Amanda Coston - Addressing validity in decision-making algorithms

Friday 10 May 2024, noon (EDT)
Toronto Data Workshop
Amanda Coston, Microsoft Research and Berkeley
Amanda Coston is a Postdoc at Microsoft Research in the Machine Learning and Statistics Team. In fall 2024 she will join the Department of Statistics at UC Berkeley as an Assistant Professor. 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 Alexandra Chouldechova and Edward H. Kennedy. Amanda is 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 Tata Consultancy Services Presidential Fellow. Her work has been recognized by best paper awards and featured in The Wall Street Journal and VentureBeat.