I obtained my bachelor’s degree in biotechnology. During my undergraduate studies, I worked on biological networks and conducted a project to predict new cancer-SNP associations using link prediction algorithms. For my master’s thesis, I developed a machine learning-based model to predict protein interactions.

Currently, I am working on a project that aims to understand the adaptive architecture of gene expression evolution. My research involves a combination of eQTL mapping and experimental evolution. A key distinction from other eQTL studies is that I will not rely on a natural population. Instead, I will generate an outcrossed population derived from two founder genotypes. This approach will likely result in higher linkage disequilibrium (LD) compared to natural populations while ensuring that all allele frequencies remain consistent across the entire genome. It is anticipated that this will not only facilitate eQTL mapping but also lead to a clearer selection response.