Bayesian optimization is a global optimization strategy for (potentially noisy) functions with unknown derivatives. With well-chosen priors, it can find optima with fewer function evaluations than alternatives, making it well suited for the optimization of costly objective functions. Well known examples include hyper-parameter tuning of machine learning models (see e.g. Taking the Human Out of the Loop: A Review of Bayesian Optimization). The Julia package BayesianOptimization.jl currently supports only basic Bayesian optimization methods. There are multiple directions to improve the package, including (but not limited to)

Recommended Skills: Familiarity with Bayesian inference, non-linear optimization, writing Julia code and reading Python code. Expected Outcome: Well-tested and well-documented new features. Mentor: Johanni Brea