机器学习；

计算机视觉；

过程控制；

故障检测与诊断；

深度学习；

大模型

过程装备控制技术及应用；

化工智能控制；

数据智能与先进控制；

机器学习与智慧化工

2005年毕业于浙江大学控制科学与工程专业获博士学位，2013-2015美国得克萨斯大学UTSouthWestern医学中心做访问副教授。

主要从事机器学习、大数据智能、计算机视觉和云数据库技术等智能算法在工业控制、结构健康监测、生物和医学等交叉领域的应用。在化工过程智能监控等方面承担或参与过国家级项目8项；在橡胶混炼智能控制等方面拥有7项国家授权的发明专利。

故障检测与诊断；

三维视觉重构；

基于AI生成的金相分析；

无损探伤；

基因组学分析；

图像预后分析；

基于AI的DFT分子性能预测加速等。

1. R. Wang, K. Zhou, H. Huang, G. Shan, F. Qiu, L. Zhu, et al., A new domain robust one-class fault detection framework for large-scale chemical processes, Chemical Engineering Science 2025 Vol. 306 Pages 121322

2. X. Zhao, B. Wang, K. Zhou, J. Wu and K. Song, High-Throughput Prediction of Metal-Embedded Complex Properties with a New GNN-Based Metal Attention Framework, Journal of Chemical Information and Modeling 2025

3. Zhang, J., et al., A soft scanning electron microscopy for efficient segmentation of alloy microstructures based on a new self-supervised pre-training deep learning network. Materials Characterization(IF=4.8), 2024. 218: p. 114532.

4. Zhou, K., et al., Image restoration through few-mode fiber using a new comprehensive attention model. Optics & Laser Technology(IF=4.6), 2024. 178: p. 111236.

5. Huang, H., et al., CausalViT: Domain generalization for chemical engineering process fault detection and diagnosis. Process Safety and Environmental Protection (IF=7.8), 2023. 176: p. 155-165.

6. Wei, X., et al., Exploring fast-inferring in transformer backboned model for fatigue crack detection and propagation tracking for proton exchange membrane. Journal of Power Sources (IF=9.2), 2023. 573: p. 233129.

7. Tong, Y.-f., et al., Multi-omics Differential Gene Regulatory Network Inference for Lung

a. Adenocarcinoma Tumor Progression Biomarker Discovery. AIChE Journal (IF=3.7), 2022. p. e17574.

8. Zhou, K., et al., Machine learning-based genetic feature identification and fatigue life prediction. Fatigue & Fracture of Engineering Materials & Structures (IF=3.7, Top cited papers and generated immediate impact), 2021. n/a(n/a): p. 1-14.

9. Tan, Y.-S., et al., Protein acetylation regulates xylose metabolism during adaptation of Saccharomyces cerevisiae. Biotechnology for Biofuels (IF=6.6), 2021. 14(1): p. 241.

其他代表作：

1、Song, K., Recognition of prokaryotic promoters based on a novel variable-window Z-curve method. Nucleic Acids Research(IF=16.6), 2012. 40(3): p. 963-971. 独立作者

2、《合成生物学导论》，2012年，科学出版社。独立作者