bohan Tang, Ziyuan Ye, Xiaowen Dong, Siheng Chen, and Yanfeng Wang
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024
[paper] [code] [slides]

SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanation

Ziyuan Ye*, Rihan Huang*, Qilin Wu, and Quanying Liu (* equal contribution)
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023
[paper] [code] [slides] [website]

Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network

Ziyuan Ye, Youzhi Qu, Zhichao Liang, Mo, Wang, and Quanying Liu
Human Brain Mapping, 2023
[paper] [code] [slides]

Immunofluorescence Capillary Imaging Segmentation: Cases Study

Runpeng Hou, Ziyuan Ye, Chengyu Yang, Linhao Fu, Chao Liu, and Quanying Liu
Proceedings of the 30th ACM International Conference on Multimedia (ACMMM), 2022
[paper] [code]

Region-focused Memetic Algorithms with Smart Initialisation for Real-world Large-scale Waste Collection Problems

Wenxing Lan*, Ziyuan Ye*, Peijun Ruan*, Jialin Liu, Peng Yang, and Xin Yao (* equal contribution)
IEEE Transactions on Evolutionary Computation (TEVC), 2021
[paper] [code] [dataset] [news]

Fluorination Enhances NIR‐II Fluorescence of Polymer Dots for Quantitative Brain Tumor Imaging

Ye Liu, Jinfeng Liu, Dandan Chen, Xiaosha Wang, Zhe Zhang, Yicheng Yang, Lihui Jiang, Weizhi Qi, Ziyuan Ye, Shuqing He, Quanying Liu, Lei Xi, Yingping Zou, and Changfeng Wu
Angewandte Chemie, 2020
[paper]

Air Pollutants Prediction in Shenzhen Based on ARIMA and Prophet Method

Ziyuan Ye
E3S Web of Conferences. EDP Sciences, 2019
[paper]

Preprints and Submissions

A computational framework to unify representation similarity and function in biological and artificial neural networks

Xuming Ran, Jie Zhang, Ziyuan Ye, Haiyan Wu, Qi Xu, Huihui Zhou, and Quanying Liu
Under Review
[paper]

Talks

Nature Neuroscience'24-Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks . Reading Group Talk @ PolyU.

Nature'24-A virtual rodent predicts the structure of neural activity across behaviors . Reading Group Talk @ PolyU.

Consciousness and AI . Reading Group Talk @ PolyU.

ICLR'23-Hebbian Deep Learning Without Feedback . Reading Group Talk @ SUSTech NCCLab.

ICLR'23-Disentangling with Biological Constraints: A Theory of Functional Cell Types . Best Paper of 2022 Award @ SUSTech NCCLab.

ICLR'22-Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond . Reading Group Talk @ SJTU Multi-Agent Graph Intelligence Crew.

ArXiv'22-Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning . Reading Group Talk @ SJTU Multi-Agent Graph Intelligence Crew.

NeurIPS'22-Pure Transformers are Powerful Graph Learners . Reading Group Talk @ SJTU Multi-Agent Graph Intelligence Crew.

NeurIPS'18-Hierarchical Graph Representation Learning with Differentiable Pooling . Reading Group Talk @ SUSTech NCCLab.

Proc.IEEE'21-Toward Causal Representation Learning . Reading Group Talk @ SJTU Multi-Agent Graph Intelligence Crew.

ICLR'19-How Powerful are Graph Neural Networks? Reading Group Talk @ SUSTech NCCLab.

Explainability in Graph Neural Networks: Recent Advances . Reading Group Talk @ SUSTech NCCLab.

TPAMI'21-Line graph neural networks for link Prediction (in Chinese). Best Paper of 2021 Award @ SUSTech NCCLab.

Motto

"Pain is inevitable but suffering is optional." --by Haruki Murakami @ "What I Talk About When I Talk About Running"