• Large language models and agentic AI for biomedical sciences
• Biomedical innovation and entrepreneurship

Dr. Danqing Yin obtained her PhD in Bioinformatics at the School of Biomedical Sciences in The University of Hong Kong, focusing on the development of scalable and AI-powered methods for omics analytics. Before HKU, she completed her Master of Information Technology at The University of Sydney, and her Bachelor of Biomedicine from University of Melbourne.

During the PhD candidature, she visited The University of Cambridge, Harvard Medical School and Qiming Venture Capitals, she pitched her internationally collaborated research, which were successfully shortlisted into the HKU DeepTech 100 ideation programme and ultimately the HKSTP IncuBio programme, as featured in the HKU Annual Report 2024.

Crossing from industry and venture back to academia, she continued the journey at her alma mater to educate the next generation, developing courses on AI for medical education while leading faculty-wide pedagogical initiatives in technology-empowered learning and teaching.

• Doctor of Philosophy (PhD) in Bioinformatics, The University of Hong Kong
• Visiting scholar, Harvard University and Broad Institute of MIT and Harvard
• Visiting scholar, The University of Cambridge
• Master of Information Technology, The University of Sydney
• Bachelor of Biomedicine, The University of Melbourne

• The 9th“Internet+”Student Innovation and Entrepreneurship Competition (2024), Gold Prize
• The 9th Hong Kong University Student Innovation and Entrepreneurship Competition (2023), Third Prize

1. Danqing Yin, Yue Cao, Junyi Chen, Candice L Y Mak, Ken H O Yu, Jiaxuan Zhang, Jia Li, Yingxin Lin, Joshua W K Ho, Jean Y H Yang, Scope+: an open source generalizable architecture for single-cell RNA-seq atlases at sample and cell levels, Bioinformatics, Volume 41, Issue 1, January 2025, btae727, https://doi.org/10.1093/bioinformatics/btae727
2. Junyi Chen, Danqing Yin, Harris Y H Wong, Xin Duan, Ken H O Yu, Joshua W K Ho, Vulture: cloud-enabled scalable mining of microbial reads in public scRNA-seq data, GigaScience, Volume 13, 2024, giad117, https://doi.org/10.1093/gigascience/giad117
3. Julie Warin, Nicolas Vedrenne, Vivian Tam, Mengxia Zhu, Danqing Yin, Xinyi Lin, et al., In Vitro and in vivo models define a molecular signature reference for human embryonic notochordal cells. iScience. 2024;27(2):109018. Published 2024 Jan 26. doi:10.1016/j.isci.2024.109018