Our Mission


At the intersection of AI, statistics, and biology, our lab is dedicated to developing novel computational frameworks for unraveling the complexities of biomedical data, paving the way for groundbreaking discoveries in computational biology and biomedical informatics.

Read more about our research vision and ongoing directions on the Research page.

Selected Publications

Full publications can be found at Publications tab or Google Scholar. *
All AI Statistics Computational Biology
MultiFlow: coupled flow matching for predicting single-cell multiomic perturbation responses in unseen cellular contexts
Haochen Wang, Charming Zhang, Mengran Zhang, Xiaoming Nie, Qiao Liu*. bioRxiv, 2026.
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MultiFlow: coupled flow matching for predicting single-cell multiomic perturbation responses in unseen cellular contexts
RegFM: an interpretable context-aware foundation model for human transcriptional regulation
Zijing Gao, Yining Sun, Haochen Wang, Rui Jiang*, Qiao Liu*. bioRxiv, 2026.
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RegFM: an interpretable context-aware foundation model for human transcriptional regulation
scMTG reconstructs single-cell temporal dynamics with Markov transition generators
Xuejian Cui, Haochen Wang, Zijing Gao, Rui Jiang*, Wing Hung Wong*, Qiao Liu*. bioRxiv, 2026.
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scMTG reconstructs single-cell temporal dynamics with Markov transition generators
Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling
Qiao Liu. arXiv, 2026.
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Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling
A Multi-modal Diffusion Model with Dual-Cross-Attention for Multi-Omics Data Generation and Translation
Erpai Luo, Lei Wei, Minsheng Hao, Xuegong Zhang†, Qiao Liu†. Nature Communication 2026.
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A Multi-modal Diffusion Model with Dual-Cross-Attention for Multi-Omics Data Generation and Translation
A Bayesian Generative Modeling Approach for Arbitrary Conditional Inference
Qiao Liu and Wing Hung Wong. arXiv, 2026.
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A Bayesian Generative Modeling Approach for Arbitrary Conditional Inference
An AI-powered Bayesian generative modeling approach for causal inference in observational studies
Qiao Liu and Wing Hung Wong. Journal of the American Statistical Association, 2026.
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An AI-powered Bayesian generative modeling approach for causal inference in observational studies
Leveraging genomic large language models to enhance causal genotype-brain-clinical pathways in Alzheimer’s disease
Qiao Liu*, Wanwen Zeng*, Hongtu Zhu, Lexin Li, Wing Hung Wong. medRxiv, 2024.
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Leveraging genomic large language models to enhance causal genotype-brain-clinical pathways in Alzheimer's disease
An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies
Qiao Liu, Zhongren Chen, Wing Hung Wong. PNAS, 2024.
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An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies
EpiGePT: a pretrained transformer-basedlanguage model for context-specific humanepigenomics
Zijing Gao*, Qiao Liu*,†, Wanwen Zeng, Rui Jiang†, Wing Hung Wong†. Genome Biology, 2024.
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EpiGePT: a pretrained transformer-basedlanguage model for context-specific humanepigenomics
Simultaneous deep generative modelling and clustering of single-cell genomic data
Qiao Liu, Shengquan Chen, Rui Jiang, Wing Hung Wong. Nature Machine Intelligence, 2021.
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Simultaneous deep generative modelling and clustering of single-cell genomic data
Density estimation using deep generative neural networks
Qiao Liu, Jiaze Xu, Rui Jiang, Wing Hung Wong. PNAS, 2021.
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Density estimation using deep generative neural networks

Support & Funding

We appreciate the support from the below organizations & agencies.

NIH Yale Yale School of Public Health