- PhD in chemistry focused on Metabolomics and Data Science.
- I’m currently learning advanced statistical methods and machine learning techniques for metabolomics data analysis.
- I’m open to collaborate on projects involving metabolomics data processing, statistical analysis, and software development.
- Multivariate techniques (PCA, SVD, SVM, kNN, PLS, OPLS-DA, HCA, etc)
- Univariate statistics
- Resolving overlapping signals (MCR-ALS with initialization, ambiguity optimization, model rank determination)
- Data treatment (scaling, normalization, imputation, transforming, etc)
- Data dimensionality reduction for megavariate datasets
- Interpreting multivariate results to explain biological variance, identify biomarkers, and predict outcomes
- Reporting and presenting complex analysis results through scientific reports, publications, and visual dashboards
- Python, tkinter, plotly, matplotlib, pandas, numpy, R, ggplot2, git, JSON handling, JavaScript, Adobe API, etc

