Sarah H. Cen
I am an Assistant Professor at Carnegie Mellon University in the Departments of Electrical & Computer Engineering and Engineering & Public Policy. I recently finished my postdoc at Stanford HAI, where I had an amazing time working with Percy Liang in Computer Science and Daniel E. Ho in the Law School's RegLab. Before that, I completed my PhD at MIT EECS under the wonderful mentorship of Aleksander Mądry and Devavrat Shah, my master's at Oxford with Paul Newman, and my undergraduate at Princeton with Naomi Leonard.
My research falls at the intersection of machine learning and AI accountability. On the technical side, I love learning new methods. I enjoy using tools and principles from a wide range of fields, including machine learning, statistics, game theory, causal inference, and information theory to tackle research questions. The goal of my research is to better design, understand, and govern AI and automated systems. As a consequence, I also have a strong interest in law, policy, ethics, and philosophy.
My research approaches research areas and problems—such as AI audits, interpretable machine learning, data ownership, and recommender systems—from multiple stakeholder perspectives. Recently, I have written on reducing information-resource gaps in AI accountability; the regulation and auditing of social media algorithms; the emergence of AI supply chains; the estimation of counterfactual potential outcomes under spillover effects; how competing for resources under uncertainty affects long-term outcomes; and individual-level rights of data-driven decision subjects. If you're interested in thinking about these interdisciplinary problems together, please reach out and tell me what you're thinking about!
Please find my (always, slightly out-of-date) C.V. here.