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.


Amanda 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.


Amanda 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.


Amanda served as an area chair for the 2021 ICLR workshop on Responsible AI, and she reviewed for NeurIPS and ICML in 2020 and 2021. She also served as an Ethics Reviewer for NeurIPS in 2021. Amanda has served on the program committee of FAccT 2021, FAT* 2020, AIES 2020, AAAI-2020 Emerging Track on AI for Social Impact, and AI for Social Good at IJAI 2019. In 2019 and 2018, Amanda co-organized the ML4D workshop at NeurIPS, which brings machine learning researchers together with field practitioners to discuss ML in the context of the developing world. The workshop explored the risks and challenges of using ML4D. In 2019, Amanda co-organized the Fairness, Ethics, Accountability, and Transparency (FEAT) reading group at CMU.


Amanda graduated from Princeton in 2013 with a degree in computer science and a certificate in the Princeton School of Public Policy and International Affairs. For her undergraduate thesis, she analyzed how machine learning techniques can improve the diagnosis of pediatric tuberculosis in collaboration with Jocelyn Tang ('14) and under the guidance of Robert Schapire. In 2019 she earned her Master of Science in Machine Learning from CMU.