Alexander Rakhlin

Alexander Rakhlin is a Professor in the Department of Brain & Cognitive Sciences at MIT, with affiliations at the MIT Statistics and Data Science Center and the Institute for Data, Systems, and Society (IDSS). His research bridges statistics, machine learning, and optimization, focusing on online prediction, reinforcement learning, and decision-making under uncertainty. He holds a PhD from MIT (2006) and previously served as an associate professor at the University of Pennsylvania (Wharton School) before joining MIT in 2023.

Research interests include reinforcement learning, neural networks, overparametrized models, and the theoretical foundations of machine learning. His work addresses challenges in sequential decision-making, robust algorithms, and the statistical complexity of modern models like large language systems. Notable contributions include the Decision-Estimation Coefficient framework for RL analysis and insights into benign overfitting in overparametrized settings.

Recent publications emphasize algorithmic advances in online learning, privacy-aware contextual bandits, and structural watermarking for language models. His theoretical work often unifies principles from information theory, optimization, and statistical learning.

Lab affiliations include the Laboratory for Information and Decision Systems (LIDS), where he collaborates on interdisciplinary projects. No specific awards or grants are explicitly listed in the provided texts.