个人简介

2026年起加入天津大学合成生物与生物制造学院。

(1) 2000-09 至 2005-06, 浙江大学, 控制科学与工程学, 博士

(2) 1994-09 至 1998-06, 浙江大学, 控制科学与工程学/计算机科学, 双学士

研究领域

机器学习、深度学习、计算机视觉和自然语言处理等技术等智能算法在化工过程控制、结构健康监测、生物和医学等交叉领域的应用。

学术成就

(1) 生物工程与生物技术专业创新人才和实践能力拍养的探索与实践（排名第六），国家级教学成果奖二等奖，2009年。

(2) 智慧化工教学团队，全国石油和化工教育优秀教学团队（负责人） ，2025年

(3) 氟化工安全保障关键技术与装备研发及应用（排名第九），天津市科学技术奖，科学技术进步奖一等奖，2025年。

代表性文章（近5年，仅含通讯作者或第一作者）

AI+生医：

1. Zhu, J., et al., Progressively Helical Multi-Omics Data Fusion GCN and Its Application in Lung Adenocarcinoma. IEEE Access, 2023. 11: p. 73568-73582.

2. 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.

3. He, Q.-e., et al., DNA methylation loci identification for pan-cancer early-stage diagnosis and prognosis using a new distributed parallel partial least squares method. Frontiers in Genetics, 2022. 13.

4. 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.

AI+化工：

1. L. Ning et al., "QuaFT: Quality-guided semantic fault intelligence under corrupted industrial data," Process Safety and Environmental Protection, vol. 207, p. 108419, 2026/02/01/ 2026, doi: https://doi.org/10.1016/j.psep.2026.108419.

2. Zhao X., et al., High-Throughput Prediction of Metal-Embedded Complex Properties with a New GNN-Based Metal Attention Framework, Journal of Chemical Information and Modeling, 2025, DOI: 10.1021/acs.jcim.4c02163

3. Wang, R., et al., A new domain robust one-class fault detection framework for large-scale chemical processes, Chemical Engineering Science 2025 Vol. 306 Pages 121322

4. Zhou, K., et al., A novel multi-label classification deep learning method for hybrid fault diagnosis in complex industrial processes. Measurement(IF=4.8), 2025. 242: p. 115804.

5. Zhou, K., et al., Domain generalization of chemical process fault diagnosis by maximizing domain feature distribution alignment. Process Safety and Environmental Protection(IF=7.8), 2024. 185: p. 817-830.

6. 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.

7. Zhou, K., et al., Exploring global attention mechanism on fault detection and diagnosis for complex engineering processes. Process Safety and Environmental Protection (IF=7.8), 2023. 170: p. 660-669.

8. 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.

AI+材料：

1. Hou W., et al., Deciphering Key Features Determining Electrochemical Stability and Conductivity of Halide Solid-State Electrolytes, Advanced Functional Materials 2025 Vol. n/a Issue n/a Pages e24886

2. 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.

3. 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.

4. Sun, X., et al., A new cyclical generative adversarial network based data augmentation method for multiaxial fatigue life prediction. International Journal of Fatigue (IF=6,一区), 2022. 162: p. 106996.

5. 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.