About
Computational Biologist & Bioinformatician — Mitacs Accelerate Postdoctoral Fellow & Research Manitoba Postdoctoral Fellow, Department of Animal Science, University of Manitoba
I am a computational biologist who builds the pipelines and models that turn sequencing and screening data into answers about microbes, antibiotics and resistance.
As a Mitacs Accelerate Postdoctoral Fellow (with Topigs Norsvin) and Research Manitoba Postdoctoral Fellow in the Translational Livestock Microbiome Research Lab (Department of Animal Science, University of Manitoba), I lead the bioinformatics side of projects on the livestock microbiome, feed efficiency, methane and antimicrobial resistance. That means designing metagenomic workflows for hybrid Illumina and Nanopore data, recovering metagenome-assembled genomes at scale on HPC clusters, quantifying resistomes and mobilomes, and applying machine learning to genome-resolved isolate collections to find signatures of health and disease.
Two collaborations anchor this work. Through a Mitacs Accelerate fellowship with the pig-breeding company Topigs Norsvin, I am building interpretable machine-learning models that predict feed efficiency from the functional gene content of the swine gut microbiome. Through an NSERC Alliance project with Prof. Le Luo Guan at the University of British Columbia, I apply machine learning and genome-scale metabolic models (GEMs), built for individual rumen methanogens and for the rumen community as a whole, to find ways to reduce enteric methane emissions from cattle. Details are on the Research page.
I completed my PhD in Microbiology at the University of Manitoba with Dr. Silvia Cardona, where I combined CRISPR interference, pooled-library sequencing and machine learning to study essential genes and antibiotic action in Burkholderia cenocepacia. That work produced a broad-host-range CRISPRi toolkit, a graph neural network that raised the hit rate of an antibacterial screen, and a CRISPRi-seq chemical-genomics platform that uncovered a new inhibitor of peptidyl-tRNA hydrolase. The dissertation received the University of Manitoba Distinguished Dissertation Award nomination from the Faculty of Science, and the antimicrobial compounds identified by the machine-learning model are the subject of a pending patent.
I release the code behind my research as reusable tools and interactive apps so that collaborators can reproduce and extend the analyses. You can find them on the Projects page and on GitHub.