Zhejiang A&F University | China
Dr. Tongcun Liu is an Associate Professor at Zhejiang A & F University, specializing in computer science and technology with a strong focus on big data analytics and artificial intelligence. He earned his Ph.D. from the Beijing University of Posts and Telecommunications and later enhanced his academic experience as a Visiting Scholar at the Hong Kong University of Science and Technology. His research primarily revolves around advanced algorithms for graph computing, recommendation systems, and AI4Science, contributing significantly to the intersection of data intelligence and computational innovation. Dr. Liu leads multiple research projects funded by the National Natural Science Foundation of China and the Zhejiang Provincial Natural Science Foundation. His current and completed projects include the development of data-driven models for estimating mangrove soil dissolved organic carbon sequestration potential and the creation of cloud-edge collaborative recommendation systems based on session flow methods. With a robust publication record of more than 30 papers in esteemed international journals and conferences, his scholarly work has had a substantial impact on the field of artificial intelligence and data-driven computing. In addition to his academic achievements, Dr. Liu holds over 10 granted patents from more than 20 applications, reflecting his strong commitment to technological innovation and the advancement of AI-based computational methodologies.
Profile : Google Scholar
Featured Publications
Feng, H., Qiu, J., Wen, L., Zhang, J., Yang, J., Lyu, Z., Liu, T., & Fang, K. (2025). U3UNet: An accurate and reliable segmentation model for forest fire monitoring based on UAV vision. Neural Networks, 185, 107207.
Fang, K., Deng, J., Dong, C., Naseem, U., Liu, T., Feng, H., & Wang, W. (2025). MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network. Proceedings of the ACM on Web Conference 2025, 5065–5074.
Liu, T., Yu, G., Kwok, H. Y., Xue, R., He, D., & Liang, W. (2025). Enhancing tree-based machine learning for chlorophyll-a prediction in coastal seawater through spatiotemporal feature integration. Marine Environmental Research, 107170.
Shi, Q., Wang, Y., Liu, T., Zhang, L., & Liao, J. (2024). STRL: Writer-Independent Offline Signature Verification with Transformers and Self-Supervised Representation Learning. 2024 10th International Conference on Computer and Communications (ICCC).
Liu, T., Bao, X., Zhang, J., Fang, K., & Feng, H. (n.d.). Enhancing session-based recommendation with multi-interest hyperbolic representation networks. IEEE Transactions on Neural Networks and Learning Systems.