Computational Protein Evolution and AI for Genotype–Phenotype Modeling.
My research focuses on large-scale protein foundation models and their application to mutational effect prediction, fitness landscape modeling, and evolutionary trajectory inference. I work on fine-tuning protein language models (e.g., ESM, METL) on deep mutational scanning (DMS) datasets and leveraging transfer learning across protein families to improve generalization across divergent sequence spaces.
I am particularly interested in integrating learned sequence embeddings into evolutionary simulation frameworks to model genotype–phenotype–fitness relationships, and in comparing transformer-based architectures with state-space models (e.g., Mamba) for scalable long-protein modeling.