About

Hello, I am a student has been working on many problems:

• Detecting natural selection
• Estimating selective pressures
• Quantifying time-varying selective pressures
• Inferring the distribution of fitness effects
• Detecting archaic admixture

My primary research interest lies in developing and applying advanced computational methods, such as machine learning and deep learning, to enable rigorous population genetic inference. I aim to uncover how evolutionary forces—mutation, genetic drift, gene flow, and natural selection—shape genetic diversity across species, with broad implications for understanding gene adaptation, biodiversity conservation, and evolutionary history. In addition, I am committed to integrating heterogeneous computational approaches into cohesive, reproducible pipelines, enabling efficient analysis of large-scale genomic datasets. Ultimately, my research seeks to bridge algorithmic innovation and biological discovery, advancing both the theoretical foundations and practical applications of evolutionary genomics.