Ge obtained her Master's degree in Bioinformatic in 2022. In her Master's thesis, she aims to investigate how reliable commonly used machine learning algorithms are in predicting phenotypes from genotypes under different circumstances like number of causal loci and effect sizes and how well the top predictors in these models map to causal mechanisms. She is interested in identifying the genetic factors driving the evolution of pathogens and using machine learning methods for predicting bacterial traits.