Chapin Korosec

Research Areas

• Mathematical immunology and immune dynamics
• Within-host viral modeling and host–pathogen interactions
• Stochastic modeling of viral evolution and mutation processes
• Machine learning for longitudinal immune profiling
• Mathematical epidemiology and population disease dynamic

Research Themes

General Description

My research program develops quantitative and computational frameworks to understand how immune responses and infectious diseases evolve across biological and population scales. It integrates mechanistic mathematical modeling, stochastic processes, and machine learning to uncover the structure of immune responses to infection and vaccination, and to translate these insights into predictive tools for epidemiology and public health. This work bridges within-host dynamics and population-level disease spread, with a particular focus on heterogeneity, evolution, and data-driven inference.

Themes/Pillars

1. Immune-Coupled Viral Dynamics

   Development of mechanistic within-host models that explicitly couple viral replication with innate and adaptive immune responses. These models incorporate cytokine signaling, T cell dynamics, and antibody responses to identify key drivers of infection outcomes and immunogenicity. A central focus is ensuring structural identifiability and biological interpretability of model parameters.

2. Stochastic Viral Evolution and Bottlenecks

   Construction of probabilistic frameworks to study how viral mutations emerge, survive, and transmit under immune pressure. This work leverages branching processes and probability generating functions to model transmission bottlenecks and multi-step mutation chains, with applications to SARS-CoV-2, influenza, and zoonotic systems.

3. Vaccine-Induced Immunity and Heterogeneity

   Development of mathematical models to characterize inter-individual variability in vaccine responses. This includes modeling immune priming, memory formation, and waning dynamics, as well as identifying latent immune processes through model-based inference from clinical data.

4. Machine Learning for Immunological Systems

   Application of machine learning to high-dimensional longitudinal immune datasets to uncover non-linear immunological signatures. Recent work demonstrates how random forests and synthetic “virtual patients” can reveal interpretable immune features and enable robust classification of vaccine responses across populations.

5. Mathematical Epidemiology and Multi-Scale Integration

   Extension of within-host and immunological models to population-level disease dynamics. This includes linking immune heterogeneity and viral evolution to epidemic spread, informing vaccination strategies, and developing predictive frameworks for emerging infectious diseases.

Education and Employment Background

• Adjunct Professor, Mathematics & Statistics, University of Guelph (2025–Present) Teaching upper-year courses in operations research and mathematical modelling; developing an independent research program in mathematical biology.
• Postdoctoral Researcher, Mathematics & Statistics, York University (2021–2025) Research in mathematical immunology, within-host viral dynamics, and machine learning for vaccine response modeling. NSERC Postdoctoral Fellow and AI for Public Health Fellow.
• Program Manager, ModERN (CIRN), York University (2023–2025) Contributed to epidemiological modeling, public health analytics, and interdisciplinary research coordination.
• Ph.D. in Physics, Simon Fraser University (2015–2021) Thesis on mathematical modeling and engineering of molecular motors.
• B.Sc. (Hons) in Physics, McMaster University (2011–2015)

Highlights

Major Awards

• Michelson Postdoctoral Prized Lectureship (2024)
  • First Canadian and BioPhysicist and Applied Mathematician to win this award in its 28 year history.
• NSERC Postdoctoral Fellowship
• NSERC PGS-D Graduate Scholarship
• Steel Memorial Award (SFU)

Cover Articles

• Cell Press Patterns (2026) – Machine learning of vaccine immunogenicity
• Nature Communications (2024) – Protein-based artificial molecular motor
• Soft Matter (2020) – Molecular motor dynamics
• Physics in Canada (2017) – Molecular motor engineering

Research Media Attention

• Coverage in CTV News, Science Daily, CBC, Discover Magazine, Physics Magazine, The Scientist, and institutional media outlets for work on COVID-19 immunity and molecular motors