联系方式

[email protected] or [email protected]

研究方向

面向大食物观与精准营养重大需求，围绕食品健康大数据开展系统研究，重点开展人工智能与机器学习的理论、方法及关键技术创新，构建面向精准营养的智能模型、算法与决策支持体系。

招生信息

招收学术型硕士、专业型硕士及博士研究生，欢迎具有人工智能、计算机、自动控制、数据科学、软件工程、生物医学等相关背景的同学报考。

1、学术型硕士研究生

招生专业:软件工程

研究方向:(1)食品大数据分析; (2)健康大数据分析; (3)人工智能与机器学习

2、专业型硕士研究生

招生专业:软件工程、人工智能

研究内容: (1)食品大数据分析; (2)健康大数据分析; (3)工业大数据分析; (4)人工智能与机器学习

3、博士研究生招生专业:软件工程

研究方向: 人工智能与精准营养

研究成果

近年来，以第一作者和通讯作者发表SCI检索论文40余篇。主编本科生和研究生教材2部。主持国家自然科学基金面上项目、青年项目、江苏省青年基金项目。入选江苏省双创博士，污污漫 至善青年学者，无锡市太湖人才计划创新领军人才。曾获中国自动化学会优秀博士学位论文，浙江省自然科学奖二等奖。

代表论文

1. Shixin Gu, Guanhua Qiao, and Jinlin Zhu*. Microbexpert: A mixture-of-experts framework for cross-domain knowledge mining of food–microbe–disease associations from biomedical texts. Future Foods, 2026.13: 100990.

2. Qinghui Weng, Mingyi Hu, Guohao Peng, Wenwei Lu, Hongchao Wang, and Jinlin Zhu*. Variational Bayesian Multi-Output Gaussian Process Regression for Metabolic Profiles Prediction with Microbiome Data. IEEE Transactions on Computational Biology and Bioinformatics, 2026.23(2): p. 704-716.

3. Guanhua Qiao, Liming Nong, Chunyang Cheng, Zhongwei Shen, Jinlin Zhu*, and Hui Li. ProFood: Progressive RGB-D fusion network for food detection in complex diet scenes. Journal of Food Composition and Analysis, 2026.149: 108773.

4. Yangxiang Wu, Mingyi Hu, and Jinlin Zhu*. DMCFMDA: A dual-channel multi-source cross-modal fusion network with contrastive learning for microbe–disease prediction. Biomedical Signal Processing and Control, 2025.110: 108039.

5. Guanhua Qiao, Dachuan Zhang, Nana Zhang, Xiaotao Shen, Xidong Jiao, Wenwei Lu, Daming Fan, Jianxin Zhao, Hao Zhang, Wei Chen, and Jinlin Zhu*. Food Recommendation Towards Personalized Wellbeing. Trends in Food Science & Technology, 2025: 104877.

6. Liming Nong, Guohao Peng, Tianyang Xu, and Jinlin Zhu*. From ensemble to knowledge distillation: Improving large-scale food recognition. Engineering Applications of Artificial Intelligence, 2025: 110727.

7. Qinghui Weng, Mingyi Hu, Guohao Peng, and Jinlin Zhu*. DMoVGPE: predicting gut microbial associated metabolites profiles with deep mixture of variational Gaussian Process experts. BMC Bioinformatics, 2025: 93.

8. Xingke Gao, Jinlin Zhu*, Furong Gao, and Zheng Zhang*. Two-dimensional Adversarial Domain Adaptation Graph Contrastive Learning for Fault Diagnosis of Limited Similar Batch Process. Process Safety and Environmental Protection, 2025: 107017.

9. Huiqin Zhang, Jie Zhang, Ling Zhao, Bingqian Yu, Hao Zhang, Wenwei Lu*, and Jinlin Zhu*. Comprehensive Database for Food-Gut Microbiota-Disease Interactions (FGMDI) Analysis and Dietary Recommendation Applications. Food Bioscience, 2024: p. 104091.

10. Jinlin Zhu, Jialin Yin, Jing Chen, Mingyi Hu, Wenwei Lu, Hongchao Wang, Hao Zhang, and Wei Chen. Integrative analysis with microbial modelling and machine learning uncovers potential alleviators for ulcerative colitis. Gut Microbes, 2024.16(1): p. 2336877.

11. Jinlin Zhu, Heqiang Xie, Zixin Yang, Jing Chen, Jialin Yin, Peijun Tian, Hongchao Wang, Jianxin Zhao, Hao Zhang, Wenwei Lu, and Wei Chen. Statistical modeling of gut microbiota for personalized health status monitoring. Microbiome, 2023.11(1): p. 184.

12. Mingyi Hu, Jinlin Zhu*, Guohao Peng, Wenwei Lu, Hongchao Wang, and Zhenping Xie. IMOVNN: incomplete multi-omics data integration variational neural networks for gut microbiome disease prediction and biomarker identification. Briefings in Bioinformatics, 2023.24(6): p. bbad394.

13. Yang Shen, Jinlin Zhu*, Zhaohong Deng, Wenwei Lu, and Hongchao Wang. EnsDeepDP: an ensemble deep learning approach for disease prediction through metagenomics. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022.20(2): p. 986-998.

14. Yaoliang Liu, Jinlin Zhu*, Hongchao Wang, Wenwei Lu, Yuan Kun Lee, Jianxin Zhao, and Hao Zhang. Machine learning framework for gut microbiome biomarkers discovery and modulation analysis in large-scale obese population. BMC Genomics, 2022.23(1): p. 850.

15. Jinlin Zhu, Muyun Jiang, Guohao Peng, Le Yao, and Zhiqiang Ge. Scalable soft sensor for nonlinear industrial big data via bagging stochastic variational Gaussian processes. IEEE Transactions on Industrial Electronics, 2021.68(8): p. 7594-7602.

16. Jinlin Zhu, Yuan Yao, and Furong Gao. Multiphase two-dimensional time-slice dynamic system for batch process monitoring. Journal of Process Control, 2020: p. 184-198.

17. Jinlin Zhu, Youqing Wang, Donghua Zhou, and Furong Gao. Batch process modeling and monitoring with local outlier factor. IEEE Transactions on Control Systems Technology, 2019.27(4): p. 1552-1565.

18. Jinlin Zhu, Zhiqiang Ge, Zhihuan Song, and Furong Gao. Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data. Annual Reviews in Control, 2018: p. 107-133.

19. Jinlin Zhu, Zhiqiang Ge, Zhihuan Song, Le Zhou, and Guangjie Chen. Large-scale plant-wide process modeling and hierarchical monitoring: A distributed Bayesian network approach. Journal of Process Control, 2018: p. 91-106.

20. Jinlin Zhu, Zhiqiang Ge, and Zhihuan Song. Distributed parallel PCA for modeling and monitoring of large-scale plant-wide processes with big data. IEEE Transactions on Industrial Informatics, 2017.13(4): p. 1877-1885.