I am a computational biologist working in cancer genomics, with a focus on using high-dimensional molecular data to understand how tumour cells evolve, change state, interact with their microenvironment, and respond to treatment.

My work combines transcriptomics, single-cell biology, multi-omics integration, statistics and machine learning, with an emphasis on producing reproducible analyses and biologically interpretable results.

Current focus: single-cell, spatial and multi-omics approaches to tumour heterogeneity, cellular plasticity and cancer progression.

Languages & workflows

Data analysis

scRNA-seq · Spatial transcriptomics · Bulk RNA-seq · miRNA-seq · Multi-omics · Differential expression · Pathway analysis · Immune deconvolution

Approach

Reproducible workflows · Statistical modelling · Machine learning · Data visualisation · Biological interpretation

Robust MAD-based quality control for high-dimensional biological datasets, designed for reproducible outlier detection and diagnostic visualisation.

Python implementation of the veryMAD workflow with support for modern single-cell analysis ecosystems.

Tools for reference-based batch correction of sequencing count data with diagnostics and confidence-aware outputs.

A lightweight Nextflow RNA-seq quantification workflow built around Salmon, QC and reproducible preprocessing.

Biological question
      ↓
Reproducible computation
      ↓
Statistical evidence
      ↓
Interpretable biology

I am particularly interested in computational approaches that connect molecular measurements with tumour cell state, evolutionary dynamics and phenotype, rather than treating high-dimensional data as an endpoint by itself.