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Simon A. Babayan

Reader (Host-Parasite Interactions & Pathogenesis)
School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow

For curriculum vitae and additional contact information, see the CV & Contact page.
đź“‹ Official University of Glasgow staff page

Research

My research seeks to understand the processes that generate individual-level heterogeneity in immune responses and parasite strategies, and how these processes shape population-level disease dynamics. I characterise biological processes—from molecular pathways to ecosystem-level transmission—and develop computational methods that capture these processes to predict and control emerging infectious diseases.

My core research falls into three biological themes, unified by a transversal cross-cutting theme of computational methods applied through a causal lens. This approach characterises causal mechanisms and applies the three layers of Pearl’s causal hierarchy: association (observing patterns), intervention (designing and evaluating interventions), and prediction (forecasting outcomes and counterfactuals).

  1. Wild immunology and host–parasite interactions — Integrating field data, immunology, and quantitative models to understand immune homeostasis in natural settings
  2. Vector-borne diseases — Analysing ecological and evolutionary processes in vectors and reservoir hosts, with deep learning models for surveillance and identification
  3. Vaccination & treatment — Determining when and where interventions most effectively improve health and reduce transmission, using machine learning to identify targets and predict efficacy
  4. Computational methods and causal inference — Developing computational methods that capture biological processes to predict and control disease dynamics, including causal inference, transmission ecology, prediction and surveillance tools, and predictive ecology

For detailed information on publications, grants, and research themes, see the Research page.

Teaching

As Programme Director for the MSc Data Science for Ecology & Epidemiology, I lead courses on programming, machine learning, and causal inference, totalling 90 credits of postgraduate teaching. I have supervised 16 PhD students with 100% completion within 4 years.

My teaching emphasises hands-on experience with real data, reproducible research practices, and the integration of computational methods with biological questions.

For detailed information on courses, supervision, and teaching philosophy, see the Teaching page.

Publications

For an up-to-date list of publications, please see my Google Scholar profile or the Research page for publications organised by theme.

Software & Data

I develop and maintain open-source software tools for computational biology, disease surveillance, and data analysis. These tools support reproducible research and enable others to apply advanced computational methods to biological questions.

For tools, repositories, datasets, and open science resources, see the Software & Data page.

Last updated: 17 March 2026