Thiago is a highly skilled and accomplished statistician with over eight years of experience in experimental statistics and statistical modelling. He holds a PhD in Statistics from the University of São Paulo, Brazil, and during his PhD studies, he gained expertise in statistical modelling in agricultural data while working as a visiting scholar at the National University of Ireland Galway, Ireland. After completing his PhD, Thiago worked as a lecturer at ESALQ/University of São Paulo from 2017 to 2019.
In 2019, Thiago joined the Insight Centre for Data Analytics in partnership with Orreco, School of Mathematics, Statistics, and Applied Mathematics, and NUI Galway as a Researcher Biostatistician. He contributed significantly to the development of statistical methods applied to athlete performance and predictive models for COVID-19, where he worked on developing statistical methods in longitudinal concordance correlation, multilevel model (hierarchical model), generalized linear mixed-effects model, state-space models, experimental design, and longitudinal data.
Thiago is not only an accomplished statistician but also an advocate for using dashboard apps to create interactive data visualization. He believes that apps are an efficient tool to make visual representations of large scale data sets and enable users to explore the complex reality of the database, or even handle multiple data locations in a single visualization.
In 2020, Thiago was awarded a Marie Skłodowska-Curie Actions (MSCA) COFOUND Fellowship (Train@Ed) to work at The Roslin Institute, University of Edinburgh where he worked on developing statistical models applied to quantitative genetics genomics of plant and animal breeding, and he is excited to bring his expertise to the AbacusBio team.
Apart from his professional achievements, Thiago also has an extensive publication record in the field of statistics, showcasing his expertise in the area. In his free time, he enjoys photography, which he sees as a way to capture the beauty of the world around him.