This presentation provides a comprehensive introduction to Bayesian Optimization, a powerful technique for efficiently finding optimal solutions in expensive-to-evaluate functions by building probabilistic models and intelligently selecting where to sample next. We'll discuss how to use BO in robust optimization where an optimum that is resistant to perturbations in the input parameters is desired. Additionally, we'll discuss the use of BO in formulation optimization where both components and their proportions are jointly optimized.