Beckett Sterner studies how mathematics is transforming biology, including biodiversity data aggregation, evolution of biological individuality, evolutionary tempo and mode, and methodology in systematic biology. He came to ASU in 2016 as an assistant professor in the Biology and Society Program and affiliated faculty in philosophy.
He started his career working in a computational biology lab studying protein function during college at MIT, and then switched to doing history and philosophy of science for his doctorate at the University of Chicago.
His research focuses on the question, When and why is mathematics useful for biology? Biologis…
Beckett Sterner studies how mathematics is transforming biology, including biodiversity data aggregation, evolution of biological individuality, evolutionary tempo and mode, and methodology in systematic biology. He came to ASU in 2016 as an assistant professor in the Biology and Society Program and affiliated faculty in philosophy.
He started his career working in a computational biology lab studying protein function during college at MIT, and then switched to doing history and philosophy of science for his doctorate at the University of Chicago.
His research focuses on the question, When and why is mathematics useful for biology? Biologists have determined the sequences of billions of nucleotides in thousands of genomes, and they have measured the expression levels of tens of thousands of genes across numerous species. However, their appetite for data is quickly outrunning their ability to give it theoretical significance. The movement to quantify life, exemplified here by genomics and its descendants, is no simple benefit to biology: at minimum, it poses major challenges for the nature and practice of biological theory. One leading solution is the introduction of computer modeling into biological theorizing, but little consensus exists among biologists on how and when computer modeling helps.
He investigates these issues by studying the process and outcomes of mathematization i.e. the consequences of making math indispensable for scientific research. Some new and ongoing projects include: the impact of computational workflows on the methodology and social structure of systematic biology (taxonomy/phylogenetics); big data and principles for managing flawed data aggregation; integrating model selection and hypothesis testing in paleobiology; and incorporating explicit landscape geometry into our theory of population lineages.