Stanley Hall, #260
University of California
Office: Stanley Hall 260J
I am interested in understanding how evolution works at a quantitative level, with enough precision to eventually predict the rates of different evolutionary outcomes. Once a lofty theoretical goal, this project is just starting to become feasible empirically, thanks to advances in DNA sequencing that allow us to track the evolution of rapidly adapting viruses and bacteria. Due to their short generation times, these organisms can acquire mutations on human-relevant timescales, and their small size allows for a degree of replication across isolated subpopulations. However, to make full use of this new stream of data, we must solve two related theoretical problems: First, how does observed sequence variation constrain potential models of microbial evolution? And given these constraints, what are the testable predictions for the spectrum of evolutionary trajectories? To address these questions, I use a combination of theoretical techniques from statistical physics and population genetics along with empirical data from laboratory evolution experiments and natural populations of viruses and bacteria, such as those that inhabit the human gut.
Before coming to Berkeley, I was a graduate student in the Physics Department at Harvard University, where I studied evolutionary dynamics and population genetics with Michael Desai. I received my BA from Swarthmore College. From 2008-2010, I conducted research at the Santa Fe Institute (initially as part of their summer REU program), where I worked on methods to detect modular structure in large, complex networks with Aaron Clauset (now a professor at CU Boulder).