My work focuses on the foundations and applications of AI to create computers that learn to help people. Reinforcement learning is a particularly appealing framework for this setting since it considers how an agent can learn to make decisions to maximize cumulative outcomes. Yet despite many exciting advances in reinforcement learning for robotics and other simulated domains, many such approaches are less suitable to support progress in important high stakes people-facing domains like education, consumer marketing and healthcare. I will describe some of our work in tackling these issues which has lead to new theoretical contributions and educational systems that enhance student learning. I will also discuss our ongoing work towards joint human-machine systems that are together, far more than the some of their parts.
Emma Brunskill is an assistant professor in the Computer Science Department at Stanford University where she leads the AI for Human Impact group. She is the recipient of a multiple early faculty career awards (National Science Foundation, Office of Naval Research, Microsoft Research) and her group has received several best research paper nominations (CHI, EDMx2) and awards (UAI, RLDM).