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Information geometry is the use of information theory and differential geometry to understand the intrinsic structure of nonlinear models. Raju’s uses information geometry to understand how we make simplified models of phenomena. In physics, a series of calculations known collectively as the Renormalization Group (RG) explains how the model used to understand a phenomena changes with the scale of observation. Previously, Raju used information geometry to better understand RG calculations with the hope of gaining insights relevant to a larger class of models. The overarching challenge in biology is to find the right coarse-grained variables to describe systems, and Raju is exploring whether simple “toy models” can be used to better understand the nature of coarse graining in biology. He is also interested in dynamical systems theory, and its application to biology, in particular cell development and ecology.