 Recently, Noack works on implementing the powerful methods of machine learning in turbulence control.Major breakthroughs are learning the control law in real-world experiments with Machine learning control (MLC) and an automated learning the control-oriented dynamical gray-box model from experimental data.Supplementary projects include data visualization, phenomenological models, vortex models and entropy-based optimization in addition to a spectrum of model-free and model-based control approaches.