Tim Beissinger, Ph.D. was a postdoc in the Ross-Ibarra lab at UC Davis.  He was researching linked selection during maize domestication. This research included assessing changes in the historical population size of maize and the selective pressures that it experienced. The work has implications regarding the abundance of rare deleterious alleles in the maize genome. Previously, Tim studied artificially selected maize and chicken populations, and released a software package called GenWin that facilitates defining window boundaries for population-scale genomic analyses.

Why I became a scientist:

I love all types of puzzle solving. In elementary school, I was the kid who would collect everybody else’s broken RC cars and other electronics to disassemble and reassemble into something different, unique, and usually quite useless. To this day, “fixing things” is one of my favorite hobbies. During my college years, I studied math (which I loved and still love), but the lack of a clear connection between pure mathematics and tangible outcomes began to wear on me. At that time, I had a part-time job in Natalia de Leon’s quantitative genetics group at the University of Wisconsin, Madison. The work I was assisting with involved developing maize varieties that yield high biomass and are well suited for biofuels production. I realized that what started out as just a part-time job actually represented a field of research that I was very excited about. Although I stumbled into it by accident, it was apparent that quantitative genetics was a great place to leverage my math training toward my favorite past-time: problem solving. After making this realization, I pursued a PhD in quantitative genetics, and I couldn’t be happier with it! As a scientist, I get to spend my workdays essentially playing games: DNA is a puzzle and a code, and  my job is to think, code, and experiment to solve it’s puzzles and crack it’s code.