Yuri Nesterov is a foundational figure in optimization, best known for Nesterov's accelerated gradient descent (1983). This "momentum" method dramatically speeds up convergence, making it a cornerstone of modern machine learning. He also co-developed the theory of interior-point methods and self-concordant functions, which revolutionized convex optimization. His "smoothing technique" provided a way to efficiently solve non-smooth problems, solidifying his massive impact on AI, operations research, and control theory.