I am a molecular biophysicist who studies the mutational landscapes of proteins with a focus on bridging high-throughput functional experiments with predictive and interpretable statistical models. The broad goal of my work is ultimately to turn biochemistry from a descriptive to a predictive science in which we can learn and understand protein functions directly from their sequences. I am especially interested in how substrate or ligand specificity is encoded in large families of homologous proteins and in how protein’s dynamic personalities shape their fitness landscapes.

Currently, I am a postdoctoral fellow in Ben Lehner’s group at the Sanger Institute, where I am working on several projects related to integrating experimental and computational methodologies to learn biochemical principles over vast sequence spaces. Previously, I was a PhD student in Biophysics at Harvard, where I worked with Rachelle Gaudet and Debora Marks on learning the sequence determinants of substrate selectivity using deep mutagenesis coupled with evolutionary sequence modeling.