• Bioinformatics
• Machine learning
• Single-cell genomics
• Spatial transcriptomics
• Biomedical artificial intelligence

Dr Huang is an associate professor in the School of Biomedical Sciences and the Department of Statistics and Actuarial Science at the University of Hong Kong (HKU). Before joining HKU, he was an EBPOD research fellow at the University of Cambridge and the European Bioinformatics Institute (EMBL-EBI). Dr Huang completed his BEng in Automation from Tsinghua University (2013) and PhD in Informatics (Machine learning and computational biology) from the University of Edinburgh (2017). His research lies at the interface between machine learning, cellular genomics, and biomedicine by developing statistical & intelligence methods to decode complex cellular systems in development and diseases.

• Department of Statistics and Actuarial Science, University of Hong Kong
• School of Computing and Data Science, University of Hong Kong

We are a 100% dry lab, developing computational methods to achieve accurate quantifications from noisy and/or sparse sequencing data and discover biological patterns from high dimensional omics data. 

We have been working on single-cell data science since the establishment of the lab, focusing on developing statistical machine learning methods to dissect the temporal transitions and spatial interaction among single cells, including RNA velocity methods to reveal cell differentiation trajectory, somatic mutation detection to identify tumor clonal structure, and ligand-receptor interaction to study cell-cell communications. Currently, we further focus “AI for cellular biomedicine” including but not limited to the following topics:

1. Multimodal AI decoding tumor-immune interaction by fusing spatial omics & histological images;
2. Modelling cancer evolution from multi-type mutations using single-cell and spatial omics;
3. Interpretable AI dissecting complex regulation and genetic effects in human development and diseases with single-cell omics;
4. Modelling RNA processing and its intrinsic dynamics, e.g., with RNA velocity, to decipher cell differentiation trajectory and its regulation.

We welcome students to join us to work on existing research directions or related new topics. Please email Dr. Huang (yuanhua@hku.hk) directly for project details and opportunities.

1. Liu Y., Cai F., Barile M., Chang Y.#, Cao D.#, and Huang Y.# “TemporalVAE: atlas-assisted temporal mapping of time-series single-cell transcriptomes during embryogenesis” Nature Cell Biology 2025.
2. Chung H.M. and Huang Y.# “SNPmanifold: Detecting single-cell single-nucleotide-variant clonality and lineages using binomial variational autoencoder” Genome Biology 2025, 26, 309.
3. Gao M.†, Barile B.†, Chabra S., Haltalli M., Calderbank E. F., Chao Y., Zheng W., Wilson N. K., Laurenti E.#, Gottgens B.#, and Huang Y.# “CLADES: a hybrid NeuralODE-Gillespie approach for unveiling clonal cell fate and differentiation dynamics.” Nature Communications 2025, 16 (1), 8174.
4. Hou R. and Huang Y.# “scTail: precise polyadenylation site detection and its alternative usage analysis from reads 1 preserved 3' scRNA-seq data” Genome Biology 2025, 26, 236.
5. Huang R. †, Huang X. †, Tong Y., Yan Y.N., Leung S.Y., Stegle O. and Huang Y.# “Robust analysis of allele-specific copy number alterations from scRNA-seq data with XClone.” Nature Communications, 2024, 15 (1), 6684.
6. Cheng C.†, Wang G.†, Zhu Y., Zhang L., Wu H., Liu Z#, Huang Y.#, Zhang J.# “Multiplexed bulk and single-cell RNA-seq hybrid enables cost-efficient disease modeling with chimeric organoids.” Nature Communications, 2024, 15: 3946.
7. Li S.†, Ma J.†, Zhao T., Jia Y., Liu B., Luo R.#, and Huang Y.# “CellContrast: Reconstructing Spatial Relationships in Single-Cell RNA Sequencing Data via Deep Contrastive Learning.” Patterns, 2024, 5: 101022.
8. Qiao C. and Huang Y.# “Reliable imputation of spatial transcriptomes with uncertainty estimation and spatial regularization.” Patterns, 2024, 5: 101021.
9. Li Z., Wang T., Liu P.# and Huang Y.# “SpatialDM for rapid identification of spatially co-expressed ligand–receptor and revealing cell–cell communication patterns.” Nature Communications, 2023, 14: 3995.
10. Hou R., Hon C.C., and Huang Y. # “CamoTSS: analysis of alternative transcription start sites for cellular phenotypes and regulatory patterns from 5’ scRNA-seq data.” Nature Communications, 2023, 14:7240.
11. Gao M., Qiao C., and Huang Y.# “UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference.” Nature Communications, 2022, 13, 6586.
12. Kwok, A. W. C., Qiao, C., Huang, R., Sham, M. H., Ho, J. W.#, & Huang, Y.#  “MQuad enables clonal substructure discovery using single cell mitochondrial variants.” Nature communications, 2022, 13(1): 1-10.
13. Qiao, C., & Huang, Y.# “Representation learning of RNA velocity reveals robust cell transitions.” Proceedings of the National Academy of Sciences, 2021, 118(49).
14. Huang, X., & Huang, Y.# “Cellsnp-lite: an efficient tool for genotyping single cells.” Bioinformatics, 2021, 37(23): 4569-4571.
15. Huang, Y.#, & Sanguinetti, G.# “BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments.” Genome biology, 2021, 22(1): 1-15.

• 2022, NSFC Excellent Young Scientists Fund (Hong Kong & Macau)
• 2018, Chinese Government Award for Outstanding Self-Financed Students Abroad
• 2017, EBPOD postdoctoral fellowship, University of Cambridge and EMBL-European Bioinformatics Institute
• 2017, Best poster award, High Throughput Sequencing algorithms (HiTSeq) workshop, ISMB/ECCB Conference

• Dr Rongting Huang received a CRI Postdoctoral Fellowship to support her research at Stanford University