Statistical methodology for population genetics inference from massive datasets with applications in epidemiology

Daniel's research focuses on problems arising from the volume of genetics data currently available. He uses and develops tools in statistics and machine learning to apply powerful genetics models at scale. His research examines population structure and its interaction with genomic selection. This provides insight into both the genetic history of people and the functional roles that genes may play in populations experiencing different environments. His research helps us to understand the information provided by massive-scale analyses, such as genome-wide association studies, as well as trying to identify genetic variants causing disease.