Across biology — from microbial communities to cancer tumors to ancient human populations — researchers measure diversity in ways that shape how we understand life. But those measurements are often discipline-specific, limiting their ability to reveal deeper patterns across systems.
Complexity Postdoctoral Fellow Maike Morrison builds mathematical tools to quantify and compare biological variation. Her work draws on ecology, population genetics, and information theory to study the structure, diversity, and stability of populations — whether those populations consist of species in an ecosystem, microbes in the gut, ancestry fractions in humans, or even assets in a financial portfolio. As an example, her doctoral research at Stanford University produced methods and software for analyzing mutational diversity in cancer, heterogeneity in microbiomes, and ancestry variability in human populations.
At SFI, Morrison plans to continue building cross-cutting frameworks to understand biological variation across fields and scales. Beginning with projects in population genetics, she plans to develop models that better reflect the genetic diversity within groups and challenge overly broad assumptions about differences between them. She’s also applying methods from biology to study heterogeneity in economic systems, including a project analyzing supply chains in Ecuador.
“Part of my work in population genetics is to develop better data science approaches that allow us to emphasize the diversity within each human population and the similarities across populations, rather than treating each group as a monolith,” she says.
Morrison earned a B.S. in mathematics from the University of Texas at Austin in 2020, where she worked with Professors Mark Kirkpatrick and SFI External Professor Lauren Ancel Meyers as a member of the Dean's Scholars Honors Program. She completed her Ph.D. in ecology and evolutionary biology at Stanford University, supported by an NSF Graduate Research Fellowship and a Stanford Graduate Fellowship.