I use computational and genomic approaches to understand how populations change through time and what evolutionary history can reveal about biological processes that cannot be directly observed.

 

During my Ph.D. in the Huber Lab at The Pennsylvania State University, I studied systems where genomic data alone are insufficient to reconstruct the past or predict future evolutionary trajectories. My research combined evolutionary simulation, population genomics, ancient DNA, and computational methods to investigate questions ranging from the spread of agriculture, to the consequences of hybridization, to purifying selection and deleterious variation in the genome. Across these systems, I became particularly interested in how integrative computational approaches can be used to interrogate the mechanisms underlying observed biological variation—reconstructing processes that cannot be directly observed and determining when those mechanisms provide information about future evolutionary outcomes.

​

As a postdoctoral researcher in the vonHoldt Lab at Princeton University, I am continuing to explore biological systems whose complexity requires integrative computational approaches, with red wolf conservation genomics providing an especially powerful example. In particular, I am interested in studying “ghost wolves”—canids that retain red wolf ancestry outside the currently managed red wolf population—and what they can reveal about the evolutionary and conservation histories of North American canids.

​

Prior to my Ph.D., I completed my B.S. with Interdisciplinary Honors at the University of New Hampshire in 2021, where I addressed biases in transcriptome and genome assembly as part of the MacManes Lab.

​​

Across my research, I am interested in a common problem: when does evolutionary history allow us to reconstruct the mechanisms that produced contemporary biological variation, and when can that history be used to predict future outcomes?LaPolice-Science