CV

Web résumé summary and downloadable CV/résumé for Ricardo R. Pavan.

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Last updated: 2026-07-24

Summary

Bioinformatics scientist with PhD-level training in high-throughput biological data and applied statistics, specializing in statistical modeling, machine learning, pipeline development, and reproducible large-scale data analysis. Track record of peer-reviewed publications, open-source scientific software (CRESSENT), and collaboration across marine ecology, virology, and biomedical research contexts.

Experience

Postdoctoral Researcher, Bioinformatics — The Ohio State University · May 2023 – present
Reproducible viral/microbiome bioinformatics pipelines (CRESSENT, VirION3), statistical analysis for microbiome and virome datasets, cyberinfrastructure integration (CyVerse/ACCESS/Tapis), mentoring.
Doctoral Researcher — University of Tasmania (IMAS) · Nov 2018 – Apr 2022
Marine microbial ecology, 16S/18S sequencing, network analysis and machine learning, environmental gradient analysis.
Project Analyst — Brazilian Ministry of Science and Technology · Mar 2016 – Nov 2018
Science policy, strategic program management, international scientific cooperation.

Education

  • PhD, Biological Sciences — University of Tasmania (2018–2022)
  • MBA, Data Science and Analytics — University of São Paulo (2020–2023)
  • MSc, Animal Biology — University of Brasília (2009)
  • BSc, Biological Sciences — University of Brasília (2005)

Grants & funding

  • Co-Principal Investigator Award, DOE Joint Genome Institute (JGI) Community Science Program
  • Postdoctoral Award, W.K. Kellogg Foundation OK-PROS Program
  • PhD Scholarship, Tasmania Graduate Research Scholarship (TGRS)

Selected publications

See the full Publications page for the complete, filterable list with DOIs.

Technical skills

Languages & tools: Python, R, Bash, SQL, Git/GitHub Pipelines & orchestration: Nextflow, Snakemake HPC & infrastructure: Slurm, Docker, Apptainer/Singularity, Conda, CyVerse, ACCESS, Tapis Analysis environments: Jupyter, Quarto Sequencing data: Illumina, Oxford Nanopore, PacBio; 16S amplicon, shotgun metagenomics, viromics, bulk and single-cell/single-nucleus RNA-seq Statistics & ML: Regression, classification, clustering, dimensionality reduction, mixed-effects models, time-series analysis

For the full narrative version of my background, see About.