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These are the slides I used for my promotion talk to associate member at the Fred Hutch. My abstract follows:
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 antibodymaking B cells.
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