Rockefeller Foundation

Indiana University

I am building a transdisciplinary research program that leverages mathematical and computational modeling, machine learning, and data science to identify the eco-evolutionary, demographic, and environmental drivers of pathogen emergence, persistence, and spread. At IU, my lab focuses on the ecology of infectious diseases in humans and other animals. Our research agenda seeks to understand the dynamics of biological populations and epidemics, focusing on how to bring experimental and observational data together with mathematical theory. Tackling biological questions of public application and importance. The majority of my body of work has been on vaccine preventable diseases dynamics, with a view to understanding fundamental processes in ecology and evolution. The lab ongoing research falls into the following themes: (i) pathogen evolution and phylodynamics; (ii) seasonal disease transmission; (iii) anthropogenic effects (e.g. vaccines); (iv) adaptive behavior.

I'm fascinated by trying to understand the frequency, magnitude and shape of seasonal disease dynamics and the effects of population heterogeneity. Primarily, identifying evolutionary, ecological and demographic drivers of (re-) emergence and seasonality of infectious disease systems, with the aim of informing vaccination and other control public health policies. Scaling up individual-level interactions to understand the population-level outcomes (e.g., the evolution of virulence or immune evasion).

My approach is to develop theoretical models to understand how human and other animal systems behave generally, while simultaneously seeking to confront and validate models with data and make predictions. We use a combination of mathematical modelling, phylodynamics approaches and cutting-edge statistical inference techniques. We focus on developing coupled models (epi-econ, evo-eco) to holistically understand a system. With these methods we are able to make quantitative, testable predictions and confront process-based models with parallel data streams. This is the central premise of my lab's research program and the common thread of our work. At Rockefeller we focus on a co-creation models with partners from several parts of the globe.