First-order methods are widely used to tackle data science and machine learning problems with complex structures, such as nonconvexity, nonsmoothness, and stochasticity. However, in many real-world scenarios, the problem structure and parameters can be unknown or ambiguous, creating significant challenges for algorithm design and stepsize selection. In this talk, I will present a novel class...