  We present AutoOED, an Optimal Experiment Design platform powered with
automated machine learning to accelerate the discovery of optimal solutions.
The platform solves multi-objective optimization problems in time- and
data-efficient manner by automatically guiding the design of experiments to be
evaluated. To automate the optimization process, we implement several
multi-objective Bayesian optimization algorithms with state-of-the-art
performance. AutoOED is open-source and written in Python. The codebase is
modular, facilitating extensions and tailoring the code, serving as a testbed
for machine learning researchers to easily develop and evaluate their own
multi-objective Bayesian optimization algorithms. An intuitive graphical user
interface (GUI) is provided to visualize and guide the experiments for users
with little or no experience with coding, machine learning, or optimization.
Furthermore, a distributed system is integrated to enable parallelized
experimental evaluations by independent workers in remote locations. The
platform is available at https://autooed.org.
