More recently, I’ve been applying my knowledge of evolution to understand and improve protein design. High-throughput sequencing-based assays can be combined with modern machine learning methods to explore the space of amino acid sequences to efficiently improve the function of proteins of interest. However, not much is understood about what sorts of data should be taken, and how sequences should be selected from models. I’m currently using in-silico fitness landscapes to experiment on the design process itself, and to understand how landscape properties translate to effective design strategies.