职称：助理教授
研究方向：大模型、图神经网络、机器学习、及相关人工智能技术在医疗和工程领域的应用
Email：houcb@fyust.edu.cn
侯诚彬，男，博士。福耀科技大学计算与人工智能学院助理教授，2022年获英国伯明翰大学计算机科学专业博士学位，曾在华为、腾讯AI Lab、福州数据技术研究院实习与工作，获福建省高层次人才C类、福州市高层次引进人才、英国工程技术协会IET Prize，受邀担任海峡两岸（福州）职工创新创业创造中心特聘AI导师。主要研究兴趣为大模型、图神经网络、机器学习、及相关人工智能技术在医疗和工程领域的应用，已在TPAMI、TNNLS、TKDE、IJCAI等国际顶尖期刊和会议上发表论文十余篇。
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] W. Mao, C. Hou*, T. Zhang, X. Lin, K. Tang, H. Lv*. Parse Trees Guided LLM Prompt Compression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025. (JCR-Q1, CCF-A，中科院一区TOP)
[2] J Song, Y Shen, C Hou*, P Wang, J Wang, K Tang, H Lv*. FedAGHN: Personalized Federated Learning with Attentive Graph HyperNetworks. Knowledge-based Systems, 2025.(JCR-Q1，中科院一区TOP)
[3] 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, selected as cover paper)
[4] 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)
[5] 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, 中科院一区TOP，CCF-B)
[6] 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)
[7] 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)
[8] 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)
[9] 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)
[10] 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)
[11] C. Hou, H. Zhang, S. He, K. Tang*. GloDyNE: Global Topology Preserving Dynamic Network Embedding (Extended Abstract). In IEEE ICDE 2022.
[12] 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)
[13] C. Hou, K. Tang*. Towards Robust Dynamic Network Embedding. In IJCAI 2021.(CCF-A)
[14] C. Hou, S. He*, K. Tang*. RoSANE: Robust and Scalable Attributed Network Embedding for Sparse Networks. Neurocomputing 2020. (JCR-Q1)
[15] G. Fu, C. Hou, X. Yao*. Learning Topological Representation for Networks via Hierarchical Sampling. In IJCNN 2019. (CCF-C)
Preprint
[16] 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.(Knowledge-based Systems, conditionally accept with revision)
[17] C. Hou, Y. Song, J. Song, W.C.C Chu, X. Fei, Jia Li, H. lv. KGC-Explainer: Towards Explainable Knowledge Graph Completion. (IEEE Transactions on Reliability, major revision)
[18] 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.
[19] 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.
[20] C. Hou, G. Fu, P. Yang, Z Hu, S. He, K. Tang*. Robust Dynamic Network Embedding via Ensembles. arXiv 2021.
著作
教育部十四五规划教材《人工智能与区块链》，闾海荣、杨旸、郑相涵、侯诚彬，清华大学出版社，2025.
更多信息详见
https://github.com/houchengbin
https://orcid.org/0000-0001-6648-793X
科研项目
[1] 清华大学委托项目，20250522，大模型驱动的知识标注技术研究，主持。
[2] 河南省卫生健康委员会，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，负责大模型技术研究。
[3] 国家自然科学基金，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，参与知识图谱构建方法研究。
[4] 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，负责动态图技术研究。
著作
教育部十四五规划教材《人工智能与区块链》，闾海荣、杨旸、郑相涵、侯诚彬，清华大学出版社，2025.
荣誉奖励
福建省高层次人才C类、福州市高层次引进人才、The IET Prize 2014 winner
