Andrea Rodriguez Delherbe

My doctoral training focuses on applying computational models based on diverse machine learning techniques and high-performance computing to study the principles of gene regulation across evolution and species. As part of the Hughes Wellcome Group and under the context of epigenetics and haematology, I am currently studying the impacts of chromatin architecture and protein binding dynamics on gene regulation for a set of transcription factors throughout different cell types and tissues across species and evolution. For this, I am applying transformer-based networks hyper-tuned on red blood cells in-house datasets.

Before joining the RDM D.Phil in Medical Sciences program, I obtained a BSc in Computer Science with a management degree at Universidad Tecnica Federico Santa Maria, Chile, where I worked as a research assistant in implementing machine learning models to answer diverse scientific questions under contexts like biomedical imaging in cancer, bioinformatics for bacterial genome analysis, astronomy, and high-energy particle physics.

My goal as a genomics data scientist is to develop and apply machine learning algorithms to answer biological questions that contribute to developmental biology, physiology, immunology, and/or oncology.