Amanda Coston

Amanda Coston is a Ph.D. student in Machine Learning and Public Policy at Carnegie Mellon University. Her research investigates how to make algorithmic decision-making systems more reliable and more equitable. Drawing on techniques from machine learning and causal inference, her work considers questions of validity, equity, and oversight in algorithmic decision systems. A central focus of her research is identifying when algorithms, data used for policy-making, and human decisions disproportionately impact marginalized groups. She is advised by Alexandra Chouldechova and Edward H. Kennedy.

During her Ph.D., Amanda interned at Facebook AI Applied Research (FAIAR), the Stanford Law School Regulation, Evaluation, and Governance Lab (RegLab), and IBM Research AI as a Science for Social Good Fellow. She completed a B.S.E from Princeton University where she was advised by Robert Schapire. Prior to her Ph.D.,  she worked at Microsoft, the consultancy Teneo, and the Nairobi-based startup HiviSasa.