Summary
Elijah Willie is an applied statistician and bioinformatician with a decade of experience building statistical and computational methods for high-dimensional biological data. Currently an Applied Statistician at the University of Sydney and completing a PhD in Mathematics and Statistics, he specializes in spatial and suspension cytometry analysis, machine learning pipelines, and data integration frameworks. His background spans academia and industry, including roles at Roche, BC Cancer, and Fusion Genomics, where he translated complex algorithms into production-ready bioinformatics pipelines. Elijah is proficient in Python, R, and high-performance computing, and has a track record of creating teaching and auto-grading tools that scale data science education. Notably, his early work applied integer linear programming to deconvolute pathogen strain diversity—an approach that connected theoretical complexity results to a real public-health problem. He combines rigorous mathematical training with practical software engineering to deliver reproducible, impact-driven computational biology solutions.
10 years of coding experience
10 years of employment as a software developer
The University of Sydney
Master of Science - MS, Biomathematics, Bioinformatics, Computational Biology and Statistics, Master of Science - MS, Biomathematics, Bioinformatics, Computational Biology and Statistics at The University of British Columbia
Bachelor of Applied Science (B.A.Sc.), Biomathematics, Bioinformatics, Computational Biology, and Biostatisitcs , Bachelor of Applied Science (B.A.Sc.), Biomathematics, Bioinformatics, Computational Biology, and Biostatisitcs at Simon Fraser University