YangLab

Introduction

YangLab, led by Dr. Guang Yang at Imperial College London, focuses on developing AI-driven techniques for imaging and biomedical data analysis with a strong emphasis on translational research. The lab’s work spans key areas such as AI-based data quality transfer, data harmonization, fast imaging, federated learning, and generative AI. These innovations are applied to tackle pre-clinical and clinical challenges in ageing, cardiovascular disease, lung disease, and oncology, with a particular focus on drug discovery and clinical decision-making. YangLab is actively collaborating with academic and industrial partners to bridge the gap between cutting-edge AI technology and real-world healthcare solutions.

Value

YangLab operates as a collaborative community that prioritizes professional growth, personal development, inclusivity, and respect. The lab fosters an environment where all members are encouraged to take ownership of their research while maintaining a strong commitment to equality, diversity, and inclusion. Mental health and work-life balance are integral to the lab’s culture, with flexibility in working hours and support for mental well-being. The lab encourages open communication, constructive feedback, and mutual mentorship to promote learning and growth. Dr. Guang Yang emphasizes the importance of independent, self-motivated research while providing mentorship and career development support, including guidance on publications, conferences, and personal goals. The lab upholds a culture of kindness, integrity, and transparency, with a focus on collaborative work and a commitment to celebrating achievements together.

Selected Publications

• Huang, J., Yang, L., Wang, F., Wu, Y., Nan, Y., Wu, W., Wang, C., Shi, K., Aviles-Rivero, A.I., Schönlieb, C.B., Zhang D. and Yang, G., 2025. Enhancing global sensitivity and uncertainty quantification in medical image reconstruction with Monte Carlo arbitrary-masked MAMBA. Medical Image Analysis, 99, p.103334.
• Huang, J., Wu, Y., Wang, F., Fang, Y., Nan, Y., Alkan, C., Abraham, D., Liao, C., Xu, L., Gao, Z., Wu, W., Zhu, L., Chen, Z., Lally, P., Bangerter, N., Setsompop, K., Guo, Y., Rueckert, D., Wang, G. and Yang, G., 2024. Data-and Physics-driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies. IEEE Reviews in Biomedical Engineering.
• Xie, C., Zhuang, X.X., Niu, Z., Ai, R., Lautrup, S., Zheng, S., Jiang, Y., Han, R., Sen Gupta, T., Cao, S., Lagartos-Donate, M.J., Cai, C.-Z., Xie, L.-M., Caponio, D., Wang, W.-W., Schmauck-Medina, T., Zhang, J., Wang, H.-L., Lou, G., Xiao, X., Zheng, W., Palikaras, K., Yang, G., Caldwell, K.A., Caldwell, G.A., Shen, H.-M., Nilsen, H., Lu, J.-H. and Fang, E.F., 2022. Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow. Nature Biomedical Engineering, 6(1), pp.76-93.
• Yang, G., Ye, Q. and Xia, J., 2022. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Information Fusion, 77, pp.29-52.
• Yang, G., Yu, S., Dong, H., Slabaugh, G., Dragotti, P.L., Ye, X., Liu, F., Arridge, S., Keegan, J., Guo, Y. and Firmin, D., 2017. DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE Transactions on Medical Imaging, 37(6), pp.1310-1321.

FAQs

YangLab does not currently offer internship opportunities or provide supervision for external remote students. Due to a high volume of emails on this topic, emails requesting internships or external supervision will go unanswered. For PhD applications, positions are typically advertised through Imperial College London’s official channels, and applicants should have a strong academic background, ideally with experience in AI, machine learning, or biomedical imaging. The lab values inclusivity, respect, and collaboration, and PhD students are encouraged to engage in independent research while benefiting from mentorship and career development support. Visiting researcher positions may be considered on a case-by-case basis, depending on the alignment of research interests and availability. For more information, please refer to the official Imperial College recruitment website. Recent updates may be shown on Dr. Guang Yang's LinkedIn.