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Sasha Rakhlin is a professor at the Massachusetts Institute of Technology (MIT) in the Statistics and Data Science Center and the Department of Brain and Cognitive Sciences. His research group works at the intersection of Machine Learning, Statistics, and Optimization, where they focus on formalizing learning processes, analyzing learning models, and deriving and implementing emerging learning methods. A significant thrust of his research involves developing theoretical and algorithmic tools for online prediction, decision-making, and reinforcement learning. Recently, his interests have expanded to understanding reinforcement learning in decision-making contexts, neural networks, overparametrized models, and large language models. His work emphasizes understanding sample complexity and developing computationally efficient methods that bridge supervised learning and decision-making. Furthermore, he has developed a theory of decision-making based on Decision Evolutionary Control (DEC) that governs sample complexity in reinforcement learning. His focus on online learning aims to develop robust prediction methods that adapt to data with independent and identically distributed (i.i.d.) properties.