导师简介

朱宏明博士现为同济大学计算机科学与技术学院副教授。研究方向主要包括：对源异构数据融合、多Agent复杂系统、时空数据分析处理、目标识别分割和变化检测等领域，研究内容应用于地理空间基础模型、空间计算平台、城市更新监测、疾病药敏分析等领域。

拟招收对以上研究内容感兴趣的本科生、硕士和博士研究生，提供机会参与企业以及国际高校的合作研究交流，有意向同学请发送邮件至zhu_hongming@tongji.edu.cn。

近5年项目

1.基于空间计算的地理空间基础模型研究，2026，主持

2.基于高密度生物信息和跨尺度信息融合的抗癌协同药物组合预测方法，国家自然科学基金，2026，参与

3.变更复杂度度量模型研究，2025，主持

4.基于多Agent空间计算管理和规划平台研究，2025，主持

5.虚拟ECU软件系统及其应用项目，2024，主持

6.大规模人形机器人标准化数据集构建技术研究与应用验证，上海市科委，2024，主持

7.宜居环境综合品质调控效果多城市验证优化，科技部重点研发计划，2023，参与

8.汽车电子开源软件应用可行性研究，2023，主持

9.“智能衣柜”服饰搭配引擎原型开发，2022，主持

10.政务数据的语义化分析技术、意图识别模型和智能审批全数据链的RPA流程配置技术研究，上海市科委，2022，主持

11.多源异构大型航空枢纽数据融合和相似度计算方法的研究，上海市科委，2022，主持

12.尿细胞染色控制系统的设计与实现，上海市科委，2022，主持

13.基于深度学习的典型要素提取识别与融合技术研究（二级课题），科技部，2022，主持

近5年论文

期刊Journal Papers

1.Zhu, H., Dai, Q., Du, B., Liu, S., & Liu, Q. (2026). CAB-SCD: Semantic Change Detection Based on Cost Aggregation. IEEE Transactions on Geoscience and Remote Sensing.

2.Zhu, H., Chen, H., Du, B., Liu, S., & Liu, Q. (2026). DGSeg: Dual guidance with textual priors and structural awareness for open-vocabulary remote sensing segmentation. Computers & Geosciences, 106182.

3.Wang, X., Huang, Y., Zhu, H., Mao, D., Zhu, X., & Liu, Q. (2026). Hgtsynergy: a transfer learning method for predicting anticancer synergistic drug combinations based on a drug-drug interaction heterogeneous graph. BMC bioinformatics.

4.Zhu, H., Wang, Z., Xu, M., Jiang, J., Zhang, J., Fan, H., ... & Du, B. (2025). STGAN-CR: A Semantics-Aware Cloud Removal Network Integrating Swin Transformer and GANs for Remote Sensing Applications. International Journal of Software Engineering and Knowledge Engineering, 35(11), 1669-1684.

5.Zhu, H., Wang, Z., Han, L., Xu, M., Li, W., Liu, Q., ... & Du, B. (2025). TSMCF: Transformer-based SAR and multispectral cross-attention fusion for cloud removal. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 6710-6720.

6.Zhu, H., Zhang, J., Wang, Z., Liu, X., Liu, Q., & Du, B. (2025). Dual-Branch Seasonal Error Elimination Change Detection Framework Using Target Image Feature Fusion Generator. Remote Sensing, 17(3), 523.

7.Wang, X., Zhu, H., Liu, Q., & Liu, Q. (2024). Fusing Micro-and Macro-Scale Information to Predict Anticancer Synergistic Drug Combinations. IEEE Journal of Biomedical and Health Informatics, 29(3), 2297-2309.

8.Liang, S., Yan, Z., Xie, C., Zhu, H., & Wang, J. (2024). Scribble-based complementary graph reasoning network for weakly supervised salient object detection. Computer Vision and Image Understanding, 243, 103977.

9.Lin, Y., Liu, S., Zheng, Y., Tong, X., Xie, H., Zhu, H., ... & Zhang, J. (2024). An unsupervised transformer-based multivariate alteration detection approach for change detection in VHR remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 3251-3261.

10.Chen, D., Wang, X., Zhu, H., Jiang, Y., Li, Y., Liu, Q., & Liu, Q. (2023). Predicting anticancer synergistic drug combinations based on multi-task learning. BMC bioinformatics, 24(1), 448.

11.Yao, K., Wang, X., Li, W., Zhu, H., Jiang, Y., Li, Y., ... & Liu, Q. (2023). Semi-supervised heterogeneous graph contrastive learning for drug–target interaction prediction. Computers in Biology and Medicine, 163, 107199.

12.Wang, X., Zhu, H., Chen, D., Yu, Y., Liu, Q., & Liu, Q. (2023). A complete graph-based approach with multi-task learning for predicting synergistic drug combinations. Bioinformatics, 39(6), btad351.

13.Luo, M., Du, B., Zhang, W., Song, T., Li, K., Zhu, H., ... & Wen, H. (2023). Fleet rebalancing for expanding shared e-mobility systems: A multi-agent deep reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3868-3881.

14.Wang, X., Zhu, H., Jiang, Y., Li, Y., Tang, C., Chen, X., ... & Liu, Q. (2022). PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network. Briefings in bioinformatics, 23(2), bbab587.

15.Zhu, H., Tan, R., Han, L., Fan, H., Wang, Z., Du, B., ... & Liu, Q. (2022). DSSM: a deep neural network with spectrum separable module for multi-spectral remote sensing image segmentation. Remote Sensing, 14(4), 818.

会议Conference Papers

1.Zhu, H., Zhang, J., Liu, Q., Dai, Q., & Du, B. (2026, February). Divide-and-Conquer Scanning Mechanism for Remote Sensing Image Change. In Pattern Recognition and Computer Vision: 8th Chinese Conference, PRCV 2025, Shanghai, China, October 15-18, 2025, Proceedings, Part II (p. 475). Springer Nature.

2.Zhu, H., Zhang, J., Liu, Q., Dai, Q., & Du, B. (2025, October). ODCCMamba-Unet: A Mamba-Unet Based Model with Omnidirectional Divide-and-Conquer Scanning Mechanism for Remote Sensing Image Change Detection. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV) (pp. 475-488). Singapore: Springer Nature Singapore.

3.Liang, X., Zhu, H., Zhu, X., Mao, D., & Liu, Q. (2025, October). MGPSyn: Molecular Graph Pretraining Enhanced Synergistic Drug Combination Prediction. In 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 7523-7529). IEEE.

4.Liang, X., Wang, X., Zhu, H., & Liu, Q. (2024, December). MTDS: Meta-Path Context Enhanced Drug Combination Synergy Prediction. In International Conference on Neural Information Processing (pp. 372-387). Singapore: Springer Nature Singapore.

5.Vegas, S., Ferre, X., & Zhu, H. (2024, October). Evidence-Based Commit Message Generation with Deep Learning Techniques (EvidenCoM). In Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (pp. 613-615).

6.Phyu, S., Li, W., Liu, Q., & Zhu, H. (2024, July). A Deep Learning Approach for Document-level Chinese Financial Event Extraction. In Proceedings of the 2024 3rd International Symposium on Robotics, Artificial Intelligence and Information Engineering (pp. 88-95).

7.Li, W., Phyu, S., Liu, Q., Du, B., Zhang, J., & Zhu, H. (2024, May). RSTIE-KGC: A relation sensitive textual information enhanced knowledge graph completion model. In 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 2991-2998). IEEE.

8.Li, Z., Wang, X., Li, Y., Zhu, H., & Liu, Q. (2024, October). DSESL: A deep stacking ensemble model for synthetic lethality prediction. In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 4807-4814). IEEE.

9.Zhu, H., Wang, Z., Xu, M., Zhang, J., Liu, Q., & Du, B. (2024). STGAN-CR: A Swin Transformer-Enhanced GAN Framework for Effective Cloud Removal in Satellite Imagery. In SEKE (pp. 51-56).

10.Li, Y., Zhu, H., Wang, X., & Liu, Q. (2023, October). Hetbisyn: Predicting anticancer synergistic drug combinations featuring bi-perspective drug embedding with heterogeneous data. In International Symposium on Bioinformatics Research and Applications (pp. 464-475). Singapore: Springer Nature Singapore.

11.Zhu, H., Wu, G., Wang, Z., Xu, M., Liu, Q., Liu, S., & Du, B. (2023, October). MTN: A Multi-Scale Transformer Network for Different Resolution Remote Sensing Images Change Detection. In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2550-2557). IEEE.

12.Rao, W., Yang, G., Zhao, Q., Liu, Y., Zhu, H., Li, M., ... & Zhang, Y. (2023, August). STIP: A Seasonal Trend Integrated Predictor for Blood Glucose Level in Time Series. In International Conference on Advanced Data Mining and Applications (pp. 437-450). Cham: Springer Nature Switzerland.