Li, Yuè (李岳)

Canada Research Chair (Tier 2) in Machine learning for Genomics and Healthcare

Assistant Professor, School of Computer Science, McGill University

Associate member at Mila - Quebec AI Institute

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. Emailing me if you are interested. To receive fast reply, please describe the following in your email: (1) Which of our papers (published after 2015 with me being either the first or last (co-)correspnoding author) that interest you the most? (2) Write a 1-2 page review on a paper; (3) Propose a project extended from that paper.


Assistant Professor at the School of Computer Science, McGill University

Associate Member at Quantitative Life Science Program, McGill University

Associate Member at Mila - Quebec AI Institute


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

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

Phone: (514) 398-7079

Short biography

I obtained a PhD degree in Computer Science and Computational Biology at University of Toronto in 2014. Prior to joining McGill, I was a postdoctoral associate at Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT (2015-2018).

Research interests

  • Machine learning methods:
    • Deep learning
    • Latent topic models
    • Bayesian inference
  • Biological problems of interest:
    • Single-cell genomics
    • Electronic Health Record (EHR)
    • Polygenic risk score
    • Inference of functional causal mutations