教育背景

2022年7月，University of Birmingham, PhD；

2015年11月，Imperial College London, MSc (Distinction)；

2014年7月，University of Liverpool, BEng (First Class)。

科研相关工作经历：

2024-02 至 今，福耀科技大学，计算与人工智能学院，助理教授

2022-05 至 2024-02，福州数据技术研究院，前沿技术研究岗，机器学习研究员

2021-12 至 2022-04，腾讯，AI LAB，研究实习生

论文著作（* 通讯作者）
Peer-reviewed
[1] T. Zhang, C. Hou*, R. Jiang, X. Zhang, C. Zhou, K. Tang, H. Lv*. Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs. IEEE Transactions on Neural Networks and Learning Systems 2024. (JCR-Q1, CCF-B)
[2] H. Zhu, G. Xie, C. Hou, T. Dai, C. Gao, J. Wang*, L. Shen*. Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning. In ACM MM 2024. (CCF-A)
[3] C. Hou, K. Liu, T. Wang, S. Shi*, Y. Li, Y. Zhu, X. Hu, C. Wang, C. Zhou, H. Lv*. DDE KG Editor: A Data Service System for Knowledge Graph Construction in Geoscience. Geoscience Data Journal 2024. (JCR-Q2)
[4] C. Hou, X. Lin, H. Huang, S. Xu, J. Fan, Y. Shi*, H. Lv*. Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews. Methods in Ecology and Evolution 2023. (JCR-Q1)
[5] C. Hou, H. Zhang, S. He, K. Tang*. GloDyNE: Global Topology Preserving Dynamic Network Embedding. IEEE Transactions on Knowledge and Data Engineering 2022. (JCR-Q1, CCF-A)
[6] C. Hou, H. Zhang, S. He, K. Tang*. GloDyNE: Global Topology Preserving Dynamic Network Embedding (Extended Abstract). In IEEE ICDE 2022.
[7] H. Zhang, C. Hou, D. McDonald, S. He*. A Network Embedding Based Approach to Drug-Target Interaction Prediction Using Additional Implicit Networks. In ICANN 2021. (CCF-C)
[8] C. Hou, K. Tang*. Towards Robust Dynamic Network Embedding. In IJCAI 2021.
[9] C. Hou, S. He*, K. Tang*. RoSANE: Robust and Scalable Attributed Network Embedding for Sparse Networks. Neurocomputing 2020. (JCR-Q1)
[10] G. Fu, C. Hou, X. Yao*. Learning Topological Representation for Networks via Hierarchical Sampling. In IJCNN 2019. (CCF-C)
[11] T. Zhang, Y. Ren, C. Hou, H. Lv*, X. Zhang. Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small Models. In IEEE BIBM 2024. (CCF-B, to appear)
[12] H. Liang, G. Xie, C. Hou, B. Wang, C. Gao, J. Wang*. Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection. In AAAI 2025. (CCF-A, to appear)
[13] C. Hou, Y. Gao, X. Lin, J. Wu, N. Li, H. Lv, C. Chu*. A Review of Recent Artificial Intelligence for Traditional Medicine. Journal of Traditional and Complementary Medicine, 2025. (JCR-Q1, to appear)

Preprint
[14] X. Lin, T. Zhang, C. Hou*, J. Wang, J. Xue, H. Lv. Node Importance Estimation Leveraging LLMs for Semantic Augmentation in Knowledge Graphs. arXiv 2024.
[15] W. Mao, C. Hou*, T. Zhang, X. Lin, K. Tang, H. Lv*. Parse Trees Guided LLM Prompt Compression. arXiv 2024.
[16] B. Wu, J. Li, J. Yu, Y. Bian, H. Zhang, C. Chen, C. Hou, G. Fu, L. Chen, T. Xu, Y. Rong. A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. arXiv 2022.
[17] J. Li, B. Wu*, C. Hou, G. Fu, Y. Bian, L. Chen, J. Huang, Z. Zheng. Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack. arXiv 2022.
[18] C. Hou, G. Fu, P. Yang, Z Hu, S. He, K. Tang*. Robust Dynamic Network Embedding via Ensembles. arXiv 2021.
[19] J Song, Y Shen, C Hou*, P Wang, J Wang, K Tang, H Lv*. FedAGHN: Personalized Federated Learning with Attentive Graph HyperNetworks. arXiv 2025.

更多信息详见
https://github.com/houchengbin
https://orcid.org/0000-0001-6648-793X

科研项目
[1] 河南省卫生健康委员会，省部共建重大项目，SBGJ202401001，大模型驱动的高血压发病机制和筛查模型/干预模式研究及应用，2025-01-01 至 2027-12-31，参与，本人负责大模型技术研究。
[2] 国家自然科学基金，专项项目，42050101，地球科学知识图谱表示模式与群智协同构建，2021-01-01 至 2023-12-31，参与，本人参与知识图谱构建方法研究。
[3] 华为-南科大 RAMS 实验室项目“鲁棒动态网络嵌入技术研究”，2020-04-07 至 2021-04-06，参与，本人负责动态图技术研究。

荣誉奖励

福州市高层次引进人才（2023）；

华为-南科大 RAMS 实验室项目优秀学生奖（2022）；

华为供应链数据技术奖（2017）；

The IET Prize 2014 winner (2014)。