I apply machine learning, algorithmics, and software engineering methods to data arising from population genomics and cancer genomics related questions.
As the modern genomic and transcriptomic techniques yield vast amounts of data, machine learning methods are well suited to tackle problems taking roots in these fields.
My research projects have led me to work with exome and transcriptome (RNA) sequencing data, as well as aCGH. I was particularly interested in the integration of these different techniques in human cancer samples, and how the consequent emergent information would give a better understanding of the sample specificities.
I've had the opportunity to work on projects related to the clinical (non-invasive diagnosis), technical (CNV detection), and molecular (non-coding RNAs) aspects of cancer.
My activities have led me to work both in a research and a clinical setting, often building bridges between both.
Method for the diagnosis of breast cancer.
European Patent EP2942399. 2015
Capturing drug response during surgery for pharmacogenomic discoveries.
5th Human Genetics in NYC Conference. 2018