Abstract: Our knowledge about much of biology is indirect: rather than directly observing a process we observe some noisy result of that process. In addition, we almost never have a complete description mapping underlying processes to observations. Given these challenges, what framework can we use to use to understand biology? In this talk I will describe the use of probabilistic models to learn about evolution from biological data. Starting with the more familiar terrain of solving equations and performing integration in math, I will describe how these same concepts are generalized to the probabilistic setting. I will illustrate how this works in practice with examples from our current research on reconstruction of evolutionary trees and maturation of antibody-making B cells.