My area of expertise is at the intersection of artificial intelligence, machine learning and education with a particular focus on using cutting-edge technological solutions to improve student learning experience. I also have extensive experience in high-throughput genomic analysis, where I integrate complex and multi-scale biological datasets to uncover meaningful insights.

Specifically, my research involves developing AI-based methods and applying them to clinically annotated omics data across various cancer subtypes. This includes the development of commercial and clinically applicable AI-based software to aid in the analysis and interpretation of genetic and epigenetic features of cancer-associated diseases.

My proficiency spans a range of statistical pattern recognition techniques, including unsupervised, semi-supervised, and supervised learning. Moreover, I specialise in high-dimensional data processing and mining, particularly in Illumina 450K/EPIC DNA Methylation and RNA-Seq (NGS data), as well as Mass-Spec DNA methylation and NanoString mRNA gene expression processing.

In addition, I have experience at developing bioinformatics tools, pipelines, and software packages tailored for the analysis of large cohorts, facilitating comprehensive investigations into the genetic and epigenetic landscape of cancer-associated diseases.