Traditional computing sciences have made significant advances with tools like Complexity and Worst-Case Analysis. However, Machine Learning has unveiled optimization challenges, from image generation to autonomous vehicles, that surpass the analytical capabilities of past decades. Despite their theoretical complexity, such tasks often become more manageable in practice, thanks to deceptively simple yet efficient techniques like Local Search and Gradient Descent. In this talk, we will delve into the effectiveness of these algorithms in complex environments and discuss developing a theory that transcends traditional analysis by bridging theoretical principles with practical applications. We will also explore the behavior of these heuristics in multi-agent strategic environments, evaluating their ability to achieve equilibria using advanced tools from Optimization, Statistics, Dynamical Systems, and Game Theory. The discussion will conclude with an outline of future research directions and my vision for a computational understanding of multi-agent Machine Learning. Bio: Emmanouil-Vasileios (Manolis) Vlatakis Gkaragkounis is currently a Foundations of Data Science Institute (FODSI) Postdoctoral Fellow at the Simons Institute for the Theory of Computing, UC Berkeley, mentored by Prof. Michael Jordan. He completed his Ph.D. in Computer Science at Columbia University, under the guidance of Professors Mihalis Yannakakis and Rocco Servedio, and holds B.Sc. and M.Sc. degrees in Electrical and Computer Engineering. Manolis specializes in the theoretical aspects of Data Science, Machine Learning, and Game Theory, with expertise in beyond worst-case analysis, optimization, and data-driven decision-making in complex environments. His work has applications across multiple areas, including privacy, neural networks, economics and contract theory, statistical inference, and quantum machine learning.