I began loving biology in college. I had (and still have) a very broad interest in the basics of evolution and ecology. I started working in the lab (studying aging in mice) and in the field (looking at reproduction in seagulls and killifish). While these experiences helped me appreciate experimental and field work, which remain invaluable for scientific research, I wasn’t great in the lab.

At the same time as I was struggling in research, I became more curious about broad hypotheses in evolution. I realized that by analyzing data from many species, I would have more replication of evolutionary scenarios so that I could test large scale patterns and hypotheses across life, rather than within a few species. I then began developing predictions from evolutionary theory and testing these predictions across numerous species. As genomic, comparative, and phylogenetic data became larger and bigger (too big to look at) I realized I would need to become ‘computational.’ Over the past years, I was surprised and excited to realize that the computation can be as fun and rewarding as the biology.

One of my favorite parts of my work is that I get to engage and develop a diverse skill set in my daily life. This includes:

• A strong foundation in biology and interest in the natural world.
• Writing clearly.
• Collaboration, working and communicating well with others.
• Computer programming. Practically this means an eagerness to take on new challenges, as well as an ability to patiently read manuals and identify / fix mistakes.
• Familiarity with math and statistics

I learned to program with a combination of reading books, asking friends, google, and regular cycles of frustration and breakthrough.

I ask where, when, why, and how species have split from one another. By comparing the genomes of closely related species I can address this question by effectively ‘reading’ the genome in two different ways -- first as a record of history in which the patterns of mutations tell us when and where species have separated, and second as a set of instructions which describe how we make two different types of organisms.

In addition to my genomic analyses, I also combine our knowledge of evolutionary relationships among species with large datasets describing species’ distributions and other important traits, as well as mathematical modelling to build a synthetic view of how biodiversity is generated across space and time.

The Brandvain lab at the University of Minnesota–Twin Cities is recruiting graduate students!