Amanda Coston is a PhD student in Machine Learning and Public Policy at Carnegie Mellon University. She earned a B.S.E magna cum laude from Princeton University, where she majored in computer science with a certificate in the Princeton School of Public Policy and International Affairs. After graduating she worked as a program manager at Microsoft. She later worked as a data scientist at Teneo and at the Nairobi-based start-up Hivisasa. Her research areas include human-centered predictive analytics, algorithmic fairness, and causal inference. She is advised by Alexandra Chouldechova and Edward H. Kennedy. In 2018 and 2019, she co-organized the Machine Learning for Developing World (ML4D) workshop at the Conference on Neural Information Processing Systems Conference (NeurIPS). She is an NSF GRFP fellow and a recipient of the Tata Consultancy Services (TCS) Presidential Fellowship. She was a Summer Graduate Student Fellow in RegLab’s 2020 Summer Institute.