Education
- M.Sc. in Bioinformatics
- B.Sc. in Applied Mathematics
// bioinformatics · population genetics
Maria
Evangelinou
> Bioinformatician
Bioinformatics MSc graduate with a background in Applied Mathematics, focused on computational genomics and population genetics. I work in Python and R on statistical modelling for biological data, with hands-on experience in machine learning, population-genetics tooling (PLINK, kinship software) and genomics workflows. I care about integrating mathematics, code and molecular biology to support genetic and pharmaceutical research.
- MSc
- Bioinformatics
- BSc
- Applied Mathematics
- 1
- Peer-reviewed publication
- 2
- Research roles
// what I do
Where mathematics meets molecular biology
I turn raw biological data into clear, defensible answers — combining rigorous statistics with reproducible code.
Population genetics
Analysing genetic structure, ancestry and kinship across demographic scenarios, including admixture and relatedness inference.
Statistical modelling
Bootstrap methods, hypothesis testing and robust diagnostics for messy biological data, grounded in applied mathematics.
Machine learning
PCA, Random Forests and classification pipelines built with scikit-learn for prediction and dimensionality reduction.
Genomics workflows
Reproducible analysis on Linux with Bash, Bioconda and Git — from raw sequence data to interpretable results.
// at a glance
A quick overview
Research roles
- Assistant Researcher, Bioinformatics
- Data Scientist — Internship
Latest publication
- Genetic affinities between the ancient Greek colony of Amvrakia and its metropolis
// let's talk
Open to research and data-science roles
Genomics, population genetics, pharmaceutical research — or anywhere maths and biology meet.