Organizers

Amanda is a joint PhD student in Machine Learning and Public Policy at Carnegie Mellon University. She received her BSE from Princeton (2013) where she studied computer science and public policy. After graduating, she worked as a program manager at Microsoft, a data scientist at Teneo, and a data consultant at Hivisasa, a journalism startup in Nairobi. She is broadly interested in how machine learning can solve problems of societal interest, and her research areas include algorithmic fairness, causal inference, and machine learning for healthcare. She is currently working with Allegheny County Department of Human Services on the opioid crisis. Her previous research considered the expansion of mobile money loans in East Africa, and she proposed fair algorithms for transfer learning in such contexts.

Maria is a joint PhD student in Machine Learning and Public Policy at Carnegie Mellon University. Prior to graduate school she received her B.Sc. in Mathematics from Universidad Nacional de Colombia (2013) and worked as a journalist for Semana magazine and Connectas. She was also a data science researcher for Quantil, where she was involved in several projects with the Colombian government. She is passionate about developing novel machine learning algorithms that are motivated by existing policy problems. Currently, her main focus is algorithmic fairness, studying how to measure and prevent bias and discrimination that may arise when when using machine learning for decision support. Her research on discovery of anomalous patterns of sexual violence in El Salvador was awarded the Innovation Award on Data Science at Data for Policy, 2016. She is the recipient of a Microsoft Research Dissertation Grant, 2018.

William is a joint PhD student in Machine Learning and Public Policy at Carnegie Mellon University. Prior to graduate school he received his BSE from Princeton (2012) and conducted cybersecurity research at MIT Lincoln Laboratory. He is the recipient of an NSF Graduate Research Fellowship as well as an ARCS graduate scholarship. William's research focuses on scalable and interpretable methods for spatio-temporal modeling of complex human behavior, such as urban crime and public health crises. He is particularly interested in interpretable models for Bayesian nonparametrics and neural networks. Using these techniques he collaborates extensively with city governments in New York City, Pittsburgh, and Boston improving the equity, efficiency, and reliability of city services.