My research focuses on understanding how neural networks process and learn from genomic data. Genomic language models such as the Nucleotide Transformer have been pretrained on vast amounts of DNA data and show promising performance on a range of benchmarks. As these systems become increasingly powerful and widely used in biological research, it's crucial to understand exactly how they arrive at their predictions. Using techniques from mechanistic interpretability - an emerging field that reverse-engineers neural networks - I develop methods to reveal how these systems represent and transform biological information. This work is aimed to help us build more reliable and capable AI systems in genomics, and biology more broadly. If any of this interests you, please feel invited to reach out!