Ph.D. Candidate in Biotechnology (RENORBIO/UFES) · M.Sc. in Genetics and Molecular Biology
I convert large-scale sequencing data into actionable biological insight — from raw reads to genome-resolved characterizations of microbial communities.
I work at the intersection of wet-lab microbiology and computational biology, currently applying this expertise to hydrocarbon-degrading microbial communities. My research spans the full pipeline: strain isolation and cultivation, Oxford Nanopore and NGS sequencing, metagenomic binning, MAG recovery, genome annotation, and comparative genomics, using Linux, R, and Python to process and interpret datasets that inform biological decision-making.
My broader background includes machine learning applied to biological data, algal lipid-mutant screening, rumen fluid microbiome characterization, and the study of hydrolytic bacteria — providing exposure to a wide range of microbial systems relevant to industrial biotechnology, from biofuel feedstocks to enzyme discovery.
- Metagenomics — end-to-end workflows, from raw sample to functional and taxonomic profiling
- Genome assembly & quality assessment — MAG recovery and microbial strain characterization
- Machine learning for omics data — classification and pattern discovery in biological datasets
- Cross-system expertise — algal lipid mutants, rumen microbiome, hydrolytic bacteria, hydrocarbon-degrading communities
- Bench-to-bioinformatics fluency — hands-on experience generating the data, not only processing it
- Scientific communication — data visualization and reporting in R for technical and non-technical audiences
Linux R Python Oxford Nanopore NGS Jupyter Notebook