Thiago de Paula Oliveira
Thiago de Paula Oliveira

Statistician

About Me

I am Thiago de Paula Oliveira, a statistician at AbacusBio with 14+ years of experience turning experimental, genomic, and performance data into decisions. I specialise in advanced mixed-model and Bayesian analytics, the development of economic and sustainability selection indexes, and the delivery of reproducible analytics products, from R, C++, bash, SQL codebases to Dockerised dashboards. Whether the brief is accelerating genetic gain, improving farm-system resilience, or strengthening agricultural production systems, I focus on rigour, transparency, and decision-ready outputs.
Interests
  • Statistics and biostatistics
  • Concordance analysis
  • Multilevel and forecast models
  • Generalized mixed-effects models
  • Longitudinal data
  • Quantitative genetics & breeding analytics
  • Agricultural decision-support dashboards
  • Reproducible analytics pipelines for agri-genomics
  • Economic and sustainability selection indexes
Education
  • PhD in Statistics

    University of Sao Paulo

  • MSc in Statistics

    University of Sao Paulo

  • BSc in Agricultural Engineering

    University of Sao Paulo

Expertise and Research
I am a statistician with 14+ years of experience turning experimental, genomic, and farm-system data into decisions. After completing my PhD in Statistics at the University of Sao Paulo, I specialised in advanced mixed-model/Bayesian analytics and in the development of economic and sustainability selection indices that keep breeding programmes accountable.

As a Consultant Statistician at AbacusBio, I lead cross-functional teams that deliver genetic-evaluation pipelines, automated QC/ETL workflows, and decision dashboards for livestock, crop, and agri-tech partners. That work depends on production-grade code in R, C++, bash, and SQL, Docker-based reproducible environments, and early collaboration between domain scientists and data engineers.

Earlier, I held a Marie Sklodowska-Curie COFUND fellowship at the Roslin Institute (University of Edinburgh), worked with multidisciplinary research and industry teams, and lectured in statistics at USP. Along the way I have published across Nature-branded journals, advised national breeding programmes, and mentored teams on delivering transparent, auditable analyses for agriculture and genomics.

Whether the brief is accelerating genetic gain, improving farm-system resilience, or strengthening agricultural production systems, my bias is toward rigour, reproducibility, and decision-ready outputs. Browse my recent publications and projects, and get in touch if you would like to collaborate or have a specific challenge in mind.

Featured Publications
Recent Publications
(2025). Breeding for sustainability: Development of an index to reduce greenhouse gas in dairy cattle. Animal.
(2023). Developing best practices for genotyping-by-sequencing analysis in the construction of linkage maps. GigaScience.
(2023). Pedigree-based Animal Models Using Directed Acyclic Graphs. Under consideration in Livestock Science.
Cited by
Citations
295
h-index
10
i10-index
11
Works
35
OpenAlex | Updated 2/25/2026
2018: 35 citations20182019: 16 citations20192020: 32 citations20202021: 95 citations20212022: 14 citations20222023: 58 citations20232024: 6 citations20242025: 0 citations202595 citations
Recent & Upcoming Talks
Recent Posts

Benchmarking Kendall's Tau in R and Rcpp

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++.

SQL for beginners - an example in field-trial

As a statistician, I teach SQL from zero using a realistic agriculture dataset: tables, keys, joins, summaries, quality checks, and a mini treatment-effect analysis.

Connect with an expert statistician

I focus on advanced statistical modelling, economic and sustainability selection indices, interactive dashboards, and reproducible (Dockerised) pipelines that deliver decision-ready insights for agriculture and genomics.

Areas of impact

Agriculture. Design and analyse agronomic and farm-systems experiments, including multi-environment trials and spatial models, to optimise yield, resource use, and sustainability.

Genetics. Build genetic-evaluation pipelines and economic and sustainability selection indices that maximise genetic gain and inform breeding objectives.

Agri-tech analytics. Build practical decision-support tools that connect statistical modelling, data engineering, and stakeholder communication.

Explore my publications, projects, and recent work. If you are interested in collaborating or would like to learn more, please get in touch.

Stay connected and follow my work in statistical modelling and data analysis:

Google Scholar | GitHub

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