Jacob Steinhardt is an assistant professor of statistics at the University of California, Berkeley.
His main research goal is to make the conceptual advances necessary for machine learning systems to be reliable and aligned with human values. This includes topics such as robustness and security, reward specification and learning human values, and macroeconomic equilibria of ML systems. Recently he has also studied the science of deep learning. In addition to studying societal aspects of machine learning from a technical perspective, Jacob has collaborated with policy researchers on the use and misuse of machine learning, and is a technical advisor to the Open Philanthropy Project.