I'm a Computational Genomics Scientist turning multi-omics data into biomarker and clinical insights, one chaos moment and one debugging session at a time.

Bioinformatics Omics Machine Learning Analytics Visualization

I work across a full spectrum of computational biology workflows, from study design to discovery.

A few pieces of my toolkit:

Unsupervised learning: consensusClustR ·
Consensus clustering from subsample-clusters, for when you need robust, stable sample subgroups.

Study design: GitHub repository omicsPoweR · Shiny app
Power analysis for omics studies.

Dimensionality reduction: GitHub repository for PCA EigenSpace · Interactive Shiny app for exploring PCA · An example on Understanding PCA.

Visualization: Vennify ·
Proportional Venn diagrams from gene lists.

Supervised learning: Classification & regression workflows spanning tree-based bagging/boosting (Random Forest, GBM), instance-based (KNN), kernel-based (SVM), and regularized regression (Lasso, Elastic Net, Ridge).

Multiple projects at various stages of the concept-to-completion cycle 🙇‍♀️, including lung transplant outcomes and pediatric liver disease.

• Our work on plasma proteome correlations with liver stiffness in pediatric cholestasis is published in Hepatology Communications.

• A study on extracellular vesicle mRNA profiles in ex vivo lung perfusion and primary graft dysfunction is currently under peer review.

📫 Find me on LinkedIn
🤓 Nerdy details: Google Scholar · ORCID

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