Benjamin Eysenbach

Short Bio

Eysenbach joined the Princeton faculty in 2023. His research aims to develop principled reinforcement learning algorithms that obtain state-of-the-art performance with a higher degree of simplicity, scalability, and robustness than current methods. Much of his work uses ideas for probabilistic inference to make progress on a important problems in reinforcement learning (e.g., long-horizon and high-dimensional reasoning, robustness, exploration).

He completed a Ph.D. in machine learning at Carnegie Mellon University and spent a number of years at Google Brain/Research before and during his doctoral work. He has an undergraduate degree in mathematics from MIT.