2025/11 – 至今, 上海理工大学, 健康科学与工程学院, 讲师

2021/09 – 2025/07, 香港科技大学，生物工程, 博士

2018/09 – 2021/06, 中国科学院大学计算技术研究所，计算机系统结构，硕士

2014/09 – 2018/06, 东北林业大学，信息管理与信息系统, 本科

1.AI与健康科学融合：致力于利用人工智能技术驱动面向特殊人群的个性化健康食品设计与研发。

2.生成式视觉智能：探索生成式模型的前沿算法，实现高质量的图像生成与跨域风格转换。

3.食品工业中的智能视觉感知技术：研究与开发基于计算机视觉的食品品质无损检测系统，以理解并解析复杂视觉信息。

[1] 一种病理切片虚拟免疫组化染色方法及系统，发明专利，中国，CN113256617B，2024年2月20日

[1] Shi, L., Zhang, Y., Wong, I. H., Lo, C. T., & Wong, T. T. (2023, October). Mulhist: Multiple histological staining for thick biological samples via unsupervised image-to-image translation. Medical Image Computing and Computer-Assisted Intervention (pp. 735-744). Cham: Springer Nature Switzerland.

[2] Shi, L., Hou, X., Wong, I. H., Chan, S. C., Chen, Z., Lo, C. T., & Wong, T. T. (2024, December). Thickv-stain: Unprocessed thick tissues virtual staining for rapid intraoperative histology. Medical Imaging with Deep Learning.

[3] Shi, L., Wong, I. H., Lo, C. T., Tsui, L. W., & Wong, T. T. (2023, April). Unsupervised multiple virtual histological staining from label-free autofluorescence images. IEEE International Symposium on Biomedical Imaging (pp. 1-5). IEEE.

[4] Oh, J.*, Shi, L.*, & Wong, T. T. (2025, September). Pathology-Aware Virtual H&E Staining of Section-Free Thick Tissues with Semantic Contrastive Guidance. Medical Image Computing and Computer-Assisted Intervention (pp. 421-430). Cham: Springer Nature Switzerland. (*co-first author)

[5] Shi, L., Wong, I. H., Lo, C. T., & Wong, T. T. (2022, September). One-side virtual histological staining model for complex human samples. In 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) (pp. 1-4). IEEE.

[6] Chan, S. C., Shi, L., Huang, B., & Wong, T. T. (2025, September). Directional Adaptive Shuffle-Based Visual State-Space Models for Medical Image Restoration. Medical Image Computing and Computer-Assisted Intervention (pp. 160-170). Cham: Springer Nature Switzerland.

[7] Chan, S. C., Shi, L., Huang, B., & Wong, T. T. (2024, May). Local Spatial Attention Transformer for Sparse Photoacoustic Image Reconstruction. IEEE International Symposium on Biomedical Imaging (pp. 1-5). IEEE.

[8] Wong, I. H., Chen, Z., Shi, L., Lo, C. T., Kang, L., Dai, W., & Wong, T. T. (2024). Deep learning-assisted low-cost autofluorescence microscopy for rapid slide-free imaging with virtual histological staining. Biomedical Optics Express, 15(4), 2187-2201.

[9] Hou, X., Liu, B., Zhang, S., Shi, L., Jiang, Z., & You, H. (2022, October). Dynamic weighted semantic correspondence for few-shot image generative adaptation. In Proceedings of the 30th ACM International Conference on Multimedia (pp. 1214-1222).