Sunbochen Tang is a PhD student whose research interests are in control theory, optimization, and machine learning. Specifically, Sunbochen is developing novel learning-based approaches to control complex dynamical systems with safety guarantees. With the support of a MathWorks Fellowship and drawing on MATLAB and Simulink, he will explore data-driven control methods for safety-critical autonomous systems with uncertain dynamics. The primary objective of his work is to develop efficient control-oriented meta-learning algorithms for systems to adapt to unknown disturbances in complex environments online. Ultimately, Sunbochen aims to develop open-source modular MATLAB scripts and Simulink models to test such learning-based control algorithms in a realistic simulation environment, thereby developing a deeper understanding of their benefits and potential limitations. His research has the potential to improve the data efficiency and safety guarantees of current learning-based algorithms by introducing control-theoretic designs and advancing the development of trustworthy autonomous systems in general.