Machine Learning 🚀 In Genomics 🧬 and HealTh ❤️ = 💡
Our research focuses on developing AI methods for computational biology, particularly in the areas of population genetics, single-cell multi-omics, and electronic health records (EHR). We are passionate about building agentic AI systems to make a translational impact in healthcare.
DoctorAgents for small temporal clinical data. arXiv 2026
ChatHealthAI aligns EHR representations with LLMs for grounded clinical reasoning arXiv 2026
FedWeight: mitigating covariate shift of federated learning on EHR data through patients re-weighting. npj Digital Medicine 2025
TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare. Health Information Science and Systems 2025
TrajGPT: Irregular Time-Series Representation Learning of Health Trajectory. IEEE Journal of Biomedical and Health Informatics 2025
Inferring multimodal latent topics from electronic health records. Nature Communications 2020
scE2TM improves single-cell embedding interpretability and reveals cellular perturbation signatures. Nature Communications 2026
Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data. Nature Communications 2021
GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling. Cell Systems 2026
Guided-topic modelling of single-cell transcriptomes enables joint cell-type-specific and disease-subtype deconvolution of bulk transcriptomes with a focus on cancer studies. Genome Biology 2023
Fast and Scalable Polygenic Risk Modeling with Variational Inference. American Journal of Human Genetics 2023
Toward whole-genome inference of polygenic scores with fast and memory-efficient algorithms American Journal of Human Genetics 2025
Sparse Polygenic Risk Score Inference with the Spike-and-Slab LASSO. Bioinformatics 2025
SparsePro: an efficient genome-wide fine-mapping method integrating summary statistics and functional annotations. PLOS Genetics 2023