Ruth Urner is an Associate Professor at York University in Toronto, Canada. She is also a faculty affiliate at Toronto's Vector Institute. Previous to that she was a senior research scientist at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, and a postdoctoral fellow at Carnegie Mellon's Machine Learning department as well as at Georgia Tech. She received her PhD from the University of Waterloo for a thesis on statistical learning theory in 2013. She regularly serves as a senior program committee member of the major machine learning conferences, such as NeurIPS, ICML, AISTATS and COLT. Her research develops mathematical tools and frameworks for analyzing the possibilities and limitations of automated learning, with a focus on semi-supervised, active and transfer learning. Currently she is particularly interested in developing formal foundations for topics relating to societal impacts of machine learning, such as robustness to adversarial and strategic manipulations, human interpretability and algorithmic fairness.