Assistant Professor, Computer Science
Emma Brunskill is an assistant professor in the Computer Science Department at Stanford University where she leads the AI for Human Impact (@ai4hi) group. Her work focuses on reinforcement learning in high stakes scenarios-- how can an agent learn from experience to make good decisions when experience is costly or risky, such as in educational software, healthcare decision making, robotics or people-facing applications. She was previously on faculty at Carnegie Mellon University. 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).
"Better Reinforcement Learning for Human in the Loop Systems"
There is increasing excitement about reinforcement learning-- a subarea of machine learning for enabling an agent to learn to make good decisions. Yet numerous questions and challenges remain for reinforcement learning to help support progress in important high stakes domains like education, consumer marketing and healthcare. I'll discuss some recent advances in these areas, and our work towards creating transparent, accountable reinforcement learning approaches that can interact beneficially with people.