Welcome to Li Lab at McGill Computer Science

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 leveraging machine learning to make a translational impact in healthcare.

Recent Research Highlights

Multimodal EHR integration

Inferring multimodal latent topics from electronic health records. Nature Communications 2020

Time-series health forecasting

TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare. ACM-BCB 2024

Polygenic risk score inference

Fast and Scalable Polygenic Risk Modeling with Variational Inference. American Journal of Human Genetics 2023

Inferring causal genetic variants

SparsePro: an efficient genome-wide fine-mapping method integrating summary statistics and functional annotations. PLOS Genetics 2023

Cell-type deconvolution

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

Single-cell foundation model

Cell ontology guided transcriptome foundation model. NeurIPS 2024 spotlight.

Single-cell embedded topic model

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data. Nature Communications 2021

Genome foundation model for scATAC-seq analysis

GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling. RECOMB 2024

Latest News

• 2025 Winter: Welcoming new postdoc researcher Dr. Jun Bai to the team!
• 2024 Fall: Welcoming new Master's students Bo-Hong Wang, Claris Gu, Neda Esfehani, and Ruilin Wang to the team!