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I am a highly skilled and accomplished statistician with over eight years of experience in experimental statistics and statistical modelling. I hold a PhD in Statistics from the University of São Paulo, Brazil, and during my PhD studies, I gained expertise in statistical modelling in agricultural data while working as a visiting scholar at the National University of Ireland Galway, Ireland. After completing my PhD, I worked as a lecturer at ESALQ/University of São Paulo from 2017 to 2019.

In 2019, I 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. I contributed significantly to the development of statistical methods applied to athlete performance and predictive models for COVID-19, where I 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.

I am not only an accomplished statistician but also an advocate for using dashboard apps to create interactive data visualization. I believe 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, I was awarded a Marie Skłodowska-Curie Actions (MSCA) COFOUND Fellowship (Train@Ed) to work at The Roslin Institute, University of Edinburgh where I worked on developing statistical models applied to quantitative genetics genomics of plant and animal breeding, and I am excited to bring my expertise to the AbacusBio team.

Apart from my professional achievements, I also have an extensive publication record in the field of statistics, showcasing my expertise in the area. In my free time, I enjoy photography, which I see as a way to capture the beauty of the world around me.

Thiago de Paula Oliveira 's Projects

covid_forecast icon covid_forecast

Making data and code available as supplementary material promotes transparency and reproducibility, enabling anyone to analyze data, reproduce our findings, and improve the methodology.

lcc icon lcc

Longitudinal Concordance Correlation (LCC) Estimated by Fixed Effects and Variance Components using a Polynomial Mixed-Effects Regression Model

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