Yue Li

Assistant Professor, School of Computer Science, McGill University


Our research group focuses on developing machine learning approaches to decipher, in a human-understandable manner, the etiology of diverse phenotypes including complex human diseases. We seek to answer some of the key questions in computational biology: what are the genetic predispositions, cell-type specificities, genomic regulatory elements, gene and pathway functions, and their interactions with environments that together give rise to the phenotypic diversity and their interdependency.

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!

*MSc/PhD applicants must apply for the admission from the graduate program first, please do not directly email me your MSc/PhD application.

Contact



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

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

Phone : TBD

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 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)
    • 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