Research Expertise and Interest

causal inference, machine learning, nonparametric statistics, responsible AI, algorithmic fairness

Research Description

Amanda Coston is an assistant professor of statistics at UC Berkeley. Her research addresses real-world data problems that challenge the validity, reliability, and equity of algorithmic decision support systems and data-driven policy-making. Her work draws on techniques from causal inference, machine learning, and nonparametric statistics. 

She earned her PhD in machine learning and public policy at Carnegie Mellon University and subsequently completed a postdoc at Microsoft Research on the Machine Learning and Statistics Team. She also holds a Bachelor of Science in Engineering from Princeton in computer science and a certificate in the Princeton School of Public and International Affairs.