Michael Buice is a member of the Modeling, Analysis, and Theory team at the Allen Institute. Before arriving at the Allen Institute, he worked in the lab of Ila Fiete at the University of Texas at Austin, where he helped derive a system size expansion for the Fisher Information for sensory and working memory systems, and developed analytic expressions for the fluctuations in attractor network models of neural networks. He was a postdoctoral fellow in Carson Chow’s group at the National Institutes of Health (NIH) where he applied kinetic theory and density functional theory to oscillator models of neural networks, answering open questions regarding the stability of asynchronous firing states in networks of finite size, a dynamical phenomenon related to the information present in the network. In addition, he helped construct a method for deriving equivalent reduced stochastic equations for systems with “incomplete information”, such as an interacting network of neurons in which only a few neurons are actually recorded. Buice earned a Ph.D. in physics from the University of Chicago working with Jack Cowan to adapt techniques from the analysis of reaction-diffusion systems in physics to the statistics of simple models of neural networks.