Keynote: Emma Brunskill

Emma is an Associate Professor (tenured) in the Computer Science Department at Stanford University, where she leads a research group developing AI systems that learn from few samples and make robust decisions, motivated by applications in healthcare and education. Her lab is part of the Stanford AI Lab, the Stanford Statistical ML group, and AI Safety @Stanford, and she is an Associate Director of the Stanford Causal Science Center. Previously, she was an Assistant Professor at Carnegie Mellon University. Her work has been recognized with early-career awards from the National Science Foundation, the Office of Naval Research, and Microsoft Research, and has received multiple best paper nominations and awards across venues including UAI, CHI, EDM, LAK, RLDM, and ITS. She serves on the International Machine Learning Society Board and advisory and leadership committees including the Khan Academy Research Advisory Board and the Stanford Faculty Women’s Forum, and she was Co-Program Chair for ICML 2023.