Prof. Fan Yang is a distinguished researcher recognized for his influential contributions across interdisciplinary scientific fields, with 2,301 citations from 1,944 documents, 67 publications, and an impressive h-index of 28, reflecting his strong global research impact. He holds advanced degrees in biomedical engineering and life sciences, where his academic foundation laid the groundwork for his extensive expertise in biomaterials, tissue engineering, regenerative medicine, and nanotechnology-driven drug delivery systems. Professionally, he has held key academic and research positions in leading universities and institutes, where he has led projects focusing on developing biofunctional scaffolds and stem cell-based therapies for tissue repair and disease treatment. His research interests center on integrating material science with cellular biology to design innovative therapeutic strategies for regenerative health. Prof. Yang possesses exceptional research skills in molecular biology techniques, cell culture engineering, polymer synthesis, and translational biomedical applications. Over the years, he has received multiple national and international honors recognizing his pioneering work in biomedical innovation and translational science. In conclusion, Prof. Fan Yang’s scholarly achievements, impactful research metrics, and dedication to advancing biomedical technologies position him as a prominent global figure in the fields of regenerative medicine and biomedical engineering.
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Featured Publications
Liang, J., Hu, D., & Feng, J. (2020). Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation. In Proceedings of the International Conference on Machine Learning (pp. 6028–6039).
Luo, M., Chen, F., Hu, D., Zhang, Y., Liang, J., & Feng, J. (2021). No fear of heterogeneity: Classifier calibration for federated learning with non-IID data. In Proceedings of the Annual Conference on Neural Information Processing Systems (pp. 5972–5984).
Liang, J., Hu, D., Wang, Y., He, R., & Feng, J. (2022). Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 8602–8617.
He, L., Liang, J., Li, H., & Sun, Z. (2018). Deep spatial feature reconstruction for partial person re-identification: Alignment-free approach. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 391–400).
Liang, J., He, R., & Tan, T. (2024). A comprehensive survey on test-time adaptation under distribution shifts. International Journal of Computer Vision, 132(4), 366–389.