Over 7 years of research experience, leveraging data analysis and high-performance computation to develop stochastic models of complex real-world systems. Notably, developed methods to derive underlying stochastic differential equations from multidimensional data sets. Proficient in Python, C++, MATLAB and R. A team-worker and highly motivated to drive insights and innovation at the intersection of mathematics, data science, and quantitative research.
Currently, a Research Fellow at UCL, developing mathematical models for the evolution of antimicrobial resistance (AMR) in heterogeneous environments. Previously, obtained a PhD and an MRes in Statistical Applied Mathematics from the SAMBa CDT in Bath, a MSc in Physics at the São Paulo State University (UNESP), and a BSc in Physics at the University of São Paulo (USP).
In my free time, I am honing out my skills in Data Science, reading, hiking or learning a new language.
PhD in Statistical Applied Mathematics, 2024
University of Bath
MRes in Statistical Applied Mathematics, 2021
University of Bath
MSc in Physics, 2020
ICTP-SAIFR / UNESP
BSc in Physics, 2017
Universidade de São Paulo

The effectiveness of non-pharmaceutical interventions, such as mask-wearing and social distancing, as control measures for pandemic disease relies upon a conscientious and well-informed public who are aware of and prepared to follow advice. Unfortunately, public health messages can be undermined by competing misinformation and conspiracy theories, spread virally through communities that are already distrustful of expert opinion. In this article, we propose and analyse a simple model of the inter-action between disease spread and awareness dynamics in a heterogeneous population composed of both trusting individuals who seek better quality information and will take precautionary measures, and distrusting individuals who reject better quality information and have overall riskier behaviour. We show that, as the density of the distrusting population increases, the model passes through a phase transition to a state in which major outbreaks cannot be suppressed. Our work highlights the urgent need for effective interventions to increase trust and inform the public.