PhD in Statistics
University of Sao Paulo
MSc in Statistics
University of Sao Paulo
BSc in Agricultural Engineering
University of Sao Paulo
As a Consultant Statistician at AbacusBio, I deliver statistical and analytical solutions for plant and animal breeding programmes, lead cross-functional delivery of genetic-evaluation pipelines, and build traceable workflows that link raw data, cleaned datasets, model inputs, and final outputs. That work depends on production-grade code in R, C++, Bash, and SQL, Docker-based environments, and clear technical documentation.
Earlier, I held a Marie Sklodowska-Curie COFUND fellowship at the Roslin Institute, worked on public-health and sports analytics projects at the National University of Ireland Galway, and taught statistics and quantitative methods at the University of Sao Paulo. Across those roles I developed software, dashboards, and analytical pipelines designed for transparency, reuse, and auditability.
I combine advanced statistical modelling with reproducible analytical tooling to make complex data more usable for researchers, analysts, and stakeholders. Browse my recent publications and projects, and get in touch if you would like to collaborate.
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Pearson correlation is a precise answer to the question of linear association. This tutorial uses simulated examples and matrixCorr to show when that answer is enough and when other dependence questions are worth asking.
Implement and benchmark a fast Kendall’s tau-a in C++ via Rcpp against base R, discuss tie handling (tau-b), and when to move from R to C++.
As a statistician I teach SQL from zero using a realistic agriculture dataset with tables keys joins summaries quality checks and a simple treatment effect analysis.
what each tool solves, when to reach for it, and ready-to-paste code.
This post explores the performance of various compression techniques in R for reading and writing operations, highlighting file size, speed, and memory usage.
I focus on data quality, statistical modelling, reusable analytical tooling, and reproducible workflows that turn complex datasets into reliable, decision-ready outputs.
Data stewardship. Design reproducible analytical workflows, automated QC/ETL pipelines, and structured computational environments that support reliability and traceability.
Data quality and usability. Build dashboards, software packages, and decision-support tools that improve accessibility, consistency, and interpretability of complex data.
Technical leadership. Lead cross-functional teams, mentor colleagues, and translate quantitative work into practical outputs for researchers, analysts, and stakeholders.
Explore my publications, projects, and recent work. If you are interested in collaborating or would like to learn more, please get in touch.
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