Ben Eysenbach is a first-year PhD student at Carnegie Mellon University who teaches computers to make smart decisions that help humans.
Ben’s research uses tools from statistics and machine learning to make robots safer and enabling them to learn autonomously, with less human engineering effort.
Before his PhD, Ben spent a year conducting robotics research at Google Brain, teaching simulated robots to do backflips and learning how to avoid breaking themselves. Ben studied mathematics as an undergraduate at the Massachusetts Institute of Technology where he was elected to Phi Beta Kappa. As an undergraduate researcher, he taught computers to understand images and videos using machine learning and computer vision. For this work Ben received the annual award for Outstanding Undergraduate Research Project in Artificial Intelligence from the MIT Computer Science and Artificial Intelligence Laboratory. Outside of class, Ben contributed to augmented reality devices, drones for measuring water quality, and a sensor for the rocket team. Ben plans to stay in academia after he completes his PhD, designing the next generation of safe machine learning algorithms and teaching the next generation of scientists.
Growing up outside San Francisco instilled in Ben a love of the outdoors. If he’s not at his desk, he’s probably running in the mountains.