Molecular Biology student at Sofia University with a strong focus on Bayesian statistics, applied machine learning, causal inference and bioinformatics workflows. I build reproducible analyses and pipelines in R (tidymodels / Stan) and lightweight shell workflows for genomics data processing.

• Name: Metodi Latinov
• Role: Molecular Biology student, applied statistics & data science projects
• Location: Sofia, Bulgaria
• Focus: Bayesian inference, causal inference methods, predictive modelling, RNA-seq QC pipelines, reproducible analysis in R
• Open to: collaborations on bioinformatics / stats projects, research internships, data-science practice problems

• Languages & tooling: R (tidymodels, tidyverse), Stan, Shell/Bash, Julia, LaTeX
• Methods: Bayesian statistics, causal inference (meta-learners), supervised learning (classification & regression), model explainability, Monte Carlo experiments for model validation
• Bioinformatics: RNA-seq QC and alignment workflows (FastQC, fastp, HISAT2, featureCounts)
• Practices: reproducible code, literate analysis, version control, clear documentation

• tidycausality — Meta machine-learning algorithms for causal inference in R (r-learners, S/U/T/X learners); R, tidymodels.
• bayesian-viral-infectivity — Bayesian estimation of viral infection probability across dilutions; R, Bayesian inference.
• qcflowr — Automated RNA‑seq QC → trimming → alignment → quantification pipeline using FastQC, fastp, HISAT2, featureCounts; Shell / bioinformatics.

• Email: metodilatinov@abv.bg
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• Portfolio / website: (add your portfolio link here)
• CV: (link to CV / Google Drive / PDF)