Ben Eysenbach is a first-year PhD student at Carnegie Mellon University who teaches machines to make smart decisions that help humans. His research in reinforcement learning has focused on making robots safer and enabling them to learn autonomously, with less human engineering effort. Ben's work has appeared at the top machine learning conferences. He co-founded the Exploration in Reinforcement Learning workshop at one of these conferences.

Before his PhD, Ben spent a year doing robotics research at Google Brain, teaching simulated robots to do backflips and learning to avoid breaking themselves. Ben has also spent time in research labs at Adobe, IBM, MIT, Uber, and Xerox. Ben studied mathematics as an undergraduate at MIT, where he was elected to Phi Beta Kappa. Outside of classes, Ben contributed to augmented reality devices, drones for measuring water quality, and sensors for the rocket team. His undergraduate research on teaching computers to understand videos received the annual CSAIL award for "Outstanding Undergraduate Research Project in Artificial Intelligence."

Ben plans to stay in academia after he completes his PhD, building a research group to design algorithms for safe, intelligent robots. Opportunities to teach and mentor likewise compel Ben to remain in schools. Whether teaching probabilistic inference or coaching high school cross-country, Ben enjoys making opportunities to learn and grow accessible to everyone.

Growing up outside San Francisco instilled in Ben a love of the outdoors. If he's not at his desk, he's probably out running absurdly long distances in the mountains.