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 past and present research projects include using circulating miRNAs as cancer biomarkers, studying the global disruption of antisense long non-coding RNAs in breast cancer samples, analyzing real-time pharmacogenetics phenotypes in ancestrally diverse populations, using graph theory analysis on Identity-By-Descent networks and developing novel, machine learning based, gene prioritization methods.
My research projects have led me to work with exome, and transcriptome (RNA) sequencing data, as well as genotyping and aCGH. I was particularly interested in the integration of these different techniques, and how the consequent emergent information would give a better understanding of sample specificities.
During my thesis, 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. Throughout my postdoc, I've applied statistical genetics methods to biobank-scale datasets.
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
Towards cancer mega-cohorts: A novel homogenization algorithm applied to diverse breast cancer RNA-Seq datasets.
American Society of Clinical Oncology (ASCO) annual meeting. 2020
Rapid response to the Alpha-1 Adrenergic Agent Phenylephrine in the Perioperative Period is Impacted by Genomics and Ancestry.
6th Human Genetics in NYC Conference. 2018
Capturing drug response during surgery for pharmacogenomic discoveries.
5th Human Genetics in NYC Conference. 2018