Amanda Coston

Amanda Coston is an Assistant Professor in the Department of Statistics at UC Berkeley. Her work considers how -- and when -- machine learning and causal inference can improve decision-making in societally high-stakes settings.

Her research addresses real-world data problems that challenge the validity, equity, and reliability of algorithmic decision support systems and data-driven policy-making. A central focus of her research is identifying when algorithms, data used for policy-making, and human decisions disproportionately impact marginalized groups.

Amanda earned her PhD in Machine Learning and Public Policy at Carnegie Mellon University (CMU) where she was advised by the incredible duo Alexandra Chouldechova and Edward H. Kennedy. Amanda completed a postdoc at Microsoft Research in the Machine Learning and Statistics Team. Amanda is an Okawa Foundation grant recipient, a Schmidt Sciences AI 2050 Early Career Fellow, a Rising Star in EECS, Machine Learning and Data Science, Meta Research PhD Fellow, NSF GRFP Fellow, K & L Gates Presidential Fellow in Ethics and Computational Technologies, and a TCS Presidential Fellow. Her work has been recognized by best paper awards and featured in The Wall Street Journal and VentureBeat.