Biography
Ye Liu, Ph.D., is currently an Associate Professor and Doctoral Supervisor at the School of Future Technology, South China University of Technology (SCUT). Her research focuses on multimodal integration theory and algorithms, graph machine learning theory and algorithms, and multi-omics/spatial omics data analysis
She has published over 20 papers in international journals and key conferences, including IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), The Conference on Neural Information Processing Systems(NeurIPS), AAAI Conference on Artificial Intelligence (AAAI), Pattern Recognition (PR), IEEE/ACM Transactions on Computational Biology and Bioinformatics (IEEE/ACM TCBB), and SIAM Journal on Scientific Computing (SIAM SISC). Among these, 15 are first-authored or corresponding-authored papers.
She has presided over 4 national and provincial-level projects and participated in 3 others. She serves as a Communication Committee Member of the Bioinformatics and Artificial Life Professional Committee of the Chinese Association for Artificial Intelligence（CAAI）, and a Committee Member of the Bio-3News Special Committee of the China Computer Federation（CCF）. Additionally, She acts as a reviewer for multiple top journals and conferences, such as IEEE TNNLS, IEEE TKDD, AAAI, and IJCAI.
Education
2016–2019: Ph.D. in Mathematics, Hong Kong Baptist University (Supervisor: Michael Ng)
2012–2016: Bachelor’s Degree in Computer Science, Beijing Jiaotong University
Work Experience
August 2021–Present: Associate Professor, South China University of Technology
September 2020–June 2021: Postdoctoral Fellow, Department of Computer Science, The University of Hong Kong (Supervisor: Reynold Cheng)
November 2019–August 2020: Research Fellow, Department of Mathematics, The University of Hong Kong (Supervisor: Michael Ng)
October 2019: Postdoctoral Fellow, Department of Mathematics, Hong Kong Baptist University
Research Interests
·Theory and Application of Multimodal Machine Learning: Cross-modal alignment and fusion, cross-modal data generation, multimodal robust learning.
·Theory and Application of Graph Machine Learning: Graph self-supervised learning and interpretability analysis, spatiotemporal graph representation learning, large-scale heterogeneous graph representation learning.
·Bioinformatics: Spatial omics data analysis, cell dynamic modeling of spatiotemporal omics data, digital cells.
Students interested in joining her research group are welcome to contact her via email: yliu03@scut.edu.cn
Courses Taught
Python Programming Language, Introduction to Engineering
Research Projects
1.Youth Science Fund Project of the National Natural Science Foundation of China, January 1, 2024–December 31, 2026 (Principal Investigator).
2.Guangzhou Science and Technology Program Project, April 1, 2023–March 31, 2025 (Principal Investigator).
3.Industry-University-Research Innovation Fund of the Ministry of Education, December 1, 2024–December 31, 2025 (Principal Investigator).
4.Enterprise-Commissioned Development Project, October 2023–October 2027 (Principal Investigator).
5.Key Research and Development Program of Guangdong Province, 2026–2028 (Participant).
6.Project of the National Key Research and Development Program "Science and Technology Response to Active Health and Population Aging", 2022–2025 (Participant).
7.Guangdong Key Laboratory of Digital Twin Humans, 2021–2024 (Participant).
8.Excellent Young Research Team of Central Universities, 2023–2025 (Participant).
Selected Publications
[1] Ye Liu, Hongshan Pu, Junjun Pan, Michael K. Ng, Hongmin Cai*. Anchor-based Multi-view Subspace Clustering with Anchor-wise and Class-wise Alignments. IEEE Transactions on Neural Networks and Learning Systems, 2025. doi: 10.1109/TNNLS.2025.3589264.
[2] Ye Liu, Chaoxiong Lin, Yuchen Mou, Huaiguang Jiang, Hongmin Cai*. QSTGNN: Quaternion Spatio-Temporal Graph Neural Networks, in IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 8, pp. 4776-4790, Aug. 2025, doi: 10.1109/TKDE.2025.3571983
[3] Zihan Ji; Xuetao Tian; Ye Liu*. AFFAKT: A Hierarchical Optimal Transport based Method for Affective Facial Knowledge Transfer in Video Deception Detection. Proceedings of the AAAI Conference on Artificial Intelligence (2025), 39(2), 1336-1344. Oral paper.
[4] Ye Liu*; Xuelei Lin; Yejia Chen; Reynold Cheng. Multi-order graph clustering with adaptive node-level weight learning. Pattern Recognition, 2024, 156: 110843.
[5] Ye Liu; Michael K. Ng*; Stephen Wu. Multi-Domain Networks Association for Biological Data Using Block Signed Graph Clustering, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020, 17(2): 435-448.
