Yue Li

Assistant Professor, School of Computer Science, McGill University

My research is focused on developing interpretable probabilistic learning models and deep learning models to model genetic, epigenetic, electronic health record, and single-cell genomic data. By systematically integrating multimodal and longitudinal data, I aim to have impactful applications in computational medicine including building intelligent clinical recommender systems, forecasting patient health trajectories, personalized polygenic risk predictions, characterizing multi-trait functional genetic mutations, and dissecting cell-type-specific regulatory elements that are underpin complex traits and diseases in human. My research program covers three main research areas involving applied machine learning in (1) healthcare and public health, (2) computational genomics, and (3) population genetics.

We are actively recruiting interested students*, postdoctoral fellows, research assistants, and visiting scholars in machine learning and computational biology! Please see the detailed description here and feel free to email me to work on some exciting projects together!


Assistant Professor at the School of Computer Science, McGill University

Associate Member at Quantitative Life Science Program, McGill University

Associate Member at Montreal Montreal Institute for Learning Algorithms (MILA)


Email : yueli with the suffix (@cs.mcgill.ca)

Office: Trottier TR3105, 3630 Rue University, Montreal, QC H3A 0C6

Phone: (514) 398-7079

Short biography

From 2015 to 2018, I was a postdoctoral associate from Prof. Manolis Kellis research group at Computer Science and Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology. I obtained a PhD degree in Computer Science and Computational Biology at University of Toronto in 2014. My PhD advisor was Prof. Zhaolei Zhang. I received Bachelor of Science Honors from the University of Saskatchewan in Computer Science Bioinformatics and Statistics in 2010. My honor thesis advisor was Prof. Anthony Kusalik.

Research interests

  • Machine learning approaches:
    • Latent variable/topic models
    • Matrix/tensor decomposition
    • Collaborative filtering
    • Deep generative models
    • Approximate Bayesian inference
  • Biological problems of interest:
    • Electronic Health Record (EHR)
    • Polygenic risk score
    • Inference of functional causal mutations
    • Expression quantitative traits loci (eQTL)
    • Tissue/cell-type deconvolution
    • Post-transcriptional regulation by microRNAs in cancers
    • RNA epigenetics (N6-methyladenosine sites)
    • Functional characterization of long noncoding RNAs
  • Bioinformatics analyses:
    • Whole-genome sequencing analysis
    • Single-cell expression profiles modeling
    • Magnetic resonance imaging (MRI) analysis