Amanda Coston is a PhD student in Machine Learning and Public Policy at Carnegie Mellon University (CMU). Her research considers fairness in algorithmic decision support systems, and she is particularly interested in how methods from causal inference can improve these systems. She is advised by Alexandra Chouldechova and Edward H. Kennedy.
TeachingAmanda particularly enjoys teaching and mentorship opportunties. She served as a teaching assistant for Matt Gormley and Tom Mitchell's Introduction to Machine Learning in 2021. She served as a project lead of the AI4ALL summer program at CMU, where she introduced high school students to algorithmic fairness in the criminal justice system using the COMPAS dataset (see Github project). She also participated in the AI undergradate mentoring program at CMU.
NotableAmanda is an NSF GRFP fellow and a K & L Gates Presidential Fellow in Ethics and Computational Technologies. In 2019 she was a recipient of the Tata Consultancy Services (TCS) Presidential Fellowship.
Her research on counterfactual risk assessments and evalution for child welfare screening won the 2018 Suresh Konda Best First Student Research Paper Award from the Heinz College.