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Background

I completed my PhD at Simon Fraser University where I simulated, designed and implemented artificial molecular motors in the lab. I completed 3 years as a postdoc at York University working on mathematical and machine learning models to understand within-host dynamics of infectious diseases, particularly SARS-CoV-2. I am current an Adjunct Professor in the Department of Mathematics and Statistics and University of Guelph.

My research program is grounded in the mathematical and statistical modeling of biological systems, with a focus on understanding how immune responses to vaccines and viral infections evolve across biological scales. I work at the interface of mathematical immunology, dynamical systems, stochastic modeling, and evolutionary epidemiology, drawing on tools from statistics, numerical analysis, data science, and optimization. I integrate data-driven methods with differential equations (ODEs and PDEs) to explore immune priming, waning, and inter-individual variability in immune response. By coupling within-host viral dynamics to immune feedback and stochastic mutation processes, I aim to uncover generalizable principles that govern immune regulation and pathogen evolution. This work contributes to predictive vaccinology and provides foundational mathematical theory to guide public health strategies. I collaborate with public health researchers, clinicians, and immunologists.

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Banner caption: Artist representation of our spherical rolling motors. Art credit: Hassan A. Tahini from sciencebrush.design