Sarah is an Assistant Professor in the Computer Science Department at Cornell. She is interested in the interplay between optimization, machine learning, and dynamics, and her research focuses on understanding the fundamentals of data-driven control and decision-making. This work is grounded in and inspired by applications ranging from robotics to recommendation systems. Sarah’s work has received a best paper award at the International Conference on Machine learning, a best paper award at the NeuRIPS Joint Workshop on AI for Social Good, and a best paper finalist award at the Conference on Robot Learning. She holds a PhD and MS in EECS from UC Berkeley and a BS in ESE from UPenn. She is the recipient of a Berkeley Fellowship and a NSF Graduate Research Fellowship.

AI2050 Project

Large scale AI systems, from personalized recommendation to chat-bots, have been implicated in unintended negative consequences like addiction and radicalization. Studying such claims scientifically requires contending with messy and correlated data from the real world, as does developing algorithms for mitigating bad outcomes. Sarah’s AI2050 project will develop the foundations for this task by combining tools from control theory with the perspective of causal inference. She will build theoretically justified tools for anticipation, detection, and intervention in realistic deployment scenarios.